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Author: Audrey Zhang (NWIW Internship 2019) Abstract Numerous studies from a health or environmental standpoint have shown how the population can make changes to improve each field of studies’ respective issues. Combining results from several research studies, this paper shows how shifting one’s diet to include fewer animal products positively impacts both health and the environment. This dietary change not only decreases one’s carbon footprints, but also minimizes risks for some of the leading causes of death, in particular cholesterol-related diseases. Establishments can do their part by simply adding healthy Vending machines in Newcastle. Introduction In recent years, health and environmental awareness have risen to become a national, or even global, concern. In the U.S., staggering statistics of diet-related conditions confirm that change needs to happen at the individual level. Alongside health awareness, the ramifications of the Greenhouse Effect are also pushing the nation to be more environmentally conscious. Despite seemingly having very different end goals in mind, both environmentalists and nutritionists can join forces and promote an option that benefits everyone. By reducing the intake of animal foods, risks of life-threatening diseases and carbon footprints can be decreased. This will not only help improve health conditions but also lower carbon emissions on a national scale. Cholesterol and Its Effects on Health Cholesterol is an organic molecule created by the body or ingested from animal foods. Around 20% of dietary cholesterol is consumed, while the other 80% is made in the body. This waxy substance is synthesized in the liver and requires acetyl-CoA, an essential molecule for regulating fatty acid synthesis. The acetyl-CoA is passed through a variety of complex reactions that ultimately produce cholesterol. A good way to maintain a healthy diet is preparing juices with the new  vitamix blender.Although often shunned as an unhealthy molecule, an adequate amount of cholesterol is essential to building cell membranes, making hormones, maintaining metabolism, and producing vitamins and bile acids. However, once blood cholesterol levels rise above 200mg/dL (200 milligrams of cholesterol for every deciliter of blood), the patient increases their risk for high blood cholesterol (diagnosed as hypercholesterolemia).[2] A rise in the body’s cholesterol level can be linked to either hereditary diseases or faulty dietary habits. It is estimated that almost 1 in 3 American adults have high cholesterol, but only 1 in 300 cases of high cholesterol is familial hypercholesterolemia, ruling out hereditary diseases as the primary issue[3]. As a result, faulty dietary habits account for the majority of cases. These habits are characterised by eating an excessive amount of animal foods, frequently exceeding the dietary recommendation of 300 milligrams per day. Excessive digestion of cholesterol results in high blood cholesterol levels, a source various potentially fatal diseases[2]. LDL (low-density lipoproteins) and HDL (high-density lipoproteins) are the two categories of cholesterol in the body. Dubbed as the “bad” cholesterol, high LDL levels result in a buildup of cholesterol on artery walls, blocking or narrowing certain vessels. Blockage or narrowing of vessels hardens the arteries, resulting in a medical condition called atherosclerosis. This puts the body at danger for life-threatening diseases such as coronary heart disease, strokes, Type 2 diabetes, and high blood pressure; some of America’s leading causes of death. On the other hand, HDL, the “good” cholesterol, lowers blood cholesterol levels by absorbing cholesterol in the bloodstream and carrying it back to the liver.[2] Figure 1: LDL and HDL Cholesterol in Arteries[4] Figure 2: Atherosclerosis in Arteries[5] Figure 3: Death Rates for the 10 Leading Causes of Death in the United States (2016,2017)[6] Risks for Cholesterol-Related Diseases Based on Diets The impact of ingested cholesterol from animal foods is most notable when comparing the health of those with different diets. These dietary habits range from meat-lovers to vegans and are categorised by the amount of food consumed from each food group. As shown in Figure 4, the amount of energy animals foods account for in a diet decreases from around 35% in a meat lover to 0% in a vegan. Between the two extreme diets (average to vegetarian), animal foods account for 10-25% of the energy expended. Furthermore, as dietary cholesterol ingested decreases with the number of animal foods consumed, eating fewer animal foods will therefore lower cholesterol levels in the body. For example, a vegan diet, which consists of no dietary cholesterol, has no energy expended on animal foods and is also shown to reduce cholesterol levels by 10-30% in comparison to an average diet.[7] Figure 4: Food Energy Distribution in Different Diets[7] As cholesterol levels drop in a less animal-food-intensive diet, risks for cholesterol-related diseases decrease concurrently as well[8,9]. A 2015 study released by the Atherosclerosis Risk in Communities (ARIC) followed the health of 11,000 adult males for a median of 22.7 years[8]. They found that the men who were the highest consumers of processed meat (i.e. jerky, bacon, sausage) had a 24% increased chance of stroke, a cholesterol-related disease. Similarly, the highest consumers of red meat (beef, lamb, pork) had a 41% increased chance of stroke. In total, the high consumers of both red and processed meat had a 62% higher chance of stroke than the average male. Since this group often consumes large volumes of food extremely high in dietary cholesterol, their bodies are more at risk for buildup and blockage of blood vessels. Therefore, it comes as no surprise that they are much more susceptible to strokes. On the other hand, an analysis in 2017 by the Icahn School of Medicine showed that a plant-based diet can decrease the risk of heart disease, another cholesterol-related condition[9], by 42%. Furthermore, those who maintained a healthy plant-based diet were 25% less likely to develop heart disease in the next twenty years compared to those who did have a healthy plant-based diet. Figure 5: Probability for Heart Disease of Stroke Based on Diet[8,9] Reducing Emissions with a Low-Cholesterol Diet Research shows that eating fewer animal foods also reduces carbon footprints, in addition to reducing the risk of cholesterol-related diseases. Foods containing dietary cholesterol are often the most carbon-intensive in comparison to plant-based foods due to the inefficient transformation of energy.[7] A 2018 study found that food production accounts for 26% of global GHG (greenhouse gas) emissions, and animal foods accounting for a staggering 31% of that number[10]. Figure 6: Carbon Emissions of Different Types of Food[11] Therefore, diets consisting of high proportions of animal foods, in particular, beef, inevitably have a high carbon footprint. On the contrary, switching out less carbon-intensive foods can greatly reduce carbon footprints. This requires no dramatic in daily life, as many alternatives, which provide similar nutrition for both less cholesterol and carbon emissions, are available. For example, in the meat lover’s diet in Figure 7, beef accounts for 1.5 of the 3.3 t CO2e. By simply cutting out the beef, one can reduce their carbon emissions by 1.5 tonnes[7]. In addition, these changes will have little impact on their protein intake. For example, in 100 grams of steak, there are 78 mg of cholesterol and 25 grams of protein. Yet, the same quantity of salmon has a similar 20 grams of protein, but only 55 mg of cholesterol.[12] Although both salmon and steak are great sources of protein, one is considerably more harmful to the environment than the other. Therefore, conscious decisions to eat fewer animal foods can both lower intake of dietary cholesterol and reduce carbon footprints, improving both the body’s health and the environment. Figure 7: Carbon Footprints (t CO2e/person) Based on Different Diets[8] Figure 8: Relation Between Risk for Disease and Food Carbon Emissions[7,8,9] Conclusion Blood cholesterol levels are essential in reducing risks for life-threatening diseases such as strokes, heart diseases, high blood pressure, and Type 2 diabetes. As cholesterol is most often consumed, the number of animal foods, which are carbon-intensive to produce, in a diet plays a large role in determining one’s blood cholesterol levels. Managing these levels is essential in controlling risks for life-threatening diseases such as strokes, heart diseases, high blood pressure, and Type 2 diabetes. Therefore, decreasing the consumption of animal foods, which contain cholesterol, can simultaneously minimize carbon footprints and risk for cholesterol-related disease, combating primary issues of both health and the environment. References “Cholesterol Metabolism,” Cholesterol metabolism (University of Waterloo, November 2015), http://watcut.uwaterloo.ca/webnotes/Metabolism/Cholesterol.html#:~:text=The%20liver%20synthesizes%20cholesterol%20from,6. “Cholesterol Levels: What You Need to Know.” MedlinePlus. U.S. National Library of Medicine, December 4, 2017. http://www.medlineplus.gov/cholesterollevelswhatyouneedtoknow.html. Collins, Sonya. “Inherited High Cholesterol: Genetic Conditions, Family History, and Unhealthy Habits.” WebMD. WebMD, March 22, 2016. http://www.webmd.com/cholesterol-management/features/high-cholesterol-genetics. “Cholesterol Your Ultimate Guide,” COBRACOR COACHING, October 13, 2019, https://cobracor.weebly.com/blog/cholesterol-your-ultimate-guide. “Coronary Artery Disease or Atherosclerosis,” Coronary Artery Disease Atherosclerosis – Cardiology – Highland Hospital – University of Rochester Medical Center (UR Medicine Cardiology , May 2019), https://www.urmc.rochester.edu/highland/departments-centers/cardiology/conditions/coronary-artery-disease.aspx. Murphy, Sherry L., Jiaquan Xu, Kenneth D. Kochanek, and Elizabeth Arias. “Products – Data Briefs – Number 328 – November 2018.” Centers for Disease Control and Prevention. Centers for Disease Control and Prevention, November 29, 2018. http://www.cdc.gov/nchs/products/databriefs/db328.htm. Wilson, Lindsay. “Shrinkthatfootprint.com,” shrinkthatfootprint.com (Shrink That Footprint, March 2013), http://www.shrinkthatfootprint.com/food-carbon-footprint-diet. Rapaport, Lisa. “Red Meat Linked to Increased Stroke Risk.” Reuters. Thomson Reuters, November 25, 2015. http://www.reuters.com/article/us-health-meat-stroke-risk/red-meat-linked-to-increased-stroke-risk-idUSKBN0TE2IA20151125. “Vegan & Plant-Based Diets and Heart Disease.” Cleveland HeartLab, Inc. Cleveland HeartLab, December 28, 2017. http://www.clevelandheartlab.com/blog/vegan-plant-based-diets-heart-disease/. Ritchie, Hannah. “Food Production Is Responsible for One-Quarter of the World\’s Greenhouse Gas Emissions.” Our World in Data. Our World in Data, November 6, 2019. https://ourworldindata.org/food-ghg-emissions. “Carbon Footprint Factsheet,” Carbon Footprint Factsheet | Center for Sustainable Systems (University of Michigan, July 2019), http://css.umich.edu/factsheets/carbon-footprint-factsheet. “Saturated Fat.” www.heart.org. American Heart Association, June 1, 2015. http://www.heart.org/en/healthy-living/healthy-eating/eat-smart/fats/saturated-fats. “Causes of Heart Failure.” www.heart.org. American Heart Association, May 31, 2017. http://www.heart.org/en/health-topics/heart-failure/causes-and-risks-for-heart-failure/causes-of-heart-failure. “Cholesterol.” Better Health Channel. Department of Health & Human Services, February 28, 2014. http://www.betterhealth.vic.gov.au/health/conditionsandtreatments/cholesterol. Ede, Georgia. “The Vegan Brain.” Psychology Today. Psychology Today, September 30, 2017. http://www.diagnosisdiet.com/diet/vegan-diets/. Haring, Bernhard, Jeffrey R. Misialek, Casey M. Rebholz, Natalia Petruski-Ivleva, Rebecca F. Gottesman, Thomas H. Mosley, and Alvaro Alonso. “Association of Dietary Protein Consumption With Incident Silent Cerebral Infarcts and Stroke.” Stroke 46, no. 12 (December 2015): 3443–50. https://doi.org/10.1161/strokeaha.115.010693. “High Cholesterol Facts.” Centers for Disease Control and Prevention. Centers for Disease Control and Prevention, February 6, 2019. http://www.cdc.gov/cholesterol/facts.htm. “High Cholesterol.” Mayo Clinic. Mayo Foundation for Medical Education and Research, July 13, 2019. http://www.mayoclinic.org/diseases-conditions/high-blood-cholesterol/symptoms-causes/syc-20350800. Morgan, Kate. “Story from Blue Cross Blue Shield Association: These Are the Top 10 Health Conditions Affecting Americans.” USA Today. Gannett Satellite Information Network, November 6, 2018. http://www.usatoday.com/story/sponsor-story/blue-cross-blue-shield-association/2018/10/24/these-top-10-health-conditions-affecting-americans/1674894002/. “Preventing High Cholesterol.” Centers for Disease Control and Prevention. Centers for Disease Control and Prevention, October 31, 2017. http://www.cdc.gov/cholesterol/prevention.htm. “September Is National Cholesterol Education Month.” Centers for Disease Control and Prevention. Centers for Disease Control and Prevention, November 25, 2013. http://www.cdc.gov/cholesterol/cholesterol_education_month.htm. About the Author Audrey is a junior in high school from California interested in Environmental Studies and Cancer Research. She plays both Varsity squash and tennis for her high school and loves reading and drawing in her free time.

