It is well known, that the inflammatory process that characterizes acute pancreatitis (AP) can lead to both pancreatic damage and systemic inflammatory response syndrome (SIRS). During the last 20 years, there has been a growing incidence of episodes of acute pancreatitis associated with hypertriglyceridaemia (HTAP). This review provides an overview of triglyceride metabolism and the potential mechanisms that may contribute to developing or exacerbating HTAP. The article comprehensively discusses the various pathological roles of free fatty acid, inflammatory response mechanisms, the involvement of microcirculation, serum calcium overload, oxidative stress and the endoplasmic reticulum, genetic polymorphism, and gut microbiota, which are known to trigger or escalate this condition. Future perspectives on HTAP appear promising, with ongoing research focused on developing more specific and effective treatment strategies. Reports of lipid metabolism disorders, in particular hypertriglyceridaemia and how this can be one of the causes of acute pancreatitis, are increasingly numerous in the literature.The pathogenesis of this aforementioned association has not yet been fully and exhaustively described to date.We give a brief overview of the triglyceride metabolism and animal models of hypertriglyceridaemia associated acute pancreatitis, which may be help for readers to understand the mechanisms and future research directions.Furthermore, to try to provide an exhaustive explanation of this pathogenetic relationship, we analyze several factors that do not only concern the inflammatory process.1.Pancreatic acinar cells, with their related enzyme secretion, play a pivotal role in the onset of the inflammatory process that characterizes acute pancreatitis (AP), which can evolve into local pancreatic complication, systemic inflammatory response syndrome (SIRS) . AnalysiAmong the most common dyslipidemias, there is certainly hypertriglyceridaemia (HTG), which occurs when plasma triglyceride levels are greater than 1.7 mmol/L, i.e. 150 mg/dL . The guiAnimal models have proposed several theories on the pathogenesis of HTAP . One of 2+) overload, endoplasmic reticulum stress, oxidative stress, chemokines and cytokines, genetic polymorphism, and gut microbiota . Because of the difficulty to obtain human pancreatic tissue during an AP episode, animal models were often used to study the early cellular events during AP. In order to help readers easily understand the mechanisms and future research directions, we also give a brief overview of the triglyceride metabolism and animal models of HTAP.However, these two theories may not provide a comprehensive explanation of the process by which