Author: Eesha Bethi Abstract Crohn’s Disease is a disease that affects the immune system in which a person\’s T cells will attack their GI tract and cause excess inflammation. Treatments for Crohn’s disease have advanced very little in the last decade, even though 3 million Americans are affected by it [1]. The main way Crohn’s is treated is through the use of prescribed thiopurine drugs. These drugs effectively treat many autoimmune disorders by suppressing the immune system, more specifically their capability to inflame certain areas of tissue. In this study, publicly available databases were used to determine what factors may affect the efficacy of thiopurine drugs in certain individuals with Crohn’s disease. First, thiopurine metabolization and how the compounds carried out their function as immunosuppressants were examined, revealing how external factors change the effectiveness of the drug. Then, the role of 6-MP and other thiopurine derivatives in the bile and caffeine pathways were studied, as well as enzymatic disorders that could prevent the drug from being metabolized. Thiopurine enzymes that were likely to have deficiencies resulting in large repercussions were TMPT, XO, IMPD, ITPA, and HPRT. These are important to know to determine someone’s responsiveness to the drug, drug dosage, and if the patient would need any other supplements to keep the pathway functioning. This study concluded that excessive use of compounds or enzymes in the caffeine or bile pathways would detract from the amount available for thiopurine metabolism. Moreover, the outcomes also showed that an enzyme deficiency will likely result in decreased levels of necessary compounds, like 6-MP, that are needed to fully metabolize the drug. Genetic screening is a beneficial solution to prevent enzyme deficiencies from reducing the effectiveness of the thiopurine treatment. This could be used to properly determine the dosage of thiopurines given to a patient so that the treatment is effective while also accounting for the amount of compounds or enzymes available for the body as a whole, including other pathways. Introduction Crohn’s disease is an inflammatory bowel disease (IBD) that causes chronic inflammation in the gastrointestinal (GI) tract. This disease can affect any part of the digestive tract from the mouth to the anus but inflammation is most common at the end of the small intestine (ileum) and beginning of the large intestine. The inflammation can permeate through the thickness of the entire wall. It can also appear in patches through the entire GI tract. It is most often diagnosed in adults between the age of 20 to 30, however, the disease can develop at any age [2]. The direct cause of Crohn’s is still unknown but most studies suggest genetic and environmental factors, like diet or stress, as possible factors. Harmless bacteria in the GI tract are mistaken by the immune system as pathogens and provokes an immune response which causes inflammation [1]. Common symptoms include persistent diarrhea, rectal bleeding, abdominal pain, loss of appetite, weight loss, fatigue, etc [3]. There are many gaps in Crohn’s research currently. There are 5 focus areas that remain under-researched, namely preclinical human IBD mechanisms, environmental triggers, novel technologies, precision medicine, and pragmatic clinical research [4]. Preclinical human IBD mechanisms include research of biochemical pathways, using humanized disease models, to yield novel and effective therapeutic interventions. The specific research gaps include triggers of immune responses, intestinal epithelial homeostasis and wound repair, age-specific pathophysiology, disease complications, heterogeneous response to treatments, and determination of disease location [5]. Precision medicine works to tailor treatments based on specific clinical and biological characteristics of individual patients to deliver optimal care. The main research gaps include understanding and predicting the natural history of IBD (disease susceptibility, activity, and behavior), predicting disease course and treatment response, and optimizing current and developing new molecular technologies [6]. Lastly, the main research gaps within pragmatic clinical research include understanding the epidemiology of IBD, accurate medication selection to increase treatment effectiveness, defining how clinicians are utilizing therapeutic drug monitoring, study of pain management, and understanding the health economics and healthcare resources utilization [7]. All of these questions still need to be answered, just proving how much more research needs to be done to effectively treat Crohn’s disease. Currently, Crohn’s disease is commonly treated with azathioprine or mercaptopurine, both of which are thiopurines. Thiopurines are immunosuppressive drugs that deactivate the area of T cells which cause inflammation. Once thiopurines enter the body they have to be metabolized to different compounds by different enzymes to create thioguanine [8]. Thioguanine will get incorporated into the DNA of the T cells during DNA replication in place of a guanine nucleotide. This changes the information that the cell receives and gets rid of the inflammation response. Objective The current issues regarding Crohn’s research is simply that it\’s not getting prioritized and that leaves significant gaps in how patients get treated. The objective is to identify how thiopurines aid in the treatment of Crohn’s Disease. Then, this paper will explain why some people with Crohn\’s disease respond better or worse to the common thiopurine treatment and how to remedy these issues. This will hopefully go on to help millions of people get access to proper treatments and dosages and incite further research to look for new treatments for Crohn’s. Methods/Results AZA/6-MP Pathway The KEGG (Kyoto Encyclopedia for Genes and Genomes) Database was used to identify the intermediates that the drugs azathioprine and mercaptopurine metabolize into before getting incorporated into the T cell DNA [9]. It showed that both compounds existed in the same pathway, “Drug Metabolism- Other Enzymes Reference Pathway” by searching each drug’s pathways individually at first. Figure 1 lists out all the compounds azathioprine is metabolized into and the enzymes that catalyze each reaction. By knowing this, it is easier to see how compounds in the pathway are incorporated in other processes. Figure 1: Metabolic pathway of AZA. Taken from the KEGG database, and annotated with additional information. The red indicates an enzyme and the black indicates a compound. [10] Azathioprine (AZA) is turned into 6-Mercaptopurine (6-MP), with the help of glutathione or other endogenous sulfhydryl-containing proteins. This reaction produces 1-Methyl- 4-nitro-imidazole. 6-MP is then further metabolized by three enzymes, thiopurine S-methyltransferase (TPMT), hypoxanthine phosphoribosyltransferase (HPRT), and xanthine oxidase (XO). TPMT adds a methyl group to 6-MP to create 6-methyl-mercaptopurine (6-MMP); XO transfers 6-MP to 6-thiouric acid (6-TUA) and HPRT metabolizes 6-MP into 6-thioinosine monophosphate (6-TIMP). The monophosphate kinase (MPK) enzyme adds a phosphate group to the 6-TIMP to create 6-thioinosine-diphosphate (6-TIDP). Then diphosphate kinase (DPK) transfers it into 6-thioinosine-triphosphate (6-TITP). The inosine triphosphate pyrophosphatase (ITPA) enzyme transfers some of the 6-TITP back into 6-TIMP. Meanwhile, TMPT adds a methyl group to 6-TIMP to create 6-methyl-thioinosine monophosphate (6-MTIMP). Then the MPK and DPK enzymes transfer 6-MTIMP into 6-methyl thioinosine diphosphate (6-MTIDP) and 6-methyl thioinosine triphosphate (6-MTITP). 6-MMP is transferred to 6-MTIMP by HPRT as well. 6-TIMP is also metabolized by inosine monophosphate dehydrogenase (IMPD) to 6-thioxanthosine monophosphate (6-TXMP). 6-TXMP is then transferred to 6-thioguanine nucleotides: 6-TGMP, 6-TGDP, 6-TGTP. Guanosine monophosphate synthetase (GMPS) turns 6-TXMP into 6-thioguanine monophosphate (6-TGMP). 6-TGMP is also metabolized into 6-thioguanine diphosphate (6-TGDP) and 6-thioguanine triphosphate (6-TGTP) by MPK and DPK. Additionally, TPMT adds a methyl group to 6-TGMP to create 6-methyl-thioguanine monophosphate (6-MTGMP) as a byproduct. HPRT converts 6-thioguanine (6-TG) into 6-thioguanine nucleotides while TPMT turns 6-TG into 6-methyl thioguanine (6-MTG) and XO converts 6-TG to 6-thiouric acid (6-TUA). [11] Figure 2: The process by which 6-TGN, an eventual derivative of thiopurine, causes apoptosis in T-cells. Figure 3: The process by which the 6-TGTP nucleotide, created from thiopurine metabolism, binds to Rac1 and blocks anti-apoptotic protein and prevents inflammation. The thioguanine nucleotides (6-TGN) created from the drug are incorporated in to the T cell DNA during DNA replication in place of a regular guanine nucleotide. This enacts the mismatch repair system (MMR) to correct the errors in the DNA. However, in this case, the MMR system will work incompletely and results in apoptosis of the T cell. (Figure 2) When normally functioning, guanine triphosphate binds to the Rac1 gene and the Vav1 protein and the guanine nucleotide exchange factor catalyzes the transformation of Rac1 between GTP (guanine triphosphate) and GDP (guanine diphosphate), in which GTP is active and GDP is inactive. However, when thiopurine drugs are input into the body, thioguanine triphosphate (6-TGTP), one of the three thioguanine nucleotides, binds to Rac1 in place of GTP. GAP proteins then convert TGTP-bound Rac1 to 6-TGDP-bound Rac1 and Vav1 becomes unable to catalyze the exchange between the two, resulting in the build-up of inactive 6-TGDP-bound Rac1 protein. This decreases inflammation in two ways. First is by apoptosis. Normally, the Vav1 catalyzed activation of the Rac1 gene results in an increased expression of the anti-apoptotic protein Bcl-x, however, the build-up of 6-TGDP-bound Rac1 protein blocks Rac1and prevents Bcl-x formation. Without an anti-apoptotic protein, apoptosis will occur. Second is preventing complex formation between T cells and antigen-presenting cells (APC). Activated Rac1 removes a phosphate group from ezrin-radixin moesin (ERM) proteins in T cells, which leaves room for APC conjugation with a T cell. This process gets reversed when thiopurines prevent activation. If T cells can’t bind to APCs, they can’t enact an immune response, an inflammatory response, to foreign substances because APCs allow T cells to recognize foregin substances. (Figure 3) [12] Related Compounds/Pathways After doing a literature search for the thiopurine mechanism of action of Crohn’s disease, the KEGG database was used to identify what other pathways thiopurine intermediates are involved with, under the hypothesis that related pathways may affect drug efficacy or symptoms. 6-MP is also involved in the bile secretion pathway, which was discovered by looking through the substances involved in the highlighted region. (Figure 4) Figure 4: 6-MP involvement in bile secretion pathway, indicated by compounds/acids highlighted in red. Taken from KEGG Database. [13] In Figure 4, the red circles marked on the right indicate the location of 6-MP in the pathway. After 6-MP is produced in the thiopurine pathway, some enters the liver to aid in the creation of oleic acid (OA). OA is an antineoplastic, which means that it prevents the abnormal growth of cells to prevent tumors in the liver. This gives the OA some anticancer properties to help protect the liver. However, all antineoplastics have some level of hepatotoxicity, which means that oleic acid is somewhat toxic to the liver. This is why OA must be secreted through urinary output soon after it’s made. If everything functions correctly, the more 6-MP produced, the more oleic acid produced, which could either be excreted or become toxic to the liver. [14] Looking at the properties of the xanthine oxidase (XO) enzyme in the KEGG Database, it was discovered that XO is also used in the caffeine metabolism pathway (Figure 5). Highlighted in red in Figure 5, XO’s role is to create methyluric acid and dimethyluric acid as more caffeine enters the body. Methyluric acid is a major metabolite of caffeine with antioxidant activity that protects cells from being damaged by unstable atoms [15]. Dimethyluric acid has a role as a human xenobiotic metabolite [16], which means it transforms less polar foreign substances into more polar compounds that can be excreted more easily [17]. Figure 5: Xanthine oxidase (XO) involvement in caffeine pathway, indicated by enzymes highlighted in red. Taken from KEGG Database. [18] Genes/Proteins + Mutations In order to identify the diseases or deficiencies that could affect the enzymes involved in the thiopurine pathway, the KEGG database was searched for the properties of each involved in the thiopurine metabolism pathways. The possible diseases and genetic variations that may affect each enzyme were then analyzed.Various additional databases such as Gene Cards were also used to validate these properties, and to understand the possible genetic variations. Each enzyme also has certain mutations that would affect its function in the thiopurine pathway. Discussion Significance of Related Pathways The possible relationship between thiopurine metabolism and the pathways in which thiopurine intermediates existed were examined, primarily the bile secretion pathway and caffeine pathway. The bile secretion pathway would be affected by a change in 6-MP levels. An increase in 6-MP levels may cause hypertoxicity in the liver because of an increase in antineoplastics. This may cause the liver to fail or a plethora of other hepatic diseases. This increase in 6-MP levels is likely if the patient has TMPT deficiency or Xanthinuria, which is why making sure the Crohn’s patient gets the right dosage is crucial. It is confirmed that a TPMT deficiency can cause an increase in toxicity when treated with thiopurines [34]. If 6-MP levels decrease, there may be an increase likelihood of developing a hepatic tumor, however, this is unlikely because there are a lot of other antineoplastics that are also in the the liver, which means they could serve in place of 6-MP in the instance that the levels decrease. This is why there is always a delicate balance of how much of the drug to dose a patient. As seen in the caffeine diagram above, the XO enzyme is used to make different acids such as methyluric acid and dime thyluric acid. One conclusion drawn from this diagram is that the more caffeine that enters the body, the more XO needed to create these enzymes and keep the pathway functioning. Therefore, in the instance that the body did not respond by overexpressing the enzyme, an increased caffeine consumption in a patient with Crohn’s Disease would reduce the effectiveness of the medication because less XO would be available to perform its function in the thiopurine pathway. This may lead to a build up in 6-MP which would either create build ups of different compounds or, if the enzymes were upregulated, would create more thioguanine nucleotides, which would speed up the process for dismantling the T cells. Certain genetic variants might also have an effect on the caffeine and thiopurine pathways, especially if the patient has Crohn’s. For example, if a patient has xanthinuria, they may want to reduce caffeine consumption because they would already have a deficiency and they soul conserve the XO enzyme to work in the thiopurine pathway. It has been proven that a complete XO deficiency can cause severe toxicity with a full dose of AZA [35]. Another example would be if the patient has TMPT deficiency, they would already be producing an oversupply of 6-MP and would need more of the XO enzyme, which is another reason to reduce caffeine consumption. Genetic Screening Because of the many common mutations that may affect thiopurine metabolism, screening for the mutations listed above could dramatically improve dosing of azathioprine/mercaptopurine for Crohn’s disease patients. The aforementioned deficiencies could either increase or decrease the production of thioguanine, which means either more or less of the drug needs to be administered to maximize the effectiveness of the drug. Some candidate screening technologies include microarrays, polymerase chain reactions (PCRs), and DNA sequencing. Microarrays look at all of the chromosomes at once. They contain thousands of short, single stranded DNA that is attached to a chip. The human DNA is then compared to the normal microarray to determine any duplications, deletions, etc. PCRs make copies of numerous short DNA sections from a small sample of genetic material that can later be analyzed and sequenced to determine variants. DNA sequencing will determine the order of the base pairs that make up DNA. This allows scientists to look for a specific variant or mutation to find a disorder. [36] To improve treatment of Crohn’s disease, these tests should always be used to detect the certain mutations/variants listed above to let a physician know if there is an enzyme deficiency before administering a drug so as to not do more harm to the patient. For example, if the patient had an IMPD, ITPA, or HPRT deficiency that meant a lot of the drug would not get metabolized into thioguanine, the physician would either need to change the drug dosage or if possibly supplement the enzymes to make the pathway efficient. In fact, multiple groups with an ITPA variation correlates with increased toxicity to thiopurines [28]. Screening gives the doctor or person prescribing the dosage of thiopurine drugs a much clearer picture of how much to give the patient to ensure that there aren\’t any ramifications, within the thiopurine pathway or another pathway it connects to, that they would have missed otherwise. Future research could go on to find different, more accurate screening methods to ensure the safety of patients. There is also a need for more research into how compound or enzyme levels are affected by other pathways and mutations. A lot has been hypothesized but more quantifiable evidence could be found. For example, a certain percentage of enzyme deficiency would lead to a certain percentage of 6-MP increase. This would create a much more streamlined system of dosing patients with Crohn’s. Conclusion Through literature searches and using a collection of databases, several possible mechanisms that may cause Crohn’s disease patients to have different responses to thiopurines have been identified. The bile secretion pathway and the caffeine metabolism pathway have a complex relationship with thiopurine metabolism that potentially affect the biochemical rates of reactions in the thiopurine metabolism pathway. Additionally, many common genetic variants play a potential role in thiopurine metabolism and response. Small molecules could be used to better regulate the bile secretion and caffeine metabolism pathways in the presence of thiopurine, and genetic screening to improve dosing for patients with genetic variants. Of course, both of these areas will need to be researched deeply before such therapies could be administered to a patient, but this research can help start the conversation about how to improve personalized medicine for Crohn’s disease patients. References Crohn\’s & Colitis Foundation. n.d. “Causes of Crohn’s Disease.” Crohn\’s & Colitis Foundation. www.crohnscolitisfoundation.org/what-is-crohns-disease/causes. 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Crohn\’s & Colitis Foundation. n.d. “Pragmatic Clinical Research.” Crohn\’s & Colitis Foundation. www.crohnscolitisfoundation.org/research/challenges-ibd/pragmatic-clinical-research. Neurath, Markus. 2010. “Thiopurines in IBD: What Is Their Mechanism of Action?” National Center for Biotechnology Information. www.ncbi.nlm.nih.gov/pmc/articles/PMC2933759/. Kanehisa, Minoru, and Susumu Goto. \”KEGG: kyoto encyclopedia of genes and genomes.\” Nucleic acids research 28, no. 1 (2000): 27-30. Kanehisa Laboratories. 2019. “Drug Metabolism – Other Enzymes – Reference Pathway.” Kyoto Encyclopedia for Genes and Genomes. https://www.kegg.jp/kegg-bin/highlight_pathway?scale=1.0&map=map00983&keyword=thiopurine. Derijks, L. J. J., L. P. L. Gilissen, P. M. Hooymans, and D. W. Hommes. 2006. “Review Article: Thiopurines in Inflammatory Bowel Disease.” Alimentary Pharmacology & Therapeutics, (05), 717-718. dpl6hyzg28thp.cloudfront.net/media/thiopurines_pharmacokinetics.pdf. de Boer, Nanne K., Laurent Peyrin-Biroulet, Bindia Jharap, Jeremy D. Sanderson, Berrie Meijer, Imke Atreya, Murray L. Barclay, et al. 2017. “Thiopurines in Inflammatory Bowel Disease: New Findings and Perspectives.” Journal of Crohn\’s and Colitis, (12), 611-612. dpl6hyzg28thp.cloudfront.net/media/thiopurines_cell_signalling.pdf. Kanehisa Laboratories. 2020. “Bile Secretion – Reference Pathway.” Kyoto Encyclopedia for Genes and Genomes. https://www.kegg.jp/kegg-bin/show_pathway?map04976+D04931. National Institute of Diabetes and Digestive and Kidney Diseases. 2019. “Antineoplastic Agents.” In LiverTox: Clinical and Research Information on Drug-Induced Liver Injury [Internet]. 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Klein. 2010. “Very important pharmacogene summary: thiopurine S-methyltransferase.” National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3086840/. Genecards Human Gene Database. n.d. “TPMT Gene (Protein Coding).” GeneCards. https://www.genecards.org/cgi-bin/carddisp.pl?gene=TPMT. “DISEASE: Thiopurine S-Methyltransferase Deficiency (TPMT Deficiency).” n.d. Kyoto Encyclopedia for Genes and Genomes. www.kegg.jp/dbget-bin/www_bget?ds%3AH00964. Peretz, Hava, Michael Korostishevsky, David M. Steinberg, Mustafa Kabha, Sali Usher, Irit Krause, Hannah Shalev, Daniel Landau, and David Levartovsky. 2019. “An Ancestral Variant Causing Type I Xanthinuria in Turkmen and Arab Families Is Predicted to Prevail in the Afro-Asian Stone-Forming Belt.” National Center for Biotechnology Information. www.ncbi.nlm.nih.gov/pmc/articles/PMC7012738/. 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Burgis, Nicholas E. 2016. “A disease spectrum for ITPA variation: advances in biochemical and clinical research.” Journal of Biomedical Science. https://jbiomedsci.biomedcentral.com/articles/10.1186/s12929-016-0291-y#:~:text=ITPA%20mutation%20causes%20infantile%20encephalopathy&text=recently%20identified%20recessive%20ITPA%20mutation,to%20a%20unique%20MRI%20pattern. “DISEASE: Early Infantile Epileptic Encephalopathy.” n.d. Kyoto Encyclopedia for Genes and Genomes. www.kegg.jp/dbget-bin/www_bget?ds%3AH00606. Weizmann Institute of Science. n.d. “IMPDH1 Gene.” GeneCards. https://www.genecards.org/cgi-bin/carddisp.pl?gene=IMPDH1. Sullivan, Lori S., Sara J. Bowne, David G. Birch, Dianna Hughbanks-Wheaton, John R. Heckenlively, Richard A. Lewis, Charles A. Garcia, et al. 2006. “Prevalence of Disease-Causing Mutations in Families with Autosomal Dominant Retinitis Pigmentosa: A Screen of Known Genes in 200 Families.” In Investigative Ophthalmology & Visual Science, 3052-3064. 7th ed. Vol. 47. N.p.: The Association for Research in Vision and Ophthalmology. https://iovs.arvojournals.org/article.aspx?articleid=2125683. “DISEASE: Retinitis Pigmentosa.” n.d. Kyoto Encyclopedia for Genes and Genomes. www.kegg.jp/dbget-bin/www_bget?ds%3AH00527. “DISEASE: Leber Congenital Amaurosis.” n.d. Kyoto Encyclopedia for Genes and Genomes. www.kegg.jp/dbget-bin/www_bget?ds%3AH00837. Azimi, F., M. Jafariyan, S. Khatami, Y. Mortazavi, and M. Azad. 2014. “Assessment of Thiopurine–based drugs according to Thiopurine S-methyltransferase genotype in patients with Acute Lymphoblastic Leukemia.” National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3980020/. Ansari, A., A. De Sica, M. Smith, K. Gilshenan, L. Fairbanks, A. Marinaki, J. Sanderson, and J. Duley. 2008. “Influence of xanthine oxidase on thiopurine metabolism in Crohn’s disease.” Wiley Online Library. https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-2036.2008.03768.x. American Association for Clinical Chemistry. 2019. “Genetic Testing Techniques.” Lab Tests Online. labtestsonline.org/genetic-testing-techniques. Common Mutation Helpful Information A- adenine G- guanine C- cytosine T- thymine Codon- 3 nucleotides that form a specific genetic code in DNA or RNA that codes for an amino acid, which will go on to create proteins A mutation in which a nucleotide is replaced, added, or deleted can result in a change in its genetic code. How to Read Mutation Notation: ex) 169G>C (Ala57Pro) Codon 169 originally had G (guanine) but a mutation replaced G with C (cytosine). This changes the amino acid created from Ala to Pro. ex) c.452G > A (p.Trp151Stop) Codon 452 originally had G (guanine) but a mutation replaced G with A (adenine). This changes the amino acid created from Trp to an ending sequence for the chain of amino acids that would have been created. About the author Eesha Bethi is currently a junior at Carroll Senior High School in Southlake, Texas. She has always been interested in STEM fields, specifically molecular biology, and hopes to pursue a career in medicine in the future. Mixed with her interest in autoimmune diseases, this started the basis of her research. She also enjoys humanitarian volunteer activities and public speaking.

Author: Thomas Higman Abstract Cancerous cells can be detected in many ways, one of which uses fractal dimension (a number that describes the complexity of a shape). We analyse the “box counting” method of finding the fractal dimension of an object and the application that this has in finding the fractal dimension of an animal cell. We review research where the fractal dimensions of cancerous and healthy cells are recorded by automated means to demonstrate how effective an automated cancer diagnosis system might be. Fractal dimension is not yet the most effective way to detect cancer, as there is an overlap in fractal dimension values that both healthy and cancerous cells could have. With further research into these processes, this type of diagnosis system could detect cancer earlier. Introduction Since the popularization of fractals by the French polymath Benoit B. Mandelbrot in the mid 1970s, fractal research has played an increasingly important role in many fields of modern science. Fractals occur everywhere, from fractal patterns in art to pathological functions in mathematics. With the use of fractal dimension, a number that describes the complexity of a shape, many complex objects such as mountains or clouds can be characterized. Fractal research has been used for many things, ranging from digital image compression to the prediction of zones of aftershock after an earthquake has hit [1] [2]. A growing application of fractals is in the autonomous detecting of cancerous cells from a sample, which could be pivotal in reducing the number of lives lost to cancer. Most notably, in 2016, Chan and Tuszynski were able to achieve an average accuracy of 0.964 in using a computer system to detect whether a cell is cancerous or healthy from its fractal dimension [3]. We explore the possibility of using fractals to detect cancer earlier and automatically in order to save more lives. Fractals and Fractal Dimension Fractals are geometrical shapes that have an infinite level of detail, just like a picture that produces ever more detail as you zoom into it. As Pickover notes, “The detail continues on for many magnifications – like an endless nesting of Russian dolls within dolls”. Although it is hard to give an exact definition of a fractal without the use of advanced mathematics, there are several basic requirements for an object to be fractal These are: An infinite level of detail Self-similarity A repeated construction A non-integer dimension Self-similarity refers to the shape in question being made out of smaller – perhaps rotated – parts of itself, as can be seen in Figure 1. Self-similarity creates infinitely detailed structures, as the fractal is repeatedly self-similar on an infinite number of scales. A repeated construction refers to the fractal being generated by a repeated rule; Figure 1 was generated by splitting a triangle into 4 equilateral triangles and then removing the middle triangle. When this rule is iterated infinitely, the fractal called the Sierpinski gasket is produced. In Figure 1, the rule was repeated 5 times, which is enough to visualize the Sierpinski gasket at print resolution. In mathematics, this simple rule is called a function and the repeating of it is called iteration. To explain non-integer dimensions consider the following analogy. If a one-dimensional object is a straight line and a two-dimensional object is (for example) a square, then an object with a dimension between the first and second dimension is an object that is in-between these two shapes. For example, imagine a line that zigzags and curves so intricately that it partially fills the plane. This would have a non-integer (otherwise known as fractal) dimension of between 1 and 2, perhaps 1.543 to give an example. It is worth noting however that, due to the natural limitation of our universe, there can be no infinitely self-similar physical fractals, only ever models that are accurate to a limited range of scales. This is because our size range is limited by the size of the smallest subatomic particles and, even though they may be extremely small, these objects are not infinite in detail. The dimension of a fractal is a key number that describes the complexity and roughness of a physical or geometrical object, meaning it can be applied to real life objects and shapes in a 2D plane. While geometric fractals are made by iterating a function, physical fractals can be produced by various processes such as diffusion-limited aggregation (where particles move with random motion before attaching to a main structure, which is how coral grows) or tumour growth. There are different ways to find the fractal dimension but the simplest is by using the box counting method. By taking a shape and superimposing grids with a decreasing grid box side length over the shape, the number of grid boxes that contain (or are superimposed over) the shape can be recorded. This is called the box-counting method. An example for the superposition of a grid onto a fractal is shown in Figure 2 with the Koch curve (or Koch Snowflake) and the values are shown in Table 1. As we can see from Table 1, as the scale increases, more detail is included as more boxes contain a part of the Koch curve. This data can now be used with the following formula to calculate the fractal dimension of the Koch curve: given that r is the grid box side-length (or scale) and N(r) is the number of boxes that contain part of the Koch curve, the fractal dimension, d, is approximated by- where ln denotes logarithm to the base e (the natural logarithm). The constant e is known as Euler’s number and is approximately equal to 2.718. A logarithm is a function where, if we choose a base and an input number, the output number is the exponent (or power) the base needs in order to become the input number. That is to say, if    then  . Table 1 shows the computed approximations to the fractal dimension of the Koch Curve by using the values obtained from Figure 2, and some extra data taken from Fractals: A Very Short Introduction, in the equation shown above [6]. Once again, as the scale of measurement increases, the number of boxes containing a part of the Koch curve increases nonlinearly. In regards to the fractal dimension values obtained, there is a period of stabilization for the first few values obtained from different scales. This means simply that the values do not give us an accurate idea of the fractal dimension, as the scale isn’t small enough. However, as the scale of grid box side length decreases, the approximation begins to converge towards the dimension 1.260. As the fractal dimension refers to the unusual scaling behavior of an object, the smaller the scale used is, the more accurate the calculated fractal dimension is. This is true for all geometric fractals, but for physical fractals this can only be the case down to a certain scale (after which the physical object loses its fractal properties). The fractal dimension of the Koch curve is actually log(4)/ log(3) (as a direct result of the Koch curve being made from 4 pieces of itself at the scale 1/3) which is 1.2618. . . [6]. Table 1: Approximate fractal dimension of the Koch curve from the box counting method. Results of Cancer Detection Using Fractal Dimension Cancer is a disease that affects one in two British people during their lifetime [7]. Among the large number of scientists researching cancer, some have turned to fractals to aid diagnosis. Healthy cells usually look smooth under a microscope whereas malignant tumour (cancer) cells usually have a rough and abnormal shape [8]. Figure 3 shows an example of the structure of cancerous and healthy cells. The shapes of these cells cannot be described by using Euclidean geometry, but a cell’s fractal dimension captures some of its characteristics in a single number. It is then possible to tell whether a cell is cancerous or not by comparing its fractal dimension with that of a healthy cell [9]. In 2000, in one of the first papers to look at the link between fractal dimension and cancer, Baish and Jain write that “Fractal analysis shows its greatest promise as an objective measure of seemingly random structures and as a tool for examining the mechanistic origins of pathological form” [11]. This paper mostly looked at the comparison between the fractal dimension of tumour vessels and blood vessels, such as arteries and veins, and it demonstrated the great potential that fractal dimension has for characterizing cancer cells. Soon after, Bauer and Mackenzie gave one of the first examples where computer programs and algorithms were used to find the fractal dimension of cells [12]. They found that when the box-counting method was used to find the fractal dimension of a variety of cells from healthy patients and patients with hairy-cell leukaemia, none of the healthy cells had a fractal dimension (d) of more than 1.28 but a large percentage of the cancer cells had d > 1.28. This is shown in the histogram in Figure 4. This study proved the capability of fractal dimension to distinguish cancer cells from healthy cells. It is worth noting that a large proportion of the cancer cells also had d < 1.28, but when a sufficiently large amount of data is compiled it is clear that the healthy cells have a smaller fractal dimension on average. Fifteen years later, in 2016, Chan and Tuszynski looked at automatically finding the fractal dimension of cells [3]. They used a large database of breast cancer histopathology images (microscopic images of a tissue for studying a disease, including both benign and malignant tumour cells, to test the accuracy of a computer prediction system at detecting whether a cell was cancerous or not. The system used a support vector machine algorithm, a basic form of machine learning, in order to find a cut-off fractional dimension for cancerous cells. The system was 0.979 accurate at classifying a cell at 40x magnification, which means that it formed the correct diagnosis about 98 times in 100. However, it was only 0.556 accurate at predicting what subtype of breast cancer the patient had (if any), which in reality is no better than a coin toss. It is interesting to note that magnifications above 40x yielded worse results, perhaps as a result of the natural limitations objects have in real life (they don’t have an infinite structure). This data shows great potential for the use of machines to classify benign and malignant tumour cells, particularly as, even when the size of the training set was reduced, there was an average accuracy of 0.964. However, this data does have some limitations: all of the images used were of a consistent standard (as they were all taken from an online database), which helped to produce reliable results but doesn’t give an accurate representation of the accuracy of results when different dyes and methods of obtaining images of the cells are used. Moreover, only breast cells were used in this research. Chan and Tuszynski concluded by stating, “At the very least, this could be used to assist in the diagnostic procedures and reduce the time burden on pathologists”. Although oncologists are finding different ways of detecting cancer cells and classifying them, there are many benefits to implementing a fractal dimension-based system. By using machine learning and fractal dimension, the issue of human subjectivity is mostly eliminated (perhaps only being needed in less clear circumstances). This could lead to a standardisation in the classification of cancer cells and could help scientists to understand more about the way cancer grows. For example, Chan and Tuszynski speculate that there could be a “correlation between the fractal dimension of the pathology slide and a clinical outcome measure such as 5 year survival of the patient” [3]. Automatic machine prediction could also speed up the process of cancer detection and could mean that more areas of the body could be tested for cancer. However, there are possible safety concerns and problems with using machine prediction. The machine could fail to correctly predict whether cancer cells are present in a patient, which could lead to the late detection of cancer or a false sense of safety in the patient. This could be resolved by requiring cross-examination of results with an oncologist, a doctor who treats cancer patients, but this could remove any speed benefits that a machine prediction could bring. Furthermore, so far only limited types of cancer cells have been tested with this technology and only in a retrospective analysis, not a blind experiment, meaning that it is unclear how successful this technology could be at detecting all cancer cells in a real environment. This method also requires the taking of biopsies of patients – which may not always be desirable since it involves the collection of cell samples using surgical tools and may lead to infections – and the digitization of each tissue sample, but in most cases biopsies would be taken regardless. If the same dye is not used in every biopsy to stain the cell the results could be unreliably recorded. In another paper by Tambasco, it was found that the higher values of fractal dimension were associated with lower tumour malignancy, which is the opposite of the results in Chan and Tuszynski’s study [3] [13]. The two studies used different dyes; in the latter the dye showed more extracellular detail, which may be why the fractal dimension results were different. It is clear that much further research and testing needs to be done on this topic before it could begin to be trialed for implementation. Conclusion Since the surge in popularity of fractals in the 1970s, scientists have found new ways to describe and explain a whole manner of previously “pathological” problems and “mathematical monsters” [14]. It is clear that fractals will play an important role in future science because of their unique way of describing nature and its systems. Wheeler, an American theoretical physicist, stated that, “no one will be considered scientifically literate tomorrow who is not familiar with fractals” [16]. The threat of cancer has been an issue that has impacted most people, whether indirectly or directly. There are different ways of automatically detecting cancer, such as automatic segmentation of cell images to classify asymmetric abnormalities [16] [17]. However the use of fractal dimension to detect cancer cells is becoming more useful because it can give an estimate of the malignancy of a cancer cell. Fractal dimension might be most effective when working alongside other diagnosis techniques and systems, particularly ones based on machine learning or deep learning. Although there is still much research to be done, it seems that fractal dimension based cancer diagnosis systems could be pivotal in increasing the speed of cancer detection, ultimately saving many lives. References Barnsley, Michael F., and Lyman P. Hurd. 1993. Fractal Image Compression. Natick, Massachusetts, United States: A K Peters Ltd.. Caneva, Alexander, and Vladimir Smirnov. 2005. Using the fractal dimension of earthquake distributions and the slope of the recurrence curve to forecast earthquakes in Colombia. Earth Sciences Research Journal, 8, p. 3–9. Chan, Alan, and Jack A. Tuszynski. 2016. Automatic prediction of tumour malignancy in breast cancer with fractal dimension. Royal Society Open Science, 3 , https://doi.org/10.1098/rsos.160558. Pickover, Clifford A. 2009. The Math Book. New York: Sterling Publishing Co., Inc. Higham, Desmond. J., and Nicholas J. Higham. 2017. MATLAB Guide. Philadelphia, PA, USA: Society for Industrial and Applied Mathematics, third ed., p. 16. Falconer, Kenneth. 2013. Fractals: A Very Short Introduction. New York: Oxford University Press, p. 47. Ahmad, Ahmad. S., and N Ormiston-Smith, and Peter D. Sasieni. 2015. Trends in the lifetime risks of developing cancer in Great Britain: comparison of risk for those born from 1930 to 1960. British Journal of Cancer, 112, p. 943. Eldridge, Lynne. 2018. Cancer Cells vs. Normal Cells: How Are They Different. (https://www.verywellhealth.com/cancer-cells-vs-normal-cells-2248794). Marius, Ioanes, and Adriana Isvoran. 2006. About Applying Fractal Geometry Concepts in Biology and Medicine. Annals of West University of Timisoara: Series of Biology, 9, p. 23-30, (http://www.biologie.uvt.ro/annals/vol_9/vol_IX_23-30_Ioanes.pdf). “Normal and cancer cells structure”. Wikimedia Commons. (https://commons.wikimedia.org/wiki/File:Normal_and_cancer_cells_structure.jpg). Baish, James W., and Rakesh K. Jain. 2000. Fractals and Cancer. Cancer Research, 60 xiv, p. 3683-3688, (http://cancerres.aacrjournals.org/content/60/14/3683.abstract). Bauer, Wolfgang and Charles D. Mackenzie. 2001. Cancer detection on a cell-by-cell basis using a fractal dimension analysis. Acta Physica Hungarica Series A, Heavy Ion Physics, 14 i, p. 43-50, https://doi.org/10.1556/APH.14.2001.1-4.6. Tambasco, Mauro, and Anthony M. Magliocco. 2008. Relationship between tumor grade and computed architectural complexity in breast cancer specimens. Human Pathology, 39 5, p. 740-746, https://doi.org/10.1016/j.humpath.2007.10.001. Zobitz, Jennifer. 1987. Fractals: Mathematical monsters. Pi Mu Epsilon Journal, 8, p. 425, http://www.jstor.org/stable/24337748. Lesmoir-Gordon, Nigel, and Will Rood, and Ralph Edney. 2009. Fractals: A Graphic 
Guide. Icon Books, London, p. 3. “Automated Cancer Diagnosis”. British Council Turkey, accessed November 2, 2018, https://www.britishcouncil.org.tr/en/programmes/education/cubed/automated-cancer-diagnosis. Hairong, Qi, and Jonathan Head. F. 2001. Asymmetry analysis using automatic segmentation and classification for breast cancer detection in thermograms. 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 10.1109/IEMBS.2001.1017386. About the Author Thomas Higham is in year 13 studying at Bolton School Boys\’ Division. He has a great passion for maths and particularly enjoys reading about all things fractal related. He hopes to have a career in maths and maybe find his own application for fractals in applied mathematics.

Abstract Statistics, in its modern sense, is a field of study that aims to analyze natural phenomena through mathematical means. As a highly diverse and versatile discipline, statistics has been developed not only in the areas of STEM subjects, but also in the spheres of social science, economics, and humanities. More recently, the use of statistics in big data has increased, mainly in relevance to machine learning and artificial intelligence. In amalgamation with these subjects, statistics has been used in numerical and textual analysis, and is also starting to be applied in areas previously thought of as exclusively the domain of humans, such as the arts. Although there are differences in conventional statistics and these new developments, their purpose, which is finding associations in data, remains the same. Introduction By reviewing this history and current developments of statistics, this article aims to outline the possible future trajectory for statistics in this new age of big data, as well as the increased role statistics now has in primary and secondary schools as a result of its expanding role in multiple disciplines. Moreover, it also addresses some realistic criticisms and concerns about the subject in the context of the rapidly advancing technology of our world. Historical Development While historical records date the first population census — an important part of statistics — to have been in 2AD during the Han Dynasty of China, the first form of modern statistics is cited to have emerged in 1662, when John Graunt founded the science of demography, a field for the statistical study of human populations. Among his notable contributions to the development of statistics, his creation of census methods to analyze survival rates of human populations according to age paved the way for the framework of modern demography. While Graunt is largely responsible for the creation of a systematic approach to collecting and analyzing human data, the first statistical work had emerged long before his time in the book Manuscript on Deciphering Cryptographic Messages. This was published some time in the 9th century, and the author, Al-Kindi, discusses methods of using statistical inference—the usage of statistics from sample data to derive inferences about the entire population—and frequency analysis—the study of repetition in ciphertext—to decode encrypted messages. This book later laid the groundwork for modern cryptanalysis and statistics[1]. The first step toward the development of statistics in its modern form was in the 19th century when two mathematicians, Sir Francis Galton and Karl Pearson, introduced to statistics the notion of a standard deviation, a numerical representation of the deviation of a set of data from its mean; methods of identifying correlation, a measure of the strength of a directly proportional relationship between two quantitative variables; and regression analysis, a statistical method to determine the graphical relationship between the independent variable and the dependent variable in a study. These new developments allowed for statistics to not only be more actively used to study human demographics, but also became a participant in the analysis of industry and politics. They later went on to found the first university statistics department and the first statistics journal. This was the beginning of statistics as an independent field of study[2]. The second wave of modern statistics came in the first half of the 1900s, when it started becoming actively incorporated into research works and higher education curricula. A notable contributor in this time period was Ronald Fisher, whose major publications on statistics helped outline statistical methods for researchers. He gave directions on how to design experiments to avoid unintentional bias and other human errors; described how statistical data collection and analysis methods could be improved through means such as randomized design, a data collection method where subjects are assigned random values of the variable in question which in turn removes possible unintentional bias on the part of both subjects and researchers; and set an example of how statistics could be used to explore various questions if a valid null hypothesis—the hypothesis in a statistical analysis that states that there is no significant difference (other than those as a result of human error during sampling) between two population variables in question—an alternative hypothesis—the hypothesis that states that there is a discernible difference between the two variables in a statistical study—and a set of data could be generated from the experiment. One such example was proving the existence of changes in crop yields, analyzed and published by Fisher in 1921[3]. In the latter half of the 20th century, the development of supercomputers and personal computers led to greater amounts of information being stored digitally, causing a rapid inflation in the amounts of large data. This resulted in the advent of the term “big data,” which refers to sizable volumes of data that can be analyzed to identify patterns or trends in them. Applications of big data range from monitoring large-scale financial activities, such as international trade, to customer analysis for effective social media marketing, and with this growing role of big data has come a subsequent increase in the importance of statistics in managing and analyzing it. The role of statistics in education and research Having become an official discipline at the university level in 1911, statistics has since then been incorporated into departments of education on various different levels. Notably, basic statistical concepts were first introduced to high schools in the 1920s. The 1940s and 1950s saw vigorous effort to broaden the availability of statistical education from younger years, as spurred on by the governmental and social efforts during and after the Second World War, where statistical analysis became frequently used to analyze military performance, casualties, and more. While educational endeavors laxed in the 1960s and 1970s, the boom of big data brought back the interest in statistics from the 1980s onward[4]. Presently, statistics is taught in both primary and secondary schools, and is also offered as Honor and Advanced Placement courses to many high school students hoping to study the subject at the college level and beyond.The field of statistics has also become a crucial element in research, ranging from predicting the best price of commodities based on levels of consumer demand in the commercial sphere to determining the effectiveness of certain treatments in the medical profession. By incorporating statistics into research, researchers have been able to find ways to represent the credibility of their findings through data analysis, and have also been able to find and prove causal relationships using hypothesis testing. Statistics is especially necessary and irreplaceable in research in that, as mentioned, it is the most accurate form of measuring the reliability of the results drawn from a study. Whether that be measuring the confidence interval of a population mean, or testing whether a new treatment has any effect on patients when compared with a placebo, it places mathematical limitations on the objective aspects of research[5]. Moreover, statistics allows for a study conducted on a sample from a defined population to be extended to that general population given that the research satisfies a number of conditions, the sample being randomly chosen being one such prerequisite. This is one of the greatest strengths of statistics; the ability to extend the findings from a sample to the entire population without having to analyze every single data point. Statistics in Big Data and Artificial Intelligence In the age of big data and artificial intelligence (AI), intellectual reasoning and ability demonstrated by machines as opposed to humans, statistics is being utilized in education and research more than ever. Often combined with computer science and engineering, statistics is being used in many different capacities such as generating probability models through which complex data can filter through and then generate a model of best fit[6]. Even in this day and age, statistics continues to be transformed and applied in new ways to cope with the growing size and complexity of big data, as well as the many other rapid advancements being made in artificial intelligence. While a large portion of big data consists of quantitative data, qualitative statistics also plays a large role in it. Notably, the analysis of text messages using statistical techniques by artificial intelligence has become one of the forefronts of the application of modern statistics. Text mining is the process of deriving information, such as underlying sentiments, from a piece of text. This method is intertwined with sentiment analysis, which, to put simply, is the subjective analysis of textual data. The fundamental purpose of sentiment analysis is the classification of text through its underlying sentiments — positive, negative, or neutral[7]. For example, “the chef was very friendly” has a positive underlying sentiment, while the sentence “but the food was mediocre” has a negative connotation.While previous statistical techniques were underdeveloped for sentiment analysis to work effectively, recent developments in deep learning, which is a subfield of AI dedicated to mimicking the workings of the human brain[8], has allowed for greater, more complex sentiment analysis. A main application of sentiment analysis is in natural language processing (NLP)—a field of study of how computers can analyze human language and draw a conclusion about the connotation of a piece of text—which is often used to measure sentiments within corporates’ financial statements. For example, when a top management comments on its quarterly or annual performance, the level of positivity in this comment can be analyzed through NLP. The top management report is generally a piece of unorganized text, which NLP converts into a structured format that AI can then interpret. Through this process, the performance levels of companies can be gauged more effectively and accurately. To train computers to be able to identify these implicit undertones, researchers must first provide and educate it with a set of data related to its purpose. This training method also goes beyond sentiment analysis; if a machine is being trained to recognize and locate a human face in an image, as is often used in camera applications on phones, it must be given a large data set of pictures with human faces which can then be used for training purposes.This data set can be split into three different sections; training data, validation data, and testing data. Training data is the data that helps the AI machine learn new material by picking up patterns within the set of data. Training data consists of two parts; input information and corresponding target answers. Given the input information, the AI will be trained to output the target answers as often as possible, and the AI model can re-run over the training data numerous times until a solid pattern is identified. Validation data is similarly structured to training data in that it has both input and target information. By running the inputs in the validation data through the AI program, it is possible to see whether the model is able to churn out the target information as results, which would prove it to be successful. Testing data, which comes much after both training and validation data, is a series of inputs without any target information. Mimicking real-world applications, testing data aims to recreate a realistic environment in which it will be able to run. Testing data makes no improvements on the existing AI model. Instead, it tests if the AI model is able to make accurate predictions based on this testing data on a consistent basis[9]. If it proves successful in doing so, then the program is ruled to be ready for real-world usage. An example of these types of data used to create an AI program can be found in AlphaGo. AlphaGo is a computer program designed to play Go, a two-player board game involving black and white stones that the players alternate placing. The goal is to enclose as much of the board’s territory as possible. Countless records of previous professional Go games spanning back centuries contributed to the training data used to teach the AlphaGo program. Through analyzing the different moves that were taken by the Go players, the creators of AlphaGo then set up different versions of the program to play against each other, which served as its validation data. AlphaGo’s widely broadcasted matches against professional players, most notably Lee Sedol, was the program’s testing data[10]. The quality and quantity of training data is also crucial in creating an effective AI model. A large set of refined data will aid the AI in identifying statistical patterns and thereby more accurately fulfill its purpose. Using the aforementioned facial recognition example, this point can be elaborated on more clearly; if a large set of images containing human faces are given to the AI during training, it will be able to recognize patterns within human faces, such as the existence of two eyes, a nose, and a mouth, and thereby increase its success rate in identifying faces during testing. However, if images of trees and stones are mixed into the training data, then the AI program may find it more difficult to accurately perceive patterns within the given data set, and consequently become less effective in fulfilling its initial purpose. Moreover, being given a larger set of training data allows an AI model to make more accurate predictions, since it has a larger pool of information in which it can identify and apply patterns to. Training data is used for a range of purposes, such as the aforementioned image recognition, sentiment analysis, spam detection, and text categorization. A common theme among these different types of training data, however, is the possibility of wrong methods of training. Artificial intelligence, with its ability to mimic the process of human thought, also raises possibilities of negative inputs with incorrect target results creating a machine with a harmful thought process. For example, if an AI program is continuously shown images of aircrafts being bombed, and taught that the target result should be positive, then the machine may consider terrorist bombings or warfare to be positive when applied to real life. Artificial intelligence, like all things created by mankind, retains the potential to be used for a malevolent cause. In particular, because we do not understand all of the statistical techniques being used by computers to analyze training data, we must continue to tread cautiously in our efforts to develop and understand AI through the application of statistics. The statistical methods used to understand and categorize big data are by no means as simple as those used by human statisticians; in fact, many of the mechanisms used by computers to find and analyze patterns in data sets still remain a mystery to us. They cannot be labeled with discrete descriptions such as “standard deviation” or “normal distribution.” Instead, they are an amalgamation of various complex pattern-identifying and data-processing techniques.Furthermore, the statistical techniques used in the realm of big data and artificial intelligence are somewhat different from previous applications of statistics. For example, the previously mentioned training data is a novel subject that was only incorporated into statistics after the subject’s introduction to AI. Statistics, which had almost exclusively dealt with quantitative data in the past, is now also used to analyze qualitative data, creating a necessity for this training data. Training data also indicates another difference between conventional and modern applications of statistics, which is that statistics in AI and machine learning require supervised learning to find relationships in data, while conventional statistics requires regression analysis[11]. Conventional statistics is more intuitive to humans but limited in its usage. On the other hand, statistics in AI and machine learning is essentially a black box that cannot be explained through previous rules, but proves more efficient in deriving implications from larger and more diverse sets of data.However, despite these many distinctions, the subject’s fundamental purpose has not changed; statistics, in the end, is an effort to mathematically approach phenomenon, identify patterns in data, and apply our findings to new situations. Consequently, recent developments in statistics and its traditional applications should be used in conjunction with each other, cancelling each other’s drawbacks with their strengths. Criticisms about statistics Apart from the concerns raised on the use of statistics in the realm of artificial intelligence and big data, conventional statistics also has its fair share of criticisms. As a constantly changing, improving discipline, there continues to exist imperfections in statistics that we should always be cautious of when using statistical analysis in any situation. For example, in 2012, statistician Nate Silver used statistical analysis to successfully predict the results of the presidential election for all 50 states in the U.S[12]. While this brought about much media attention to the role of statistics in fields beyond the scope of learning it was commonly associated with, this event led to what could arguably be referred to as an overreliance on statistical prediction in the next U.S. presidential election. As can be seen by this example, there certainly exists shortcomings in statistics, both in the collection of statistical data and our use of it.Among the multiple criticisms frequently made about the subject, there is a recurring theme that can be found; they often condemn how it distorts our perception of phenomena by oversimplifying it. While statistics is a tool used to conveniently perceive the message portrayed to us by large sets of data, it is, in the end, a discipline based on averages and predictions. The real world does not always act with this in mind, and therefore deviates from statistical predictions most of the time. Moreover, data analysis is mostly done in the realm of quantitative data, so qualitative aspects of socio economic phenomena are often underrepresented in statistical results. This also makes it easier for statisticians to use data to understate or exaggerate the issue at hand, therefore making some statistical data unreliable[13]. However, we do need some form of numeric representation for situations that require comparison, so utilizing statistics is necessary. This is why overreliance on statistical analysis is both easy and dangerous to do. One example is the overreliance on GDP statistics; this usually leads to the conclusion that the economic situations of most citizens of a country are improving. This is not always the case, especially for countries whose economic disparity is also widening. The individual welfare of the population is not accurately and entirely reflected in the GDP of a nation, which only tells us its overall economic status — including its corporations, government, and net exports. Therefore, relying only on GDP statistics may lead to the inaccurate analysis of the personal welfare of the people. Statistics, in the end, is a discipline of averages and predictions. No matter how much effort researchers put into refining the analysis methods of numerical data, they will always fall short of being able to fully represent a real-life phenomenon by only deploying numbers. Statistics will always fall short of giving a definite answer about virtually anything. All conclusions made about hypotheses are never certain, and comparisons between two sets of data at best give a solid prediction. However, it must also be understood that this is the very definition of statistics. Statistics serves to give a better interpretation of complicated issues by removing certain factors that bring about uncertainty during the process of research; thus, it may be too much to expect statistics to be able to give an exact one-to-one portrayal of the situation it is analyzing. It is, like all other disciplines, used best when amalgamated with other approaches and fields. The future of statistics Statistics, with its ability to explore different social phenomena using situation hypotheses and reliably interpret nonphysical trends, is a rapidly growing discipline in the modern world. With the ability to be used in conjunction with a variety of other subjects such as mathematics, economics, the social sciences, and computer science, statistics is relevant and necessary in all kinds of different fields. While the future of statistics is not entirely clear — predictions on which domain it will be used most often in, and which spheres of knowledge it will most frequently intermingle with vary — it is safe to say that statistics will be taking on a similarly important, if not greater, role in our future than it is now. Statistics has already played a large role in helping us understand general trends in data, and with the world becoming increasingly interconnected, this unique aspect of statistics will only become more necessary. Big data and artificial intelligence are becoming the centerpiece of modern technological development, and because the statistical techniques being used in these fields are very different and entirely transcendental of the statistical mechanisms previously used by human statisticians, the adaptation of data analysis and statistical usage to this new trend is all the more necessary. Amalgamated with statistics, big data and AI have been explored in numerical and textual analysis for many years. This is not, however, the boundary of their potentials; efforts are already being made to expand their usage into the field of human creation, such as the arts. A major example is the development of artificial intelligence algorithms to find similarities between paintings by various artists by a team of researchers from MIT[14]. In a world that is increasingly reliant on different types and greater amounts of big data, statistics must evolve to fit its needs, and it, at this moment, seems to be walking down the right path. References [1] “History of Statistics.” Wikipedia, Wikimedia Foundation, 15 Aug. 2020, https://en.wikipedia.org/wiki/History_of_statistics. Accessed 19 Aug. 2020. [2] “Statistics.” Wikipedia, Wikimedia Foundation, 17 Aug. 2020, https://en.wikipedia.org/wiki/Statistics. Accessed 19 Aug. 2020. [3] Fisher, R. A. “Studies in Crop Variation. I. An Examination of the Yield of Dressed Grain from Broadbalk: The Journal of Agricultural Science.” Cambridge Core, Cambridge University Press, 27 Mar. 2009, www.cambridge.org/core/journals/journal-of-agricultural-science/article/studies-in-crop-variation-i-an-examination-of-the-yield-of-dressed-grain-from-broadbalk/882CB236D1EC608B1A6C74CA96F82CC3. Accessed 6 Oct. 2020. [4] Scheaffer, Richard L, and Tim Jacobbe. “Statistics Education in the K-12 Schools of the United States: A Brief History.” Journal of Statistics Education, vol. 22, no. 2, 2014, pp. 1–14., doi:https://doi.org/10.1080/10691898.2014.11889705. Accessed 15 Aug. 2020. [5] Calmorin, L. Statistics in Education and the Sciences. Rex Bookstore, Inc., 1997. [6] Secchi, Piercesare. “On the Role of Statistics in the Era of Big Data: A Call for a Debate.” Statistics & Probability Letters, vol. 136, 2018, pp. 10–14., https://www.sciencedirect.com/science/article/abs/pii/S0167715218300865. Accessed 16 Aug. 2020. [7] Gupta, Shashank. “Sentiment Analysis: Concept, Analysis and Applications.” Towards Data Science, Medium, 19 Jan. 2018, https://towardsdatascience.com/sentiment-analysis-concept-analysis-and-applications-6c94d6f58c17. Accessed 19 Aug. 2020. [8] Brownlee, Jason. “What Is Deep Learning?” Machine Learning Mastery, Machine Learning Mastery Pty. Ltd., 16 Aug. 2019, https://machinelearningmastery.com/what-is-deep-learning/. Accessed 20 Aug. 2020. [9] Smith, Daniel. “What Is AI Training Data?” Lionbridge, Lionbridge Technologies, Inc., 28 Dec. 2019, https://lionbridge.ai/articles/what-is-ai-training-data/. Accessed 20 Aug. 2020. [10] “AlphaGo: The Story so Far.” DeepMind, Google, 2020, https://deepmind.com/research/case-studies/alphago-the-story-so-far. Accessed 6 Oct. 2020. [11] Shah, Aatash. “Machine Learning vs Statistics.” KDnuggets, KDnuggets, 29 Nov. 2016, www.kdnuggets.com/2016/11/machine-learning-vs-statistics.html. Accessed 19 Aug. 2020. [12] O\’Hara, Bob. “How Did Nate Silver Predict the US Election?” The Guardian, Guardian News and Media, 8 Nov. 2012, www.theguardian.com/science/grrlscientist/2012/nov/08/nate-sliver-predict-us-election. Accessed 21 Aug. 2020. [13] Davies, William. “How Statistics Lost Their Power – and Why We Should Fear What Comes Next.” The Guardian, Guardian News and Media, 19 Jan. 2017, www.theguardian.com/politics/2017/jan/19/crisis-of-statistics-big-data-democracy. Accessed 21 Aug. 2020. [14] Gordon, Rachel. “Algorithm Finds Hidden Connections between Paintings at the Met.” MIT News, Massachusetts Institute of Technology, 29 July 2020, https://news.mit.edu/2020/algorithm-finds-hidden-connections-between-paintings-met-museum-0729. Accessed 6 Oct. 2020.

Author: Maritza Tsabitah Editor: Afreen Hossain Abstract The data obtained from the Apollo Lunar Astronauts highlights a concerning long-term risk: radiation-induced cardiovascular disease (RICVD) resulting from exposure to galactic cosmic rays (GCR). To ensure the success of future Moon-Mars missions, the development of an effective protection shield is imperative. This study aims to assess the effectiveness of a pumpkin configuration as a shield against GCR, considering its impact on the cardiovascular system, mortality rate data from the Apollo Lunar Astronauts, and insights from previous studies on magnetic shielding. The findings suggest that the pumpkin configuration holds promise in shielding against GCR, but further refinement of the concept and innovative advancements are needed for swift code implementation. This study advocates for considering the pumpkin configuration as an alternative, aligning it with NHS training and the recent Orion Protection Plan, HERA, which can efficiently detect and categorize space radiation. Introduction In 2022, the Artemis Program heralded the continuation of the Moon to Mars expedition and extended its policy for International Space Station (ISS) operation. This signifies the onset of exploration beyond Low Earth Orbit (LEO) in the foreseeable future. However, ensuring the health of astronauts remains a primary concern for the success of these missions, particularly given the challenges posed by space radiation. The primary sources of space radiation include Solar Particle Events (SPE) and Galactic Cosmic Rays (GCR). While various active shielding methods can insulate against SPE, the formidable challenge lies in shielding against GCR, primarily due to the presence of high-energy ions (HZE) that have the potential to damage physiological functions, notably the cardiovascular system. During a Mars mission, astronauts may be exposed to approximately 1Sv of GCR, resulting in a potential 5% increase in cardiovascular damage. The impacts of GCR Galactic cosmic rays (GCR) harbor the potential to instigate severe pathologies, encompassing fatal conditions like cancer, cardiovascular disease, and organ inflammation. A retrospective analysis of the mortality data of 20 deceased US astronauts spanning from 1959 to 1991 underscores the gravity of cardiovascular implications, attributing 10% of deaths to cardiovascular disease and 5% to cancer. This investigation illuminates the vulnerability of the human cardiovascular system to the deleterious effects of space radiation. In a meticulous examination, patients exposed to radiation were compared with those diagnosed with radiation-induced cardiovascular disease (RICVD), specifically through chest X-ray and gamma radiation. The outcomes are unmistakable, with RICVD demonstrating an alarming radiation dose of 500Gy, significantly surpassing the range of chest X-rays (0.1 - 120 Gy) and gamma radiation (more than 3Gy) . The composite elements of GCR, prominently featuring hydrogen (H), iron (Fe), helium (He), and silicon (Si), impart a spectrum of detrimental impacts on the cardiovascular system. These repercussions encompass endothelial dysfunction in the aortic wall, identified as the primary instigator of RICVD, myocardial damage due to apoptosis, perpetuation of a chronic inflammatory state, upregulation of oxidative enzymes, and DNA double-strand destruction. Notably, astronauts in low earth orbit (LEO) may find reprieve from radiation exposure owing to the protective influence of the magnetosphere. However, those venturing beyond this protective shield face heightened susceptibility. Data extrapolated from the Apollo Lunar astronauts accentuates this risk, revealing a cardiovascular mortality rate 4-5 times higher than their LEO counterparts. As the adage goes, anticipation becomes paramount in navigating the intricate interplay between space exploration and cardiovascular well-being. Experiment with shielding: pumpkin configuration Numerous initiatives have been undertaken to explore Active Shielding Methods (ASM) to counter the formidable energy of Galactic Cosmic Rays (GCR). Diverging from the effectiveness observed in Solar Particle Events (SPE), GCR proves resistant to preventive measures involving Electrostatic Fields (EF) and Plasma Shielding (PS), primarily due to their focus on protons rather than High-Energy Ions (HZE). Intriguingly, past research has unveiled a promising avenue in the utilization of Superconducting Materials (SM) within the unique framework of the Pumpkin Configuration (PC). High-temperature superconductors, such as Niobium-Titanium (NbTi) and Niobium-Tin (NbSn), have emerged as the most efficient options per unit mass for Active Shielding against Space Radiation (ASR). Leveraging the Lorentz Force (LF), these materials induce particle motion perpendicular to the Magnetic Field (MF), thereby altering the trajectories of charged particles. Recent advancements in the study of the Pumpkin Configuration (PC) have notably propelled the transition of Magnetic Shielding Concepts (MSC) from theoretical constructs to practical applications in Spacecraft Design (SD). Result The pumpkin configuration refers to a multiple toroid magnet system in which each toroid is built with three racetrack coils and has a lower construction mass than a typical toroidal structure. Furthermore, its magnet can defend a 1083m volume (5m diameter by 5.5m long), reducing the dose of free space by 45%, and it weighs 54. This could reduce the GCR and allow for adequate dosages to be absorbed. However, it would not be able to totally protect the astronauts from the GCR because there are no recognized radiation limitations for the mission and the long-term physical impact has not been well examined. This approach has the potential to limit the exposure, but it has yet to be thoroughly researched and improved. Another reason that would make implementation difficult is the large number of codes required to model the heavy ions and licensing issues. Alternatives Nonetheless, there are techniques to safeguard the astronauts' cardiovascular health in preparation for the near-term trip. NASA collaborated with Orion to develop the Orion Radiation Protection Plan, which includes the Hybrid Electronic Radiation (HERA) operation process. HERA is designed to advise crew members if they need to seek shelter in the radiation case and to characterise the sort of shielding they encounter. Non-Technical Skill (NTS) training has also been proven to reduce RICVD by 18% in 1-year death rates. The compatibility of the NTS training in leadership, teamwork, situation awareness, and surgical skills will result in better mission, safety, and health results for the crews. Conclusion The space mission beyond the LEO is limited by GCR which contains HZE as a leading cause of radiation-induced cardiovascular disease among the Apollo Lunar astronauts. Many people have devised a solution to protect astronauts by employing a magnetic shield with a pumpkin configuration of superconducting materials and the Lorentz force. The concept, however, has not been thoroughly tested and developed. It also requires a large number of codes to be deployed for HZE. As an alternative to the neartime mission, the collaboration between NASA and Orion in establishing the Orion Radiation Protection Plan would be an effective way to reduce the risk of RICVD. The NTS training also has shown positive outcomes to ensure mission success with safe and healthy astronauts, which could be a fundamental requirement for the Moon-Mars astronauts. References Ferone, Kristine , Charles Willis, Fada Guan, Jingfei Ma, Leif Peterson, and Stephen Kry. "A Review of Magnetic Shielding Technology for Space Radiation." Radiation 1, no. 3 (2023): 46-57. https://doi.org/10.3390/radiation3010005 Huff, Janice L., Lanik Plante, Steve Blattnig, Ryan B. Norman, Mark P. Little, Amit Khera, Lisa C. Simonsen, and Zarana S. Patel. "Cardiovascular Disease Risk Modeling for Astronauts: Making the Leap From Earth to Space." Frontiers 9, (2022). https://doi.org/10.3389/fcvm.2022.873597 Meerman, Manon, Tom C. Gartner, Jan W. Buikema, Sean M. Wu, Sailay Siddiqi, Carljin V. Bouten, K J. Grande-Allen, Willem J. Suyker, and Jesper Hjortnaes. "Myocardial Disease and Long-Distance Space Travel: Solving the Radiation Problem." Frontiers 8, (2021). https://doi.org/10.3389/fcvm.2021.631985 Townsend, L. W. “Overview of active methods for shielding spacecraft from energetic space radiation.” 1st International Workshops on Space Radiation Research and 11th Annual NASA Space Radiation Health Investigators' Workshop Arona, (2000): 1-2. Abadie, L. J., Cranford, N., Lloyd, C. W., Shelhamer, M. J., and Turner, J. L. “The Human Body in Space.” NASA, February 3, 2021. Accessed July 15, 2023. https://www.nasa.gov/hrp/bodyinspace Delp, M. D., Charvat, J. M., Limoli, C. L., Globus, R. K., and Ghosh, P. “Apollo Lunar Astronauts Show Higher Cardiovascular Disease Mortality: Possible Deep Space Radiation Effects on the Vascular Endothelium.” Scientific Reports, (2016). https://doi.org/10.1038/srep29901 Al Zaman, M. A., Maruf, H. A., Islam, M. R., and Panna, N. “Study on superconducting magnetic shields for the manned long termed space voyages.” The Egyptian Journal of Remote Sensing and Space Science 24, no.2 (2021): 203-210. https://doi.org/10.1016/j.ejrs.2021.01.001 Norbury, J. W., Schimmerling, W., Slaba, T. C., Azzam, E. I., Badavi, F. F., Baiocco, G., . “Galactic cosmic ray simulation at the NASA Space Radiation Laboratory.” Life Sciences in Space Research 8 no.2 (2016): 38-51. https://doi.org/10.1016/j.lssr.2016.02.001 Bird, E., Hargens, A. R., and Petersen, L. G. “Magnitude of Cardiovascular System Response is Dependent on the Dose of Applied External Pressure in Lower Body Negative and Positive Pressure Devices.” Frontier, (2019). https://doi.org/10.3389/conf.fphys.2018.26.00031 Robertson, J., Dias, R. D., Gupta, A., Marshburn, T., Lipsitz, S. R., & Pozner, C. N. (2020). “Medical Event Management for Future Deep Space Exploration Missions to Mars.” Journal of Surgical Research, 246, 305-314. https://doi.org/10.1016/j.jss.2019.09.065

Author: Abhipsha Sahu Introduction Happy Leap Year! A quirk of our current calendar system is the fact that every four years, an additional day gets added to the month of February, giving us the 29th of February: the leap day. The leap day owes its existence to the fact that the Earth takes about 365.25 days to orbit the sun. That quarter of a day adds up to a full missing day every four years. The concept of leap years often leads us to feel like the passage of time is rather arbitrary- and to some degree, it is! The leap day is why, even across timescales of hundreds of years, January is always a cold month in the northern hemisphere, and why the southern hemisphere always has warm christmases. It keeps things consistent between the solar year and our calendar year. Leap years are just one consequence of the earth’s orbital characteristics, but what else does it mean for us? Goldilocks and the Origin of Life Perhaps the most unique thing about the Earth’s orbit around the sun is its distance from the sun. The Earth is often described to lie in what is known as the “Goldilocks Zone” which is a region where water can exist in a primarily liquid form. Liquid water on earth has long been thought to be the reason life exists, as biological models and existing archaeological evidence indicate that early life originated in the Earth’s oceans. By far the most important quirk of the Earth’s orbit is that it exists within the region that allowed life to exist. Given that Earth is the only planet currently known to be inhabited by living organisms, our unique orbit is crucial to our very lives. An orbit too far or too close to the sun would’ve made the evolution of life on earth completely impossible. Seasons in the Sun-Earth Orbital System The most obvious effect that the Earth’s orbit around the sun has is that it creates seasons. It is a popular misconception that seasons arise due to the physical distance from the earth to the sun changing throughout the course of a year. While it is true that due to the Earth’s orbit being elliptical, it is sometimes further away from the sun than at other times of the year, this difference is not significant enough to cause significant seasonal variation. In reality, seasonal variation is almost entirely a result of the tilt of the earth’s rotational axis, also known as its “obliquity”, as the planet moves around the sun. This results in the distribution of sunlight across the earth being uneven across the two hemispheres. During summer months, the incoming solar radiation is simply more “direct”, resulting in hotter weather. This axial tilt also explains why days are longer in the summers and shorter in the winters. The sun’s rays are more direct throughout summer months, and therefore cover the surface for a longer time. The axial tilt is also why seasons in the northern and southern hemispheres are always opposed. While one hemisphere receives higher intensity solar radiation and experiences summer, the other is tilted away, thus giving rise to winter. What do Ice Ages and the Pole Stars Have in Common? The earth’s orbit is also responsible for long-term variation in the Earth’s climate, through a series of cyclic changes known as Milankovitch Cycles. These cycles are governed by changes related to three main characteristics: the Earth’s axial tilt or Obliquity, the shape of Earth’s orbit or Eccentricity, and the direction in which the Earth’s rotational axis points, or Precession. The angle at which the Earth’s axis is tilted varies between two extremes, about 21.4 degrees and 24.5 degrees. At greater angles, the differences between seasons is sharper. Therefore, over millions of years, seasonal variation becomes more extreme before gradually becoming more uniform. The eccentricity of the Earth’s orbit is a measure of how elliptical it is. While planetary orbits generally have quite low eccentricities, the fact that the Earth is sometimes closer to the sun and sometimes further away does have small impacts on its climate by impacting the length of seasons. The eccentricity of the earth oscillates in an approximately hundred-thousand year long cycle. While this variation doesn’t significantly impact the Earth’s climate across short-term time scales, it will over long time scales. At higher eccentricities, certain seasons will be significantly longer than others. When the orbital eccentricity is lowest, this variation decreases to almost none. Precession is a phenomenon by which the direction in which the Earth’s axis periodically “wobbles” and changes direction. Although this change is slow, it eventually leads to variation in the Earth’s climate by controlling how extreme seasons are in each hemisphere by controlling which one experiences summer at perihelion. Aside from climatic variation, precession is also why the pole stars change every few tens of thousands of years. The Milankovitch cycles combined are responsible for long-term climatic patterns like ice ages. The effect of Earth’s orbit on seasonal variation is still an active field of research, as mapping out these long-term patterns can get quite complicated and many questions regarding the subject still remain unanswered. Conclusion Seasonal weather patterns and climatic cycles similar to the Milankovitch Cycles are not unique to the earth, and are an almost universal consequence of planetary orbital dynamics. However, at this point in human history, it is only on Earth that they directly affect us. Perhaps one day, a multiplanetary human race will investigate the various ways in which planets’ orbits affect everyday life. For now, plenty remains to be understood about our own planet’s movement around our sun. In 2024, we can celebrate the 29th of February as one such lovely consequence. Once again, Happy Leap Year! References [1] https://warwick.ac.uk/newsandevents/knowledgecentre/science/physics-astrophysics/leap_years/ [2] https://ugc.berkeley.edu/background-content/earths-spin-tilt-orbit/ [3] https://climate.nasa.gov/news/2948/milankovitch-orbital-cycles-and-their-role-in-earths-climate/ [4] https://www.treehugger.com/everything-you-need-to-know-about-earths-orbit-and-climate-cha-4864100

Author: Afreen Hossain Introduction: Number theory is a fascinating branch of mathematics that deals with the properties and relationships of integers. It may sound intimidating, but at its core, number theory explores the fundamental nature of numbers. In this beginner's guide, we'll take a journey through the basics of number theory, unraveling the mysteries that lie within the world of integers. A few topics under number theory are: The Foundation: Integers Let's start with the basics. Integers are whole numbers, both positive and negative, including zero. They form the foundation of number theory. Examples of integers are -3, -2, -1, 0, 1, 2, 3, and so on. Number theory focuses on understanding the unique properties and patterns within this set of numbers. Divisibility and Factors A key concept in number theory is divisibility. An integer 'a' is said to be divisible by another integer 'b' if 'a' can be expressed as 'b * c', where 'c' is also an integer. For example, 15 is divisible by 3, as 15 = 3 * 5. Factors are integers that divide a given number without leaving a remainder. For instance, the factors of 12 are 1, 2, 3, 4, 6, and 12. Number theory delves into understanding the properties of these divisors and how they relate to the integers. Prime Numbers Prime numbers are a crucial element in number theory. A prime number is a positive integer greater than 1 that has no positive divisors other than 1 and itself. Examples include 2, 3, 5, 7, and 11. Every positive integer can be uniquely expressed as a product of prime numbers, a concept known as the Fundamental Theorem of Arithmetic. Greatest Common Divisor (GCD) and Least Common Multiple (LCM) The GCD of two integers is the largest positive integer that divides both numbers. For example, the GCD of 8 and 12 is 4. The LCM of two integers is the smallest positive integer that is a multiple of both numbers. For instance, the LCM of 8 and 12 is 24. These concepts are fundamental in solving problems related to divisibility and factors. Modular Arithmetic Modular arithmetic is a fascinating aspect of number theory that deals with remainders. In modular arithmetic, we work with the remainder when dividing one number by another, known as the modulus. It has applications in cryptography, computer science, and various other fields. Quadratic Residues Quadratic Residues and Non-Residues: In modular arithmetic, quadratic residues are squares of integers that leave the same remainder when divided by a particular modulus. Non-residues are numbers that are not quadratic residues. Law of Quadratic Reciprocity: A fundamental result in number theory that establishes a relationship between the solvability of quadratic congruences with different moduli. Arithmetic Functions Euler's Totient Function: Counts the positive integers up to a given number that are coprime (have no common factors) with that number. Möbius Function: A function defined on the positive integers with applications in number theory. Ramanujan's Sum: A type of series discovered by the Indian mathematician Srinivasa Ramanujan. Additive Number Theory Partition Theory: Deals with ways of expressing a number as the sum of positive integers. Goldbach's Conjecture: Posits that every even integer greater than 2 can be expressed as the sum of two prime numbers. Waring's Problem: Explores the representation of numbers as sums of powers of positive integers. Elliptic Curves Elliptic Curve Arithmetic: Studies the properties of elliptic curves and their points. Applications in Cryptography: Elliptic curve cryptography utilizes the difficulty of solving certain problems related to elliptic curves for secure communication. Cryptography RSA Algorithm: A widely used public-key encryption method based on the difficulty of factoring large composite numbers. Diffie-Hellman Key Exchange: Allows two parties to establish a shared secret key over an untrusted communication channel. Applications of Number Theory in Modern Cryptography: Utilizes number theory concepts to ensure the security of cryptographic systems. These are only a few illustrations of the wide topic of number theory, which has numerous links and is still being studied. It can be used in many branches of information theory, computer science, and cryptography in addition to pure mathematics. References: YouTube: Home, 9 November 2017, https://www.programmersought.com/article/22024550501/. Number Theory and Cryptography I. Introduction, https://pi.math.cornell.edu/~mec/2008-2009/Anema/numbertheory/intro.html. YouTube: Home, 9 November 2017, https://www.profaccred.com/number-theory/. YouTube: Home, 9 November 2017, https://www.vaia.com/en-us/explanations/math/pure-maths/number-theory/. “Number Theory - Definition, Examples, Applications.” Cuemath, https://www.cuemath.com/numbers/number-theory/.

Author: Elianna Gadsby Editor: Afreen Hossain Why are bees so important? As we know bees are crucial in today's society. In fact, pollinators such as bees are responsible for 75% of pollination worldwide, as well as this, 1 in 3 mouthfuls of our food is dependent on pollinators such as bees. Researchers have also discovered that there is a direct correlation between pollination and the nutritional value of food. However, their numbers are depleting due to various diseases threatening large parts of our food supply, an example of one of these diseases is American Foulbrood (AFB). What is American Foulbrood disease? AFB is caused by bacterial spores of the Paenibacillus. It is one of the most devastating bee diseases. Young honey bees ingest the spores in their food, and in 1-2 days the spores take root in their gut sprouting out rod-like structures. These rods rapidly multiply before invading the blood and body tissues killing the larvae from the inside out. Due to its highly infectious nature, previously if a colony was to become infected it had to be burnt or buried deeply. What is the solution? Fortunately, the world’s first vaccine to combat AFB was approved for US usage on the 4th of January 2023. The mechanism of the vaccine is very interesting. The dead Paenibacillus bacteria is ingested orally in the Queen’s royal jelly before she is introduced into the hive. The vaccine contents are then transferred to the bee’s fat body for storage. The vitellogenin (which are yolk proteins) binds to pieces of the vaccine and delivers these specific immune elicitors to the Queen bee’s eggs in the ovaries. The developing larvae are now vaccinated and are more immune to the infection as they hatch. However, the exact mechanism of how the immune elicitors can enter the insect eggs is still unknown. Whilst this vaccine is not a cure, it has decreased the risk of AFB by 30-50%. Why do I find the vaccine so interesting? I personally find this vaccine fascinating as it was previously thought that insects do not possess any kind of long-lasting immunity, in the last decade scientists discovered features in the bee that could suggest a primitive immune system in the Queen bee. As well as this new research has shown that insects can genetically pass information from one generation to the next through a mechanism called Transgenerational Immune Priming. As bees do not produce traditional antibodies it was thought that a vaccine was not possible. Thanks to these recent discoveries, a vaccine to target one of the most aggressive apian diseases was created. What are the future applications of this technology? The vaccine not only contributes to enhancing colony health but also possesses medicinal properties, fostering the growth of commercial beekeeping through the production of items like medicinal honey and wax. There is optimism that it will open avenues for the development of other insect vaccines. Also, Dalan Animal Health, the company behind the AFB vaccine, is actively engaged in creating similar vaccines to address European Foulbrood (EFB). Currently in the pre-clinical phase is a combined AFB/EFB vaccine, while a vaccine for Chalkbrood is nearing the pre-clinical stage. When inquiring about the future prospects of the vaccine, Hichole Hoffman, the Operations Manager at Dalan Animal Health, stated, "Dalan is working to expand the pipeline into other honeybee diseases. This research has the potential to impact many invertebrates, such as mealworms, shrimp, and other insects. Our vaccine and platform technology are pioneering a new era in the insect health sector, revolutionizing how we care for invertebrates." References [1] Graham, Flora. “Daily Briefing: World’s First Vaccine for Bees.” Nature, 11 Jan. 2023, https://doi.org/10.1038/d41586-023-00058-5. [2] “How Does the World’s First Vaccine for Honeybees Work? “It’s like Magic.”” Www.cbsnews.com, www.cbsnews.com/news/first-vaccine-honeybees-its-like-magic/. [3] Magazine, Smithsonian, and Sarah Kuta. “The World’s First Vaccine for Honeybees Is Here.” Smithsonian Magazine, 9 Jan. 2023, www.smithsonianmag.com/smart-news/the-worlds-first-vaccine-for-honeybees-is-here-180981400/. [4] “Science — Dalan Animal Health.” Www.dalan.com, www.dalan.com/science.

Author: Himanshu Sadulwad Cancer is the second leading cause of deaths in the world next to cardiovascular diseases. Even more agonizing than the mortality rate is the physical and mental suffering associated with it. The question commonly asked is, 'Will there ever be a cure for cancer?' The answer to this simple question is difficult, because cancer is not one disease but many disorders that share a profound growth dysregulation. The only hope for containing this disease is to study its development and pathogenicity. Many model organisms have been incorporated to study these properties. While studying these organisms it was observed that certain organisms such as the naked mole rat, blind mole rat, certain bats, elephants and whales are resistant to cancer. Studying these organisms can help us understand the onset of the disease and the natural defense mechanisms of the body against these. Cancer and it's onset Cancer is characterized by loss of control of cellular growth and development leading to excessive proliferation and spread of cells. Characteristics of cancer cells Loss of contact inhibition: Normal cells are characterized by contact inhibition that is they form monolayers and cannot move away from each other. Cancer cells can form multiple layers. Metastasis: It refers to the spread of cancer cells from the primary site of origin to other tissues of the body where they produce secondaries. Loss of anchorage dependence Increased rate of replication and transcription Increased glycolysis Molecular basis It is caused by genetic changes in a single cell resulting in its uncontrolled multiplication. Oncogenes The genes capable of causing cancer are called oncogenes. Their sequences in a normal cell are termed as protooncogenes. Activation of a protooncogene to an oncogene Mechanisms include: Viral insertion into chromosome Chromosomal translocation Gene amplification Point mutation Factors causing oncogene activation Environmental factors Mutations Oncogenic viruses Inactivation of antioncogenes Defense mechanisms against cancer Different species require different number of mutations 'hits' that is inactivation of a specific gene for malignant transformation. Two hits are required for transformation of mouse fibroblasts, namely inactivation of either Trp53 or Rb1 and activation of Hras. In contrast, 5 hits are required to transform human fibroblasts: Inactivation of: TP53 (Tumor protein 53 or p53 is a tumor suppressor protein) RB1 (Retinoblastoma associated protein) PP2A (Protein phosphatase 2A) Constitutive activation of: Telomerase HRAS (Harvey rat sarcoma) The need for anticancer mechanisms This data suggests that humans have evolved more robust anticancer defense mechanisms than these mice. Evolutionary pressure to evolve anticancer mechanisms is very strong because an animal developing cancer prior to its reproductive age would leave no progeny. Thus, animals developed efficient anticancer mechanisms to delay the onset of tumors until post-reproductive age. Hence, cancer becomes more frequent in aged animals once they are no longer subject to natural selection. This implies that animals with a longer lifespan will develop more robust anticancer defenses which keep them cancer free until after their reproductive ages. Another factor influencing the risk of cancer is body size. Larger animals have more somatic cells and can accumulate more mutations, thus statistically increasing the risk of cancer development. To counteract this risk large-bodied species have evolved more efficient tumor suppressor mechanisms. Therefore, novel and more sophisticated anti-cancer strategies are found in long-lived and large-bodied mammals. The molecular mechanisms of cancer resistance are an area of interest for cancer research. These mechanisms have been evolutionarily selected over millions of years. Understanding these may hold the key to enhance cancer resistance in humans. General study of anticancer mechanisms in species Telomerase is a ribonucleoprotein that replicates the repetitive sequences at the ends of chromosomes, known as telomeres. It must be de-repressed to transform human cells. But it is constitutively active in the mouse. DNA polymerases cannot fully replicate chromosome ends, as they require an RNA primer to start. This is referred to as the ‘end replication problem'. Rebuilding chromosome ends is accomplished by telomerase, which carries its own RNA template. In most human somatic cells, expression of the protein component of telomerase TERT is silenced during embryonic differentiation. Due to this when cells divide, their telomeres shorten which eventually leads to replicative senescence. This is an important tumor suppressor mechanism which limits cell proliferation. Thus, mice are already a step closer to malignant transformation as they constitutively express telomerase. There is a defined mass threshold of 5,000 to 10,000 g after which telomerase activity is repressed in the majority of somatic cells. This shows that to counteract the statistical probability of developing tumors due to a larger body mass, these organisms evolved the mechanism of replicative senescence. It is also observed that larger and longer lived species require more hits for transformation as compared to smaller and shorter lived species. Small and shorter lived species require inactivation of Trp53 or Rb1 along with an activating mutation in Hras to form tumors. However, small species which have longer lifespans required both Trp53 and Rb1 to be inactivated. In contrast larger species require constitutive activation of telomerase along with the aforementioned changes to develop a tumor. Larger and longer lived species further required the inactivation of PP2A. This indicates that body mass and lifespan play a vital role in shaping the various tumor suppressor mechanisms. It can be argued that replicative senescence should have been evolutionarily selected in small animals to prevent tumor growth. This problem can be solved with the simple hypothesis that in small organisms, a benign tumor arising prior to short-telomere mediated growth arrest would be hazardous for a small organism. A 3g tumor greatly impedes the movements of a 30g mouse but would be inconsequential to a 50kg organism. Hence, small bodied, long lived organisms developed a mechanism which restricts cell proliferation early that is at the hyperplasia stage. There exist some animals who possess such robust anticancer mechanisms that they are almost cancer resistant. Let us study the defense mechanisms in some of them. Naked mole rat The naked mole rat (Heterocephalus glaber) is a mouse sized rodent that inhabits subterranean tunnels in East Africa. Due to a constant underground temperature it has no need for insulation and has lost its fur. It is the longest living rodent with a lifespan of 32 years in captivity. Out of thousands of these animals observed only 6 cases of neoplasms were reported which occurred due to exposure to greater light and temperature ranges. The naked mole rat is a small, long-lived mammal and hence does not rely on replicative senescence. Rather it relies on early acting, anti hyperplastic tumor suppressor mechanisms. Its arsenal of anticancer mechanisms include: Early contact inhibition It has a modified form of contact inhibition which is early acting and arrests cell proliferation at stages earlier to the formation of a dense monolayer. It is triggered by activation of p16INK4A rather than p27 which is the activator in humans. If the gene encoding p16INK4A which is Cdkn2aINK4A is silenced, normal contact inhibition occurs via p27. To completely nullify contact inhibition loss of both the genes Cdkn2aINK4A and Cdkn1b (which codes for p27) is required. Thus these rats have an increased level of protection. pALT The Cdkn2a-Cdkn2b is a locus that contains key tumor suppressor genes. In humans it encodes cyclin dependent kinase (CDK) inhibitors p15INK4B, p16INK4A and a p53 activator protein ARF. However, in the naked mole rat due to alternative splicing, pALT is produced which acts as a potent CDK inhibitor. High molecular mass hyaluronan Hyaluronan is a linear glucosaminoglycan the major non-protein component of the extracellular matrix. Longer molecules of hyaluronan have anti-proliferative, anti-inflammatory and anti-metastatic properties. Naked mole rats have hyaluronan molecules 6 to 30 times longer than those in humans. This occurs due to two factors. The hyaluronan synthase 2 gene (Has2) has a unique sequence leading to higher production. The second is that hyaluronidases have reduced activity in their tissues. Inactivation of Tp53 and Rb1 The inactivation of these tumor suppressors causes apoptosis in naked mole rat cells as opposed to rapid proliferation which occurs in human cells. Similarly, inactivation of Cdkn2aARF, which reduces activity of p53 also triggers senescence in them. Additional mechanisms of cancer resistance in naked mole rat cells include high fidelity protein synthesis, more active antioxidant pathways and more active proteolysis. Blind mole rat The blind mole rat (Spalax ehrenbergi) has a lifespan of 21 years and is resistant to cancer. The modifications in this organism include Reduced p53 activity The strictly subterranean life of the blind mole rat resulted in its increased tolerance to hypoxia. To avoid hypoxia induced apoptosis, it has a modified Tp53 sequence which weakens p53. Concerted cell death It was observed that after 12-15 population doubling, the entire culture of blind mole rat cells dies within 3-4 days via a combination of necrotic and apoptotic processes. It is mediated by a massive release of INFß into the medium. This suggests that blind mole rat cells are acutely sensitive to hyperplasia. Production of HMM-HA The high molecular mass hyaluronan slows proliferation of tumor cells. Reduced activity of heparanase Heparanase is an endoglycosylase that degrades heparin sulphate on the cell surface and in the ECM. The blind mole rat expresses a splice variant of heparanase that acts as a dominant negative which inhibits matrix degradation. This along with abundant expression of HMM-HA results in a more structured ECM that restricts tumor growth and metastasis. Elephants and whales Peto's paradox In 1977, Peto noted that while humans have 1000 times more cells than a mouse and are much longer-lived, human cancer risk is not higher than that in the mouse. This observation was seemingly inconsistent with the multistage carcinogenesis model according to which individual cells become cancerous after accumulating a specific number of mutational hits. This contradiction became known as Peto’s paradox. An answer to Peto’s paradox is that different species do not need the same number of mutational hits. In other words, large-bodied and long-lived animal species have evolved additional tumor suppressor mechanisms to compensate for the increased numbers of cells. Furthermore, many large animals are also long-lived, hence they need additional protection from cancer over their lifespan. Anticancer mechanisms in elephants Elephants possess 19 extra copies of the TP53 gene. All the additional copies appear to be pseudogenes and contain deletions. Some of these are transcribed from neighboring transposable element derived promoters. Transcripts from two of the 19 TP53 pseudogenes are translated in elephant fibroblasts. However, all the additional copies of TP53 are missing DNA binding domains and the nuclear localization signal and, therefore, cannot function as transcription factors. Elephant cells have an enhanced p53-dependent DNA damage response leading to an increased induction of apoptosis, compared to smaller members of the same family, such as armadillo and aardvark. Although the precise mechanism of action of the novel forms of TP53 is not known, it was proposed that their protein products may act to stabilize the wild type p53 protein by binding to either the wild type p53 molecule itself or to its endogenous inhibitors, the MDM2 proteins. Anticancer mechanisms in whales Comparative genomic and transcriptomic studies in the bowhead whale identified genes under positive selection linked to cancer and aging, as well as bowhead whale-specific changes in gene expression, including genes involved in insulin signaling pathways. Notable examples of positively selected genes are excision repair cross complementation group 1 (ERCC1), which encodes a DNA repair protein and uncoupling protein 1 (UCP1), which encodes a mitochondrial protein of brown adipose tissue. In addition, these studies identified copy number gains and losses involving genes associated with cancer and aging, notably a duplication of proliferating cell nuclear antigen (PCNA). Since both ERCC1 and PCNA are involved in DNA repair, these proteins may protect from cancer by lowering mutation rates; thus whales may not need extra copies of TP53 because their cells do not accumulate cancer causing mutations and do not reach a pre-neoplastic stage. Slower metabolism of the largest mammals may lead to lower levels of cellular damage and mutations, and thus contribute to lower cancer incidence. Conclusion The reason for diversity in tumor suppressive mechanisms is that the need for more efficient anticancer defenses has arisen independently in different phylogenetic groups. As species evolved larger body mass and longer lifespan, depending on their ecology, the tumor suppressor mechanisms had to adjust to become more efficient. In each case, the ecology and unique requirements of individual species would determine the outcome. While the ultimate goal of cancer research is to develop safe and efficient anticancer therapies as well as preventative strategies, what can be learnt from tumor-prone models has its limitations. Mice simply do not possess anticancer mechanisms that humans do not already have. With regard to inherently cancer resistant species, the potential for improving the development of anticancer therapies is much greater. Anticancer adaptations that evolved in these species may be missing in humans and if introduced into human cells could result in increased cancer resistance. For example, humans did not evolve HMM-HA, as they do not lead a subterranean lifestyle; hence, activating similar mechanisms in humans may be beneficial. HA is a natural component of human bodies and is well tolerated. Therefore, identifying strategies to systemically upregulate HMM-HA in human bodies may serve in cancer prevention for predisposed individuals or as a cancer treatment. Nature is a treasure trove of resources and while we seek an ideal anticancer mechanism, the answer may already be out there. Understanding the molecular mechanisms of multiple anticancer adaptations that evolved in different species and then developing medicines reconstituting these mechanisms in humans could lead to new breakthroughs in cancer treatment and prevention. References Cleeland CS, et al. Reducing the toxicity of cancer therapy: recognizing needs, taking action. Nat Rev Clin Oncol. 2012;9:471–478. doi: 10.1038/nrclinonc.2012.99. Lipman R, Galecki A, Burke DT, Miller RA. Genetic loci that influence cause of death in a heterogeneous mouse stock. J Gerontol A Biol Sci Med Sci. 2004;59:977–983. Szymanska H, et al. Neoplastic and nonneoplastic lesions in aging mice of unique and common inbred strains contribution to modeling of human neoplastic diseases. Vet Pathol. 2014;51:663–679. doi: 10.1177/0300985813501334. Rangarajan A, Hong SJ, Gifford A, Weinberg RA. Species- and cell type-specific requirements for cellular transformation. Cancer Cell. Prowse KR, Greider CW. Developmental and tissue-specific regulation of mouse telomerase and telomere length. Proc Natl Acad Sci U S A. 1995;92:4818–4822. Keane M, et al. Insights into the evolution of longevity from the bowhead whale genome. Cell reports. 2015;10:112–122. doi: 10.1016/j.celrep.2014.12.008. Abegglen LM, et al. Potential Mechanisms for Cancer Resistance in Elephants and Comparative Cellular Response to DNA Damage in Humans. Jama. 2015;314:1850–1860. doi: 10.1001/jama.2015.13134. Nat Rev Cancer. 2018 Jul; 18(7): 433–441.PMC6015544

Author: Afreen Introduction: In the realm of graphical user interfaces (GUI), 's Tkinter library provides a versatile toolkit for creating interactive applications. When combined with the powerful image processing capabilities of the Pillow library, you can easily develop a simple yet effective image viewer. In this article, we will explore a script that utilizes Tkinter and Pillow to create an interactive image viewer with navigation buttons and a slideshow feature. The highlighted portions contain the codes and corresponding explanations either above or below them. To code along with the project, you can go to the following GitHub repository link: https://github.com/AfreenInnovates/image-slider Setting Up the Environment: Before diving into the code, ensure you have Tkinter and Pillow installed in your environment. You can install them using the following commands: pip install tk pip install Pillow Understanding the Code: Now, let's break down the code step by step: 1. Importing Libraries: The script begins by importing the necessary libraries: from tkinter import * from PIL import ImageTk, Image Tkinter is employed for creating the graphical user interface, while Pillow handles the loading and processing of images. 2. Creating the Tkinter Window: root = Tk() # Any name can be used instead of root. This line initializes the main Tkinter window, serving as the container for the graphical elements. 3. Loading and Storing Images: The script loads a set of images and stores them in a list: my_img_1 = ImageTk.PhotoImage(Image.open("images/d1.jpg")) my_img_2 = ImageTk.PhotoImage(Image.open("images/d2.jpg")) # ... Repeat for other images. Here, d1.jpg and so on are images in the folder “images”. If you just want to access one image, then just type: my_img_1 = ImageTk.PhotoImage(Image.open("d1.jpg")) # Check the repo link to understand. # Storing all images in a list (same as array). my_images = [my_img_1, my_img_2, my_img_3, my_img_4, my_img_5] # The images are converted into Tkinter PhotoImage objects and organized into a list named `my_images`. 4. Displaying the first image: my_label_1 = Label(root, image=my_img_1) my_label_1.grid(row=0, column=0, columnspan=3) # This code creates a Tkinter Label widget (`my_label_1`) to display the first image. The label is positioned on the grid in the first row, spanning three columns. 6. Functions for handling buttons: def btn_forw(): global img_num # If the current image is the first, disable the previous button if img_num == 1: prev_btn.config(state=DISABLED) # Increment the image number img_num += 1 # Check if we have reached the end of the image list if img_num > len(my_images): img_num = 1 # Wrap around to the first image # Update the display update_display() # Enable or disable navigation buttons based on the current image number prev_btn.config(state=NORMAL if img_num > 1 else DISABLED) forw_btn.config(state=NORMAL if img_num < len(my_images) else DISABLED) The function uses the global keyword to access and modify the global variable img_num. It first checks if the current image is the first one. If so, it disables the previous button (prev_btn) since there's no previous image. The image number is then incremented, and the function checks if it has reached the end of the image list. If so, it wraps around to the first image for a continuous loop. The update_display() function is called to refresh the displayed image. Finally, the state of the previous and forward buttons is adjusted based on the current image number to enable or disable them accordingly. def btn_prev(): global img_num # If the current image is the last, disable the forward button if img_num == len(my_images): forw_btn.config(state=DISABLED) # Decrement the image number img_num -= 1 # Check if we have reached the start of the image list if img_num < 1: img_num = len(my_images) # Set to the last image # Update the display update_display() # Enable or disable navigation buttons based on the current image number prev_btn.config(state=NORMAL if img_num > 1 else DISABLED) forw_btn.config(state=NORMAL if img_num < len(my_images) else DISABLED) Similar to btn_forw(), this function uses the global keyword to access and modify the global variable img_num. It checks if the current image is the last one. If so, it disables the forward button (forw_btn) since there's no next image. The image number is decremented, and the function checks if it has reached the start of the image list. If so, it sets the image number to the last image for a continuous loop. The update_display() function is called to refresh the displayed image. Finally, the state of the previous and forward buttons is adjusted based on the current image number to enable or disable them accordingly. def update_display(): my_label_1.configure(image=my_images[img_num - 1]) img_num_label.config(text=f"Image {img_num}/{len(my_images)}") my_label_1 is updated with the image from my_images at the current index (img_num - 1). The text of img_num_label is updated to reflect the current image number out of the total number of images. 7. Navigation Buttons: prev_btn = Button(root, text="<<", command=btn_prev, state=DISABLED) exit_btn = Button(root, text="Exit app", command=root.quit) forw_btn = Button(root, text=">>", command=btn_forw) # Three buttons are created for navigation – moving backward, quitting the application, and moving forward through the images. 8. Grid Placement for Buttons: prev_btn.grid(row=1, column=0) exit_btn.grid(row=1, column=1) forw_btn.grid(row=1, column=2) # The navigation buttons are positioned on the grid in the second row. 9. Image Counter Label: img_num_label = Label(root, text=f"Image {img_num}/{len(my_images)}") img_num_label.grid(row=2, column=0, columnspan=3) # A label (`img_num_label`) is created to display the current image number out of the total number of images. It is placed in the third row, spanning three columns. 10. Slideshow Feature: def start_slideshow(): btn_forw() # Starts slideshow from the current image root.after(1000, start_slideshow) # Change image every 1000 milliseconds (1 second) start_slideshow_btn = Button(root, text="Start Slideshow", command=start_slideshow) start_slideshow_btn.grid(row=3, column=0, columnspan=3) # The script defines a function `start_slideshow` that automatically advances to the next image at regular intervals. The function is triggered by the "Start Slideshow" button. 11. Main Event Loop: root.mainloop() # This line initiates the main event loop of the Tkinter application, ensuring the graphical user interface remains responsive. Congratulations on creating an amazing project! Keep progressing and create even more :)

Author: Himanshu Sadulwad Prevention of any disease is largely based on the epidemiology of the disease. To effectively lower the number of COVID-19 cases globally, special emphasis has been placed on these epidemiological studies which help us to find the underlying causes of the surge in cases. Variants of Concern Like other viruses, COVID-19 does evolve over time. Most mutations in the SARS-CoV-2 genome have negligible impact on viral functions. But some variants have gained attention due to their rapid emergence, transmission and clinical implications. These are termed as variants of concern. Early in the pandemic, a study which monitored amino acid changes in the spike protein of SARS-CoV-2 identified a D614G (glycine for aspartic acid) substitution that became the dominant polymorphism globally over time. In animal and in vitro studies, viruses bearing the G614 polymorphism demonstrate higher levels of infectious virus in the respiratory tract, enhanced binding to ACE-2, and increased replication and transmissibility compared with the D614 polymorphism. The G614 variant does not appear to be associated with a higher risk of hospitalization or to impact anti-spike antibody binding. It is now present in most circulating SARS-CoV-2 lineages. Alpha (B.1.1.7 lineage) This variant was first reported in the UK in December 2020. It became the globally dominant variant until the emergence of the Delta variant. This variant includes 17 mutations in its viral genome. 8 of these are in spike proteins. This results in an increased affinity of the protein to ACE 2 receptors which enhances the viral attachment and subsequent entry into host cells. This variant was reported to be 43% to 82% more transmissible than the preexisting variants. It was also associated with an increased in mortality compared to other variants. Beta (B.1.315 lineage) The Beta variant was first identified in South Africa in October 2020. It includes 9 mutations in the spike proteins out of which 3 increase the binding to ACE receptors. The main concern with this variant was immune evasion as it had reduced neutralization by monoclonal antibody therapy, convalescent sera and post-vaccination sera. Gamma (P.1 lineage) The third variant of concern was identified in December 2020 in Brazil. It harbors 10 mutations in the spike proteins. It did not become a globally dominant variant. Delta (B.1.617.2 lineage) This variant was initially identified in December 2020 in India and was responsible for the deadly second wave of COVID-19 infections in April 2021. It harbors 10 mutations in its spike proteins. Compared to the Alpha variant, the Delta variant was more transmissible and associated with a higher risk of severe disease and hospitalization. Omicron (B.1.1.529 lineage) This variant was first identified by the WHO in South Africa on 23 November 2021 after a sharp rise in the number of COVID-19 cases. It was quickly recognized as a VOC due to more than 30 changes to the spike protein of the virus. Initial modeling suggests that it shows a 13-fold increase in viral infectivity and is 2.8 times more infectious than the Delta variant. It is also reported to evade infection and vaccine induced humoral immunity to a greater extent than prior variants. Immune evasion Omicron appears to escape humoral immunity and to be associated with a higher risk of reinfection in individuals previously infected with a different strain. These observations are further supported by findings from several laboratories, in which sera from individuals with prior infection or prior vaccination did not neutralize Omicron as well as other variants; in some cases, neutralizing activity against Omicron was undetectable in convalescent as well as post-vaccination sera. Severity of disease Observational data suggest that the risk of severe disease with Omicron infection is lower than with other variants. An analysis from England estimated that the risk of hospital admission or death with Omicron was approximately one-third that with Delta, adjusted for age, sex, vaccination status, and prior infection. The reduced risk for severe disease may reflect partial protection conferred by prior infection or vaccination. However, animal studies that show lower viral levels in lung tissue and milder clinical features (eg, less weight loss) with Omicron compared with other variants provide further support that Omicron infection may be intrinsically less severe. On the other hand, even if the individual risk for severe disease with Omicron is lower than with prior variants, the high number of associated cases can still result in high numbers of hospitalizations and excess burden on the health care system. Omicron Sublineages The original Omicron variant is sublineage BA.1. Sublineage BA.2, which differs by approximately 40 mutations, demonstrates a replication advantage compared with BA.1 and accounts for the majority of Omicron sequences globally. Vaccine efficacy appears largely similar for BA.2 in comparison to BA.1. Although reinfections with BA.2 in individuals with prior BA.1 infection occur, they have been rare and mainly in unvaccinated individuals. Accordingly, BA.1 infection in vaccinated individuals appears to elicit neutralizing antibodies that have potent activity against BA.2 as well. Other variants within the Omicron lineage include recombinant variants (eg, XE, which is a combination of BA.1 and BA.2) and new sublineages (eg, BA.2.12.1, BA.4, BA.5). Some of these appear to have a replication advantage compared with other Omicron sublineages; however it is unknown whether their impact on disease severity or immune escape differs from that of other Omicron sublineages. Variants of Interest VOIs are defined as variants with specific genetic markers that have been associated with changes that may cause enhanced transmissibility or virulence, reduction in neutralization by antibodies obtained through natural infection or vaccination, the ability to evade detection, or a decrease in the effectiveness of therapeutics or vaccination. So far since the beginning of the pandemic, the WHO has described eight variants of interest (VOIs), namely Epsilon (B.1.427 and B.1.429) Zeta (P.2) Eta(B.1.525) Theta (P.3) Iota(B.1.526) Kappa(B.1.617.1) Lambda(C.37) Mu (B.1.621). Epidemiology Since the first cases of COVID-19 were reported in Wuhan, Hubei Province, China, in December 2019 and the subsequent declaration of COVID-19 as a global pandemic by the WHO in March 2020, this highly contagious infectious disease has spread to 223 countries with more than 281 million cases, and more than 5.4 million deaths reported globally. This reported case counts underestimate the burden of COVID-19, as only a fraction of acute infections are diagnosed and reported. Seroprevalence surveys in the United States and Europe have suggested that after accounting for potential false positives or negatives, the rate of prior exposure to SARS-CoV-2, as reflected by seropositivity, exceeds the incidence of reported cases by approximately 10-fold or more. Persons of all ages are at risk for infection and severe disease. However, patients aged ≥60 years and patients with underlying medical comorbidities such as obesity, cardiovascular disease, chronic kidney disease, diabetes, chronic lung disease, smoking, cancer, solid organ or hematopoietic stem cell transplant patients are at an increased risk of developing severe COVID-19 infection. In fact, the percentage of COVID-19 patients requiring hospitalization was six times higher in those with preexisting medical conditions than those without medical conditions. Notably, the percentage of patients who succumbed to this viral illness was 12 times higher in those with preexisting medical conditions than those without medical conditions. Data regarding the gender-based differences in COVID-19 suggests that male patients are at risk of developing severe illness and increased mortality due to COVID-19 compared to female patients. Similarly, the severity of infection and mortality related to COVID-19 differs between different ethnic groups. Will it ever end? Since December 2019, this virus has been wreaking havoc around the globe disrupting all walks of life. While our healthcare systems work tirelessly to provide treatment, researchers have stepped up to help solve the situation. Although IHME models suggest that global daily SARS-CoV-2 infections have increased by more than 30 times from the end of November, 2021 to Jan 17, 2022, reported COVID-19 cases in this period have only increased by six times because the proportion of cases that are asymptomatic or mild has increased compared with previous SARS-CoV-2 variants, the global infection-detection rate has declined globally from 20% to 5%. Despite the reduced disease severity per infection, the massive wave of omicron infections means that hospital admissions are increasing in many countries and will rise to twice or more the number of COVID-19 hospital admissions of past surges in some countries according to the IHME models. So the question stands, when will this be over? Pandemics do not end overnight with a parade or some armistice. Usually, the virus evolves to a less severe variety, the majority of the population develop resistance to it and the disease fades into the background. If this happens the era of extraordinary measures by governments to control the transmission of the disease will be over. The pandemic may end but COVID-19 will return. References Giovanetti M, Benedetti F, Campisi G, Ciccozzi A, Fabris S, Ceccarelli G, Tambone V, Caruso A, Angeletti S, Zella D, Ciccozzi M. Evolution patterns of SARS-CoV-2: Snapshot on its genome variants. Biochem Biophys Res Commun. 2021 Jan 29;538:88-91. Korber B, Fischer WM, Gnanakaran S, Yoon H, Theiler J, Abfalterer W, Hengartner N, Giorgi EE, Bhattacharya T, Foley B, Hastie KM, Parker MD, Partridge DG, Evans CM, Freeman TM, de Silva TI, Sheffield COVID-19 Genomics Group. McDanal C, Perez LG, Tang H, Moon-Walker A, Whelan SP, LaBranche CC, Saphire EO, Montefiori DC. Tracking Changes in SARS-CoV-2 Spike: Evidence that D614G Increases Infectivity of the COVID-19 Virus. Cell. 2020 Aug 20;182(4):812-827.e19. Volz E, Mishra S, Chand M, Barrett JC, Johnson R, Geidelberg L, Hinsley WR, Laydon DJ, Dabrera G, O'Toole Á, Amato R, Ragonnet-Cronin M, Harrison I, Jackson B, Ariani CV, Boyd O, Loman NJ, McCrone JT, Gonçalves S, Jorgensen D, Myers R, Hill V, Jackson DK, Gaythorpe K, Groves N, Sillitoe J, Kwiatkowski DP, COVID-19 Genomics UK (COG-UK) consortium. Flaxman S, Ratmann O, Bhatt S, Hopkins S, Gandy A, Rambaut A, Ferguson NM. Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England. Nature. 2021 May;593(7858):266-269 Aleem A, Akbar Samad AB, Slenker AK. Emerging Variants of SARS-CoV-2 And Novel Therapeutics Against Coronavirus (COVID-19) [Updated 2022 May 12]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 Jan-. https://doi.org/10.1016/S0140-6736(22)00100-3 Hale, T. et al. Nat. Hum. Behav. 5, 529–538 (2021). (This is the third article in the series, Pandemic at a perspective)

Author: Himanshu Sadulwad If one gets infected with COVID-19, not all hope is lost. There are treatment options available to treat the disease based on the severity and the strain which has infected the individual. Stages of the disease Before listing the options available for treatment let us first take a look at the different stages through which this disease progresses. The National Institutes of Health (NIH) issued guidelines that classify COVID-19 into five distinct types. Asymptomatic or presymptomatic infection: Individuals with positive SARS-CoV-2 test without any clinical symptoms consistent with COVID-19 Mild illness: Individuals who have any symptoms of COVID-19 such as fever, cough, sore throat, malaise, headache, muscle pain, nausea, vomiting, diarrhea, anosmia (loss of smell), or dysgeusia (altered taste) but without shortness of breath or abnormal chest imaging. Moderate illness: Individuals who have clinical symptoms or radiologic evidence of lower respiratory tract disease and who have oxygen saturation (SpO2) ≥ 94% on room air. Severe illness: Individuals who have (SpO2) ≤ 94% on room air; a ratio of partial pressure of arterial oxygen to fraction of inspired oxygen, (PaO2/FiO2) <300 with marked tachypnea with respiratory frequency >30 breaths/min or lung infiltrates >50%. Critical illness: Individuals who have acute respiratory failure, septic shock, and/or multiple organ dysfunction. Patients with severe COVID-19 illness may become critically ill with the development of acute respiratory distress syndrome (ARDS) which tends to occur approximately one week after the onset of symptoms. Medicines to treat COVID-19 Currently, a variety of therapeutic options are available that include antiviral drugs, anti-SARS-CoV-2 monoclonal antibodies, anti-inflammatory drugs, immunomodulators agents are available under FDA issued Emergency Use Authorization( EUA) or being evaluated in the management of COVID-19. The clinical utility of these treatments is specific and is based on the severity of illness or certain risk factors. The clinical course of the COVID-19 illness occurs in 2 phases, an early phase when SARS-CoV-2 replication is greatest before or soon after the onset of symptoms. Antiviral medications and antibody-based treatments are likely to be more effective during this stage of viral replication. The later phase of the illness is driven by a hyperinflammatory state induced by the release of cytokines and the coagulation system’s activation that causes a prothrombotic state. Anti-inflammatory drugs such as corticosteroids, immunomodulating therapies, or a combination of these therapies may help combat this hyperinflammatory state than antiviral therapies. Antiviral therapies: Molnupiravir Named after the Norse God Thor's hammer Mjölnir, molnupiravir is a directly acting broad-spectrum oral antiviral agent acting on the RdRp enzyme was initially developed as a possible antiviral treatment for influenza, alphaviruses including Eastern, Western, and Venezuelan equine encephalitic viruses. Based on meta-analysis of available phase 1-3 studies, molnupiravir was noted to demonstrate a significant reduction in hospitalization and death in mild COVID-19 disease. Results from a phase 3 double-blind randomized placebo controlled trial reported that early treatment with molnupiravir reduced the risk of hospitalization or death in at-risk unvaccinated adults with mild-to-moderate, laboratory-confirmed Covid-19. Paxlovid It consists of ritonavir in combination with nirmatrelvir. It is an oral combination pill of two antiviral agents which on an interim analysis of phase 2-3 data, found that the risk of COVID-19 related hospital admission or all-cause mortality was 89% lower in the paxlovid group when compared to placebo when started within three days of symptom onset. FDA approved Paxlovid for patients with mild to moderate disease. Remdesivir It is a broad-spectrum antiviral agent that previously demonstrated antiviral activity against SARS-CoV-2 in vitro.Based on results from three randomized, controlled clinical trials that showed that remdesivir was superior to placebo in shortening the time to recovery in adults who were hospitalized with mild-to-severe COVID-19, the FDA approved remdesivir for clinical use in adults and pediatric patients. However, results from the WHO SOLIDARITY Trial conducted at 405 hospitals spanning across 40 countries involving 11,330 inpatients with COVID-19 who were randomized to receive remdesivir (2750) or no drug (4088) found that remdesivir had little or no effect on overall mortality, initiation of mechanical ventilation, and length of hospital stay. There is no data available regarding the efficacy of remdesivir against the new SARS-CoV-2 variants; however, acquired resistance against mutant viruses is a potential concern. Hydroxychloroquine and chloroquine These were proposed as antiviral treatments for COVID-19 initially during the pandemic. However, data from randomized control trials evaluating the use of hydroxychloroquine with or without azithromycin in hospitalized patients did not improve the clinical status or overall mortality compared to placebo. Data from randomized control trials of hydroxychloroquine used as postexposure prophylaxis did NOT prevent SARS-CoV-2 infection or symptomatic COVID-19 illness. Lopinavir/Ritonavir It is an FDA-approved combo therapy for the treatment of HIV and was proposed as antiviral therapy against COVID-19 during the early onset of the pandemic. Data from a randomized control trial that reported NO BENEFIT was observed with lopinavir-ritonavir treatment compared to standard of care in patients hospitalized with severe COVID-19. It is currently not indicated for the treatment of COVID-19 in hospitalized and nonhospitalized patients. Ivermectin It is an FDA-approved anti-parasitic drug used worldwide in the treatment of COVID-19 based on an in vitro study that showed inhibition of SARS-CoV-2 replication. A single-center double-blind, randomized control trial involving 476 adult patients with mild COVID-19 illness was randomized to receive ivermectin 300 mcg/kg body weight for five days or placebo did NOT achieve significant improvement or resolution of symptoms. Ivermectin is currently not indicated for the treatment of COVID-19 in hospitalized and nonhospitalized patients. Anti-SARS-CoV-2 Neutralizing Antibody Products: Individuals recovering from COVID-19 develop neutralizing antibodies against SARS-CoV-2, and the duration of how long this immunity lasts is unclear. Nevertheless, their role as therapeutic agents in the management of COVID-19 is extensively being pursued in ongoing clinical trials. Convalescent Plasma therapy This therapy was evaluated during the SARS, MERS, and Ebola epidemics; however, it lacked randomized control trials to back its actual efficacy. The FDA approved convalescent plasma therapy under a EUA for patients with severe life-threatening COVID-19. Although it appeared promising, data from multiple studies evaluating the use of convalescent plasma in life-threatening COVID-19 has generated mixed results. REGN-COV2 It is an antibody cocktail containing two noncompeting IgG1 antibodies (casirivimab and imdevimab) that target the RBD on the SARS-CoV-2 spike protein that has been shown to decrease the viral load in vivo, preventing virus-induced pathological sequelae when administered prophylactically or therapeutically in non-human primates. Results from an interim analysis of 275 patients from an ongoing double-blinded trial involving non hospitalized patients with COVID-19 who were randomized to receive placebo, reported that the REGN-COV2 antibody cocktail reduced viral load compared to placebo. This interim analysis also established the safety profile of this cocktail antibody, similar to that of the placebo group. Bamlanivimab and Etesevimab These are potent anti-spike neutralizing monoclonal antibodies. Bamlanivimab is a neutralizing monoclonal antibody derived from convalescent plasma obtained from a patient with COVID-19. Like REGN-COV2, it also targets the RBD of the spike protein of SARS-CoV-2 and has been shown to neutralize SARS-CoV-2 and reduce viral replication in non-human primates. In Phase 2 of the BLAZE-1 trial, bamlanivimab/etesevimab was associated with a significant reduction in SARS-CoV-2 viral load compared to placebo Sotrovimab It is a potent anti-spike neutralizing monoclonal antibody that demonstrated in vitro activity against all the four VOCs. Results from a preplanned interim analysis(not yet peer-reviewed) of the multicenter, double-blind placebo-controlled Phase 3, demonstrated that one dose of sotrovimab (500 mg) reduced the risk of hospitalization or death by 85% in high-risk non hospitalized patients with mild to moderate COVID-19 compared with placebo. Immunomodulatory Agents: Corticosteroids Severe COVID-19 is associated with inflammation-related lung injury driven by the release of cytokines characterized by an elevation in inflammatory markers. The Randomized Evaluation of Covid-19 Therapy (RECOVERY) trial, which included hospitalized patients with clinically suspected or laboratory-confirmed SARS-CoV-2 who were randomly assigned to received dexamethasone or usual care showed that the use of dexamethasone resulted in lower 28-day mortality in patients who were on invasive mechanical ventilation or oxygen support but not in patients who were not receiving any respiratory support. Interferon-ß-1a Interferons are cytokines that are essential in mounting an immune response to a viral infection, and SARS-CoV-2 suppresses its release in vitro.[123] However, previous experience with IFN- β-1a in acute respiratory distress syndrome (ARDS) has not benefited. Currently, there is no data available regarding the efficacy of interferon β-1a on the four SARS-CoV-2 VOCs Alpha (B.1.1.7), Beta (B.1.351), Gamma(P1), and Delta (B.1.617.2). Given the insufficient and small amount of data regarding this agent’s use and the relative potential for toxicity, this therapy is NOT recommended to treat COVID-19 infection. Interleukin (IL)-1 Antagonists Anakinra is an interleukin-1 receptor antagonist that is FDA approved to treat rheumatoid arthritis. Its off-label use in severe COVID-19 was assessed in a small case-control study trial based on the rationale that the severe COVID-19 is driven by cytokine production, including interleukin (I.L.)-1β. This trial revealed that of the 52 patients who received anakinra and 44 patients who received standard of care, anakinra reduced the need for invasive mechanical ventilation and mortality in patients with severe COVID-19. Anti-IL-6 receptor Monoclonal Antibodies Interleukin-6 (IL-6) is a proinflammatory cytokine that is considered the key driver of the hyperinflammatory state associated with COVID-19. Targeting this cytokine with an IL-6 receptor inhibitor could slow down the process of inflammation based on case reports that showed favorable outcomes in patients with severe COVID-19. The FDA approved three different types of IL-6 receptor inhibitors for various rheumatological conditions (Tocilizumab, Sarilumab) and a rare disorder called Castleman’s syndrome (Siltuximab). Need for a vaccine The most fruitful way to prevent the spread of any disease would be to immunize the population towards the disease. This is where vaccines come into the picture. But to create a vaccine against a viral disease whose pathogen has the ability to mutate is quite tricky. Nonetheless multiple such efforts using various techniques have been successful and approved by the FDA for use. Let us take a look at them BNT162b2 vaccine (Tonizameran) This mRNA based vaccine has been developed by BioNTech/Pfizer. Individuals 16 years of age or older receiving two-dose regimen the vaccine when given 21 days apart conferred 95% protection against COVID-19 with a safety profile similar to other viral vaccines. Based on the results of this vaccine efficacy trial, the FDA issued a EUA on December 11, 2020, granting the use of the BNT162b2 vaccine to prevent COVID-19. mRNA-1273 vaccine This mRNA based vaccine has been developed by Moderna. Results from another multicenter, Phase 3, randomized, observer-blinded, placebo-controlled trial demonstrated that individuals who were randomized to receive two doses of the vaccine given 28 days apart showed 94.1% efficacy at preventing COVID-19 illness and no safety concerns were noted besides transient local and systemic reactions. Based on the results of this vaccine efficacy trial, the FDA issued a EUA on December 18, 2020, granting the use of the mRNA-1273 vaccine to prevent COVID-19. Ad26.COV2.S vaccine (Johnson and Johnson vaccine) It received EUA by the FDA on February 27, 2021, based on the results of an international multicenter, randomized,placebo-controlled multicenter, phase 3 trial showed that a single dose of Ad26.COV2.S vaccine conferred 73.1% efficacy in preventing COVID-19 in adult participants who were randomized to receive the vaccine. ChAdOx1 nCoV-19 vaccine (Covishield) It has a clinical efficacy of 70.4% against symptomatic COVID-19 after two doses and 64 % protection against COVID-19 after at least one standard dose. This too has been approved by multiple countries for use. In addition to the vaccines mentioned above, as many as seven other vaccines, including protein-based and inactivated vaccines, have been developed indigenously in India(Covaxin), Russia(Sputnik V), and China(CoronaVac) and have been approved or granted emergency use authorization to prevent COVID-19 in many countries around the world. References Cascella M, Rajnik M, Aleem A, et al. Features, Evaluation, and Treatment of Coronavirus (COVID-19) [Updated 2022 Jan 5]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 Jan- Copyright © 2021 Rodriguez-Guerra M, Jadhav P, Vittorio TJ. https://doi.org/10.7573/dic.2020-10-3. Published by Drugs in Context under Creative Commons License Deed CC BY NC ND 4.0. Stasi, Cristina et al. “Treatment for COVID-19: An overview.” European journal of pharmacology vol. 889 (2020): 173644. doi:10.1016/j.ejphar.2020.173644 Wu K, Werner AP, Moliva JI, Koch M, Choi A, Stewart-Jones GBE, Bennett H, Boyoglu-Barnum S, Shi W, Graham BS, Carfi A, Corbett KS, Seder RA, Edwards DK. mRNA-1273 vaccine induces neutralizing antibodies against spike mutants from global SARS-CoV-2 variants. bioRxiv. 2021 Jan 25 ( This article is second in the series of articles 'Pandemic at a perspective')

Author: Himanshu Sadulwad 'On 31 December 2019, the WHO China Country Office was informed of a pneumonia of unknown cause, detected in the city of Wuhan in Hubei province, China.' No human would have imagined that these cases would lead to a full blown global pandemic. As the pandemic now enters its third year let's take a look back at the steps which led to this disaster. Origin Following the initial report of these pneumonia cases in China, on 4th January 2020 WHO responded to the cluster of pneumonia cases to track the situation and provide information as it emerged. Following this, guidelines were released with reference to SARS and MERS and on 11th January 2020 the first case of this novel coronavirus outside China was confirmed in Thailand. Field visits were made to Wuhan to discuss the screening procedures at airports to limit the spread of the virus. The genome of the novel coronavirus was made publicly available on 11th January 2020. On 30th January 2020 WHO Director-General Dr. Tedros Ghebreyesus declared the novel coronavirus outbreak a Public Health Emergency of International Concern. On 11th February 2020 WHO announced that the disease caused by the novel coronavirus would be named COVID-19. The decision to not let the disease be named after a person or region was to prevent any stereotypes which may arise due to the name.The events that followed included release of guidelines by the WHO, multiple preventive measures and general information about the disease was released to educate the masses. This period also saw the spread of the disease outside China and a spike in the number of cases. "Pandemic is not a word to use lightly or carelessly. It is a word that, if misused, can cause unreasonable fear, or unjustified acceptance that the fight is over, leading to unnecessary suffering and death." On 11th March 2020 concerned by the alarming levels of spread and severity, and by the alarming levels of inaction, WHO officially declared that COVID-19 could be characterized as a pandemic. Following this massive announcement, countries stepped up their resolutions to combat the virus. Curfews and lockdowns were implemented to reduce its spread. Our major weapon to limit the spread of this disease was and remains to this day 'mask'. Let us evaluate how the use of a mask helped reduce cases and the stigma surrounding it. Masks Need for masks Among all plausible routes, airborne transmission of SARS-CoV-2 via respiratory droplets and aerosols is responsible for the rapid spread of COVID-19. Respiratory droplets, which have a relatively large size of 5–10 μm, are emitted when an infected individual coughs or sneezes. In comparison, aerosols are nuclei of respiratory droplets that form after evaporation, and are usually less than 5 μm in size. Viral particles have been found in respiratory droplets and exhaled aerosols of infected individuals, and are bound to inhalable aerosols in the atmosphere. Accumulating evidence suggests that respiratory droplets and aerosols expelled during a sneeze or cough can travel up to 12 to 26 feet, significantly farther than the 6-ft social distancing guideline recommended by the Centers for Disease Control and Prevention (CDC) of the United States. Due to their small size and hence negligible influence by gravity, aerosols generally remain afloat in the air for prolonged periods of time, causing an additional threat of airborne transmission, especially in indoor environments with poor ventilation. This is where the use of masks comes into the picture. This simple and low-cost method is highly effective at mitigating virus transmission. Reducing disease spread requires two things: limiting contacts of infected individuals via physical distancing and other measures and reducing the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces transmissibility per contact by reducing transmission of infected respiratory particles in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. By the end of June 2020, nearly 90% of the global population lived in regions that had nearly universal mask use, or had laws requiring mask use in some public locations, and community mask use was recommended by nearly all major public health bodies. This was a radical change from the early days of the pandemic, when masks were infrequently recommended or used. Efficacy of the usage of masks If there is strong direct evidence, either a suitably powered randomized controlled trial (RCT),a suitably powered metaanalysis of RCTs, or a systematic review of unbiased observational studies that finds compelling evidence, then that would be sufficient for evaluating the efficacy of public mask wearing, at least in the contexts studied. Cochrane and the World Health Organization both point out that, for population health measures, we should not generally expect to be able to find controlled trials, due to logistical and ethical reasons, and should therefore instead seek a wider evidence base. This issue has been identified for studying community use of masks for COVID-19 in particular. Therefore, we should not be surprised to find that there is no RCT for the impact of masks on community transmission of any respiratory infection in a pandemic. The Australian influenza RCT and the Beijing households observational trial found around 80% efficacy among compliant subjects, and the one SARS household study of sufficient power found 70% efficacy for protecting the wearer. A Cochrane review on physical interventions to interrupt or reduce the spread of respiratory viruses included 67 RCTs and observational studies. It found that “overall masks were the best performing intervention across populations, settings and threats.” Multiple studies offer evidence in favor of widespread mask use as source control to reduce community transmission. Nonmedical masks use materials that obstruct particles of the necessary size; people are most infectious in the initial period postinfection, where it is common to have few or no symptoms; nonmedical masks have been effective in reducing transmission of respiratory viruses; and places and time periods where mask usage is required or widespread have shown substantially lower community transmission. Models suggest that public mask wearing is most effective at reducing spread of the virus when compliance is high. The Swiss Cheese Model The Swiss cheese model of accident causation is used in risk analysis and risk management. This model was originally proposed by James Reason. The metaphor itself is easy enough to grasp; multiple layers of protection, imagined as cheese slices, block the spread of SARS CoV-2. No one layer is perfect; each has holes, and when the holes align, the risk of infection increases. But several layers combined — social distancing, masks, hand-washing, testing and tracing, ventilation, government messaging, vaccination — significantly reduce the overall risk. Note that in the figure the 'misinformation mouse' is nibbling holes which weakens the barriers, leaving us vulnerable. The main feature of a Swiss cheese model is that a single layer doesn't guarantee protection as it has its shortcomings and errors, 'holes'. In order to intensify our protection we need multiple such layers. This way we can minimize our risk of contracting the virus. References Reason, J. “Human error: models and management.” BMJ (Clinical research ed.) vol. 320,7237 (2000): 768-70. doi:10.1136/bmj.320.7237.768 Ju, Jerry T J et al. “Face masks against COVID-19: Standards, efficacy, testing and decontamination methods.” Advances in colloid and interface science vol. 292 (2021): 102435. doi:10.1016/j.cis.2021.102435 Howard, Jeremy et al. “An evidence review of face masks against COVID-19.” Proceedings of the National Academy of Sciences of the United States of America vol. 118,4 (2021): e2014564118. doi:10.1073/pnas.2014564118 Cowling B. J., et al. , Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: An observational study. Lancet Public Health 5, E279-E288 (2020) Higgins J. P., et al. , Cochrane Handbook for Systematic Reviews of Interventions (John Wiley, 2019) Wang Y., et al. , Reduction of secondary transmission of SARS-CoV-2 in households by face mask use, disinfection and social distancing: A cohort study in Beijing, China. BMJ Global Health 5, e002794 (2020)

Author: Katie Bean Introduction January marks cervical cancer awareness month, and it has never been more important to shed light on seventh most common type of cancer (Manikandan et al. 2019, 314). Mainly affecting women or anyone with a cervix under the age of 45, cervical cancer is usually caused by HPV (human papilloma virus) infections. Though this form of cancer can indeed be fatal, it is extremely treatable if caught in early stages through screening, and largely preventable owing to the development of HPV vaccines. Raising awareness for cervical cancer encourages screening and vaccination and also helps to promote research and implement these essential screening and vaccination programmes in lower income countries. Aetiology and Pathogenesis Most cases of cervical cancer result from infection with the HPV virus. There are two particular strains of HPV that are associated with cervical carcinoma: HPV-16 and HPV-18. The earliest transformation of healthy cells into cancerous occurs in a region called the squamocolumnar junction (see figure 1). The premalignant transformation of cells in this region is termed cervical intraepithelial neoplasia (CIN) - the severity of which is systematically graded (see figure 2). For carcinogenesis to occur, HPV viral proteins must alter the host cells. The oncoproteins most responsible for this are called E6 and E7 - they prevent the function of the p53 tumour suppressor gene, allowing uncontrolled cell proliferation. Once the cells have become cancerous, angiogenesis (blood vessel formation) can occur, allowing tumours to grow and metastasize - thus exacerbating the condition. Risk factors There are a number of risk factors that might increase the chance that an individual develops cervical cancer. Essentially all people that are sexually active will ultimately become infected with HPV in their lives. These short-term, less aggressive infections are not the cause of cervical cancer, rather it is the higher risk, previously mentioned strains that hold responsibility. Therefore, one of the most significant risk factors for developing cervical cancer is having multiple sexual partners, as it increases the chance that an individual may become infected with a high-risk strain. Individuals that are also at a higher risk are the immunocompromised; if someone has a weakened immune system, it is harder for them to fight off a more persistent HPV infection. Smoking is also a pertinent risk factor; it has been hypothesised that the by-products of tobacco can damage DNA within cervical cells and that smoking reduces the capability of the immune system to respond to HPV. (National Cancer Institute 2023) However, the precise molecular mechanisms by which this occurs are yet to be proven - more research is needed to do so. (Castle 2008, 6084) Long-term use of oral contraceptives is also associated with increased risk - the risk increases by a factor of 1.9 for every 5 years of oral contraceptive use. (Johnson et al. 2019, 166-174) Again, the molecular mechanisms behind this association is not well understood. (National Cancer Institute 2023) Preventative Measures In 2018, the WHO initiated a global call for action focussing on the elimination of cervical cancer. This was followed by the Global Strategy to Accelerate the Elimination of Cervical Cancer put forward by the World Health Assembly. The Strategy proposes three central goals to achieve this - vaccination, screening and treatment. More specifically, the Strategy aims to have 90% of girls fully vaccinated with the HPV vaccine by the age of 15 and 70% of women screened using a high-performance test by the age of 35, and again by the age of 45 (The Lancet Regional Health – Americas 2017). In the UK, the vaccine offered by the NHS is called Gardasil 9, and is recommended around 12-13 (before an individual becomes sexually active) (GOV.UK 2023). The vaccine works by action of virus-like particles (VLPs), which induce antibody production. Upon reinfection with the actual virus, these antibodies prevent the HPV from entering and infecting the basal cells of the epithelium. (Harper, Vierthaler, and Santee 2010, 2) One of the reasons that the HPV vaccine has been found to be highly effective is due to the strong immunogenic nature of the virus-like particles; they stimulate high levels of antibody production (National Cancer Institute 2021). Condoms (an example of a physical barrier method) are also able to reduce the risk of HPV transmission between individuals, though infection can still occur in areas of skin that are exposed during intercourse. (National Cancer Institute 2023) Screening for Cervical Cancer Since cervical cancer is such a preventable disease, it is imperative to emphasise the importance of available screening procedures; these ensure the detection of abnormal cells, thus preventing the development of cervical cancer. The traditional method of cervical screening is a Pap smear (named after the Greek physician Georgios Papanikolaou). The procedure, that lasts no longer than 5 minutes, involves the insertion of a speculum and soft brush into the cervix to take a sample of cells. This service is available to women and those with a cervix aged 25-64 in England (NHS 2024). The Pap smear allows for detection of HPV - if present, this can be followed by a cytology test to check for abnormal cells. If abnormal cells are detected, the patient is referred for a colposcopy (GOV.UK 2024). During a colposcopy, a colposcope (lighted microscope) enables the magnification of cervical tissue. (Cleveland Clinic 2022). If a precancerous lesion is identified, a ‘large loop excision of the transformation zone’ is performed - this removes the precancerous cells, inhibiting development. (Burmeister et al. 2022) Disease management Treatment for cervical cancer varies depending on the progression of the tumour, as well as the health and preference of the patient (for example, if they desire to preserve fertility). In early stages of the disease, surgical procedures are able to remove the carcinoma. Microinvasive disease can be treated with conisation (the removal of a ‘cone-shaped portion’ of the cervix) (Cooper et al. 2023), as well as a simple trachelectomy (removal of the cervix). (Circle Health Group 2024). The aforementioned procedures are fertility-sparing, though a simple hysterectomy can also be performed if the patient does not desire to preserve fertility (Marth et al. 2017, 76-77). larger tumours require radical hysterectomy with bilateral lymph node dissection (the latter is used to assess whether cancer cells have spread to the lymph nodes of the pelvis - this would increase the risk of metastasis) (Marth et al. 2017, 76). Radiotherapy and chemotherapy are employed in more advanced stages of the disease (e.g., when the cancer has metastasised) (Marth et al. 2017, 77). Radiotherapy and chemotherapy can also be used as adjuvant therapy (this is administered after treatment to reduce the chance of the cancer returning) (Mayo Clinic 2024). Chemotherapy is often used palliatively, with the aim of improving the quality of life for terminal patients (Johnson et al. 2019, 170). Raising Awareness Although HPV infection, and ultimately cervical cancer, is largely preventable, there are various socioeconomic factors that reduce the efficiency of screening. A study on cervical cancer in Central and South America concluded that poverty is the most significant factor in the incidence of and deaths from the disease globally. (Murillo et al. 2016, 5). Economic determinants are significant particularly regarding access to medical resources for screening. For example, developed countries tend to have a higher standard of experimental conditions, enabling higher sensitivity for Pap smear analysis (usually 80-90%). In areas that have less sophisticated conditions, the sensitivity is limited to around 30-40% (Zhang et al. 2020, 724). There are other barriers that reduce access to screening, such as cultural beliefs and anxiety around gynaecological examinations; these factors tend to be more prevalent in low and middle-income countries (The Lancet Regional Health – Americas 2017). A general lack of awareness is also a crucial barrier; a 2018 study conducted in India investigated 100 female students between the ages of 18 and 24 found that only 5 individuals were aware about causes and risk factors regarding cervical cancer. 96 individuals were unaware about Pap testing or the HPV vaccine (Manikandan et al. 2019, 318). However, in order to align with the WHO’s global call, programmes are being implemented to overcome this. For example, the HOPE project in Peru recruits women from disadvantaged communities to spread information about HPV to women aged 30 and 49 years old (HOPE PERU 2024). They additionally provide self-sampling tests to women and follow the tests up with analysis, collaborating with a network of expert gynaecologists (HOPE PERU 2024). The HOPE ladies were successfully able to increase awareness in their community, the participation rate increased to 94% owing to their efforts. The success of this programme could potentially lead to similar schemes being implemented in other countries (The Lancet Regional Health – Americas 2017). Developments in Screening and Treatment As previously mentioned, there are a number of socioeconomic factors that act as barriers to cervical cancer screening. One particular manifestation of this is reduced sensitivity of Pap smear analysis, though an alternative method, liquid-based cytology is able to overcome this limitation. Its higher sensitivity enables cervical abnormalities to be detected despite less sophisticated experimental conditions (Zhang et al. 2020, 724). Regarding treatment, immunotherapy is fast becoming a useful method for managing cervical cancer. It focuses on targeting ‘checkpoints’ which can be turned on or off to stimulate an immune response; cancer cells often manipulate these to evade immune recognition. Drugs that inhibit these checkpoints are therefore able to treat cervical cancer (Johnson et al. 2019, 170). Pembrolizumab is an example of a drug that targets a checkpoint, the checkpoint in question being that between the two proteins PD-1 and PD-L1. Some tumours express protein PD-L1 at a high level, and when PD-L1 upon interaction with PD-1, it prevents T-cells from destroying the cancerous cells. Pembrolizumab works by inhibiting this interaction, enabling T-cells to efficiently destroy the tumour cells (Flynn and Gerriets 2023). In an article written in March 2023, the Royal Marsden announced pembrolizumab as the first approved immunotherapy treatment for cervical cancer provided by the NHS (under the name of Keytruda). It is administered in combination with chemotherapy to patients suffering with advanced-stage cervical cancer, where the cancer has not responded to previous treatments (The Royal Marsden 2023). Conclusion Overall, cervical cancer month functions as an essential reminder of the importance of raising awareness for one of the most common cancers globally. Measures such as screening and vaccination allow the detection and monitoring of HPV infections, though barriers to this prove to be hugely prevalent in countries with lower income. By implementing programmes such as the HOPE project in Peru and investing in research into more comprehensive analysis techniques and treatment, the risk of cervical cancer can be significantly reduced. There is still a long way to go in the journey towards cervical cancer elimination, but building on these advances could ensure a future free of cervical cancer. References Burmeister, Carly A., Saif F. Khan, Georgia Schaefer, Nomonde Mbatani, Tracey Adams, Jennifer Moodley, and Sharon Prince. 2022. “Cervical cancer therapies: Current challenges and future perspectives.” Tumour Virus Research 13, no. 13 (June): 13. 10.1016/j.tvr.2022.200238. Castle, Philip E. 2008. “How Does Tobacco Smoke Contribute to Cervical Carcinogenesis?” Journal of Virology 82, no. 12 (June): 6084–6086. 10.1128/JVI.00103-08. Circle Health Group. 2024. “Fertility Sparing Treatment (Trachelectomy) for Cervical Cancer.” Circle Health Group. https://www.circlehealthgroup.co.uk/treatments/cervical-cancer-fertility-sparing-treatment. Cleveland Clinic. 2022. “Colposcopy: Biopsy, Purpose, Procedure, Risk & Results.” Cleveland Clinic. https://my.clevelandclinic.org/health/diagnostics/4044-colposcopy. Cooper, Danielle B., Jose Carungno, Charles J. Dunton, and Gary W. Menefee. 2023. “Cold Knife Conization of the Cervix - StatPearls.” NCBI. https://www.ncbi.nlm.nih.gov/books/NBK441845/. Flynn, James P., and Valerie Gerriets. 2023. “Pembrolizumab - StatPearls.” NCBI. https://www.ncbi.nlm.nih.gov/books/NBK546616/. GOV.UK. 2023. “Information on the HPV vaccination from September 2023.” GOV.UK. https://www.gov.uk/government/publications/hpv-vaccine-vaccination-guide-leaflet/information-on-the-hpv-vaccination-from-september-2023. GOV.UK. 2024. “Cervical screening: programme overview.” GOV.UK. https://www.gov.uk/guidance/cervical-screening-programme-overview. Harper, Diane M., Stephen L. Vierthaler, and Jennifer A. Santee. 2010. “Review of Gardasil.” J Vaccines Vaccin 1, no. 107 (November): 107. 10.4172/2157-7560.1000107. HOPE PERU. 2024. “THE PROJECT – HOPE PERU.” HOPE PERU. https://hopeperuproject.org/project/. Johnson, Cynae A., Deepthi James, Amelita Marzan, and Mona Armaos. 2019. “Cervical Cancer: An Overview of Pathophysiology and Management.” Seminars in Oncology Nursing 35, no. 2 (April): 166-174. https://doi.org/10.1016/j.soncn.2019.02.003. The Lancet Regional Health – Americas. 2017. YouTube: Home. https://www.sciencedirect.com/science/article/pii/S2667193X22000783. Manikandan, Saranya, Subasish Behera, Nageswarao M. Naidu, Vignesswary Angamuthu, Omar Mohammed, and Abhitosh Debata. 2019. “Knowledge and Awareness Toward Cervical Cancer Screening and Prevention Among the Professional College Female Students.” J Pharm Bioallied Sci 2019 May, no. 11 (May): 314-320. 10.4103/JPBS.JPBS_21_19. Marth, C., F. Landoni, S. Manher, A. Gonzalez-Martin, and N. Columbo. 2017. “Cervical cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up.” Annals of Oncology 28, no. 4 (July): 72-83. https://doi.org/10.1093/annonc/mdx220. Mayo Clinic. 2024. “Adjuvant therapy: Treatment to keep cancer from returning.” Mayo Clinic. https://www.mayoclinic.org/diseases-conditions/cancer/in-depth/adjuvant-therapy/art-20046687. Murillo, R., R. Herrero, M. S. Sierra, and D. Forman. 2016. “Etiology of cervical cancer (C53) in Central and South America.” International Agency for Research on Cancer. National Cancer Institute. 2021. “Human Papillomavirus (HPV) Vaccines - NCI.” National Cancer Institute. https://www.cancer.gov/about-cancer/causes-prevention/risk/infectious-agents/hpv-vaccine-fact-sheet. National Cancer Institute. 2023. “Cervical Cancer Causes, Risk Factors, and Prevention.” National Cancer Institute. https://www.cancer.gov/types/cervical/causes-risk-prevention. NHS. 2024. “Cervical screening.” NHS. https://www.nhs.uk/conditions/cervical-screening/. The Royal Marsden. 2023. “Immunotherapy available to NHS cervical cancer patients for the first time.” The Royal Marsden. https://www.royalmarsden.nhs.uk/news-and-events/news/immunotherapy-available-nhs-cervical-cancer-patients-first-time. Zhang, Shaokai, Huifang Xu, Luyao Zhang, and Youlin Qiao. 2020. “Cervical cancer: Epidemiology, risk factors and screening.” Chinese Journal of Cancer Research 32, no. 6 (December): 720-728. 10.21147/j.issn.1000-9604.2020.06.05.

Author : Himanshu Sadulwad Synopsis An estimated 37.7 million people are currently living with HIV. As of now, it has claimed 36.3 million lives. A disease with one of the highest death tolls in the world, we speak of none other than HIV-AIDS. AIDS remains a major public health issue that affects millions worldwide. On this World AIDS day, let us educate ourselves a bit more on this crucial topic. Origin In humans, AIDS is caused by two lentiviruses namely, Human Immunodeficiency Viruses types 1 and 2 (HIV-1 and HIV-2). In this section, we describe the origins of these viruses and the circumstances that lead to the AIDS pandemic. Both HIVs are the result of multiple cross-species transmissions of simian immunodeficiency viruses (SIVs) naturally infecting African primates. Most of these transfers resulted in viruses that spread in humans to only a limited extent. However, one transmission event, involving SIVcpz from chimpanzees in southeastern Cameroon gave rise to HIV-1 group M which is the principal cause of the AIDS pandemic ( Paul Sharp and Beatrice Hahn 2011). How humans acquired the ape precursors of HIV-1 groups M, N, O, and P is not known; however, based on the biology of these viruses, the transmission must have occurred through cutaneous or mucous membrane exposure to infected ape blood or body fluids. Such exposures occur most commonly in the context of bushmeat hunting (Peeters et. al. 2002). Whatever the circumstances it seems clear that human-ape encounters in west-central Africa have resulted in four independent cross-species transmission events. Since its first discovery, HIV-2 has remained largely restricted to West Africa, with the highest prevalence rates in Guinea-Bissau and Senegal ( de Silva et al. 2008). Most individuals infected with HIV-2 do not progress to AIDS, although those who do, show clinical symptoms indistinguishable from HIV-1 ( Rowland-Jones and Whittle 2007). A sooty mangabey origin of HIV-2 was first proposed in 1989 (Hirsch et al. 1989) and subsequently confirmed by demonstrating that humans in West Africa harbored HIV-2 strains that resembled locally circulating SIVsmm infections (Gao et al. 1992; Chen et al. 1996). The fact that sooty mangabeys are frequently hunted as agricultural pests in many areas of West Africa provided plausible routes of transmission. Virology The human immunodeficiency virus (HIV) is grouped to the genus Lentivirus within the family of Retroviridae. On the basis of genetic characteristics and differences in the viral antigens, HIV is classified into the types 1 and 2 (HIV-1, HIV-2). Epidemiologic and phylogenetic analyses currently available imply that HIV was introduced into the human population around 1920 to 1940. HIV Genome structure The HIV genome consists of two identical single-stranded RNA molecules that are enclosed within the core of the virus particle. The genome of the HIV provirus, also known as proviral DNA, is generated by the reverse transcription of the viral RNA genome into DNA, degradation of the RNA and integration of the double-stranded HIV DNA into the human genome. The DNA genome is flanked at both ends by LTR (long terminal repeat) sequences. In the direction 5′ to 3′ the reading frame of the gag gene follows, encoding the proteins of the outer core membrane, the capsid protein, the nucleocapsid and a smaller, nucleic acid-stabilising protein.The gag reading frame is followed by the pol reading frame coding for the enzymes protease, reverse transcriptase, RNase H and integrase . Adjacent to the pol gene, the env reading frame follows from which the two envelope glycoproteins gp120 (surface protein) and gp41 (transmembrane protein) are derived. Particle structure The mature HIV particle is round, measures approximately 100 nm in diameter, with an outer lipid membrane as its envelope.The viral envelope is composed of a lipid bi-layer and, in mature virus particles, the envelope proteins SU and TM. The conical capsid is assembled from the inner capsid protein p24.Two identical molecules of viral genomic RNA are located inside the capsid and several molecules of the viral enzymes RT/RNase H are bound to the nucleic acid. Infection of human cells The surface glycoprotein gp120 of the mature HIV particle binds to the CD4 receptor on the host cell. All CD4-positive cells such as T helper cells, macrophages, dendritic cells and astrocytes are susceptible to HIV. After attachment to the CD4 molecule via the C4-domain of gp120, a conformational change in CD4 and gp120 occurs, opening up an additional site for gp120 to enable binding to the co-receptor, i.e. chemokine receptor 5 (CCR5) or chemokine receptor 4 (CXCR4) on the cell surface. Fusion of the viral and cellular membranes leads to translocation of the viral capsid into the cytoplasm. The capsid is taken up by an endosome, and a change in the pH value in the phagosome induces the release of the capsid contents into the cytoplasm. Activation of reverse transcriptase takes place in the cytoplasm. HIV RT transcribes the single-strand HIV RNA genome into DNA (complementary DNA or cDNA). In parallel to DNA synthesis, the RNA strand is degraded enzymatically by RNase H, followed by conversion of single-stranded cDNA into double-stranded DNA (proviral DNA) by the DNA-dependent DNA polymerase activity of RT. This proviral DNA is transported via nucleopores into the cell nucleus in the form of a complex consisting of the integrase and linear or circular proviral DNA. The integrase then inserts at random the proviral genome into the human host cell genome. Integration of the proviral DNA finalizes the HIV infection of the cell and the establishment of a persistent infection. Epidemiology According to present knowledge, the spread of HIV started at the beginning of the 20th century. Zoonotic transmission of SIVcpz from chimpanzees (Pan troglodytes) occurred for HIV-1 group M and group O around 1920 and HIV-1 group N around 1960 in West Central Africa. HIV-2 was transmitted zoonotically from Sooty Mangabey (Cercocebus atys) to humans in West Africa around 1940. Molecular genetic analyses suggest that HIV-1 was exported to Haiti probably in 1966 and arrived in North America approximately 2 years later. Since the mid-1980s the different HIV-1 M subtypes have been spreading, leading to a global pandemic. In contrast to HIV-1, HIV-2 initially remained restricted to West Africa because of its significantly lower infectivity. After HIV-2 was exported to Portugal and France probably during the mid-1960s, the spreading of HIV-2 with a low prevalence especially in Europe, South America, and Asia is documented. Globally, an estimated 37.7 million people were living with HIV in 2020. Transmission HIV can enter the body via intact mucous membranes, eczematous or injured skin or mucosa, and parenteral inoculation. The major factors leading to the transmission of HIV are as follows: Having unprotected sexual intercourse with a person infected with HIV Sharing injections, drug equipment with someone who has HIV. Through vertical transmission i.e. from mother to child during pregnancy, birth, or breastfeeding. Receiving blood transfusions, organ transplants, or tissue transplants that are contaminated with HIV. Symptoms Acute HIV Infection A few weeks after getting HIV, many people have flu-like symptoms, which may last days or weeks. These symptoms can include fever, headache, tiredness, and enlarged lymph glands in the neck and groin area. Some people may have no symptoms. Chronic HIV Infection In the next stage of HIV infection, the virus still multiplies, but at very low levels. People may not feel sick or have any symptoms. If they are not getting treatment for HIV during this stage, they can still pass the virus to other people. Getting and staying on treatment prevents passing HIV to others. Without HIV treatment, people can stay in this stage for a decade or more, although some move through this stage faster. AIDS AIDS is the most advanced stage of HIV when a person's immune system is severely weakened and has difficulty fighting infections and certain cancers. At this stage, serious symptoms develop, such as: Rapid weight loss Serious infections Pneumonia Recurrent fevers Prolonged swelling of the lymph nodes Skin blotches Prolonged diarrhea Sores of the mouth, anus, or genitals Memory loss Depression Other neurologic disorders NOTE: The only way to know for sure if you have HIV is to get tested. One CANNOT rely on symptoms to tell whether you have HIV. Diagnosis The detection systems can be differentiated into two principles of detection namely Antibody and Virus detection. HIV RNA can be detected in the blood using a nucleic acid test (NAT) about 11 days after infection. Antibody screening tests HIV antibody screening tests are used for the primary diagnosis followed by a confirmation test in the case of a reactive result in the screening assay. In addition to the ELISA (enzyme-linked immunosorbent assay) or variants of this test system, particle agglutination tests are used. Approved ELISA tests contain antigens of HIV-1 group M, particularly HIV-1 M: B, group O, and HIV-2. Depending on the manufacturer, additional antigens derived from the reverse transcriptase and the p24 protein are included in the test systems. An infection can be detected serologically after 3 weeks. Confirmatory test (Western blot, Immunoblot) The Immunoblot/Western blot assay was introduced as serologic confirmatory tests, but these test systems have a lower sensitivity in the early phase of HIV infection than antibody screening tests or p24 antigen detection systems. Only if the criteria for a positive Immunoblot/Western blot are fulfilled can the HIV infection be considered as confirmed. p24 Antigen test The p24 protein forms the inner capsid. Each virus particle contains approximately 2,000 p24 molecules. Detection of p24 antigen is performed using a combination of polyclonal or monoclonal antibodies, following the principle of the sandwich ELISA technique. NAT - Nucleic Acid Amplification Technology Diagnosis of an HIV infection can be performed by determining proviral DNA in cells or of the viral RNA genome in plasma. For analysis of the viral load and the presence of HIV in blood donations, RNA is extracted from virus particles in plasma. Genome detection can be done either via direct amplification of defined target sequences or through the use of probes with subsequent signal amplification. Treatment HIV infection has a very complex pathogenesis and varies substantially in different patients. Therefore, it can easily be considered a very host-specific infection. The specificity of pathogenesis often complicates treatment options that are currently available for HIV infection. Effective management of HIV infection is possible using different combinations of available drugs. This method of treatment is collectively known as antiretroviral therapy (ART). Standard ART consists of a concoction of at least three medicines (termed as “highly active antiretroviral therapy” or HAART). Effective ART often helps control the multiplication of HIV in infected patients and increases the count of CD4 cells, thus, prolonging the asymptomatic phase of infection, slowing the progression of the disease, and also helping in reducing the risk of transmission. The following are the important HIV drug classes: Reverse transcriptase inhibitors Protease inhibitor Fusion inhibitor Chemokine Receptor 5 Antagonist Integrase Strand Transfer Inhibitors Prevention Anyone can get HIV but steps can be taken to protect self from HIV Protected sexual intercourse with the use of condoms, diaphragms, etc. Limit number of sexual partners Get tested and treated for STDs Get Pre-exposure Prophylaxis Use of sterile equipment while injecting drugs. Social impact and stigma "We live in a completely interdependent world, which means we cannot escape each other. How we respond to AIDS depends, in part, on whether we understand this interdependence. It is not someone else's problem. This is everybody's problem." - Bill Clinton HIV stigma refers to the negative attitudes and beliefs about people with HIV. It is the prejudice that comes with labeling an individual as part of a group that is believed to be socially unacceptable. The patients are being discriminated against based on outdated and prejudiced views of the people surrounding them. In many families and societies being diagnosed with HIV simply means being social outcasts. People fear they may contract the disease through mere casual contact or handling the articles being used by them. This leads to them being shunned by society. As with any disease, recovery from HIV needs the mental stability of the person. Unfortunately regarding this disease mental stability is a luxury that is not given to the person. It is considered that it is the individual's fault that they have contracted this disease. HIV is frequently transmitted through actions that are kept secret, hidden, or are illegal- a characteristic that makes this disease unique. Misconceptions have shaped beliefs; unfortunately, most people in our society do not realize that it is not the patient’s intention to be infected by HIV. Consequently, despair, exhaustion, and helplessness approaching panic are experienced by most patients, who are faced with society’s rejection, losing their hopes for a prosperous future. It is almost impossible to conceptualize the degree of suffering the human body undergoes and the despair of the human mind in a societal group devastated by AIDS. Like any other stigma it is rooted in the fear of HIV. Many of our ideas about this disease emerge from that which appeared first in the early 1980s. There still exist numerous misconceptions about how HIV is transmitted and what it means to live with HIV today. As with any other taboo, overcoming this social stigma requires the eradication of the fear associated with it. This fear can only be eradicated through education. Multiple efforts are being taken by WHO and local healthcare authorities to educate the public about this disease; mainly its transmission and modes of prevention. People need to realize that speaking with the patient or casual contact with the infected does NOT spread this disease. The people suffering from AIDS need to be accepted in society. They need support and strength from the ones surrounding them to combat it. The theme of World AIDS day 2021 is " End inequalities. End AIDS" , this year we take the fight not to the hospitals but we fight it within us. We fight to accept the harsh reality that these patients live in. This year we try to make this world more hospitable to them. Only with the acceptance of the true reality can mankind produce a united front to combat this disease. References Origin of HIV and the AIDS Pandemic by Paul M. Sharp and Beatrice H. Hahn Human Immunodeficiency virus (HIV) by German Advisory Committee Blood (Arbeitskreis Blut) Subgroup 'Assessment of Pathogens Transmissible by Blood' Current scenario of HIV/AIDS, Treatment Options, and Major Challenges with Compliance to Antiretroviral Therapy by Adnan Bashir Bhatti, Muhammad Usman and Venkataramana Kandi The social stigma of HIV-AIDS : society’s role by Emmanuel N Kontomanolis, Spurgeon Michalopoulos and Zacharias Fasoulakis

Author: Afreen Introduction: Time is like a big mystery that people have been curious about for a very long time. It's the fourth dimension, and it has puzzled us for centuries. As we learn more about the world, we also learn more about time and how it works. There's this cool thing called time dilation, which comes from Albert Einstein's ideas about space and time. Let's break it down: 1. Einstein's Idea : A really smart guy named Albert Einstein had some ideas about how time works. He said that time is not the same for everyone. It can be different depending on how fast you're moving. 2. Time is Relative : Imagine you and a friend have super fast toy cars. If your friend zooms by in their car, time might feel different for them compared to you. It's like time can stretch or squish depending on how fast you're going. 3. Two Parts : Einstein had two big ideas – special relativity and general relativity. Special relativity, from 1905, says that time can be different for different people depending on how they're moving. So, time dilation is basically the idea that time is not always the same. It can change depending on how fast you're moving. It sounds a bit tricky, but it helps us understand more about the universe and how things work together. The equation central to special relativity is the famous time dilation equation: Where This equation shows that as an object's velocity approaches the speed of light, time in its frame of reference appears to slow down from the perspective of a stationary observer. Let us take an example. Imagine you have two twins, just like two peas in a pod. One twin stays on Earth, and the other goes on a super-fast space trip. When the space-traveling twin comes back, they would find out that they got a bit younger compared to the twin who stayed on Earth. It's like time went slower for the twin in space. This is because of something called time dilation. Now, think about super tiny particles, like really, really small. Scientists have these cool machines, like the Large Hadron Collider (LHC), that make these tiny particles go super fast. And guess what? These speedy particles experience time dilation, just like the twin in space. This helps scientists understand more about how things work in our universe. Here's a real-world example: you know those GPS devices that help you find your way? The satellites in space, which make GPS work, move really fast. Because they're speedy, they experience time dilation. If scientists don't correct for this, your GPS might not give the right directions. So, understanding time dilation is like making sure your GPS takes you to the right place! Now, when we talk about really, really big things in space, like black holes, time dilation gets super extreme. It's like time slows down near these giant objects. Scientists have checked this out, and it turns out, time really does behave differently near black holes. So, time dilation isn't just a fancy idea – it helps us explore space and make things like GPS work better!

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Department: Production Description: Writing high quality articles for the website. Articles may also get posted through YSJ’s social media platforms and distributed to the newsletter. Content Creators will work closely with the departmental lead, contributing to a set schedule of ideas. The role is creative, where content creators can pitch their own ideas they would like to write about. Person Specification Fluency in (British) English Excellent command of concise writing Time management Creativity and Innovation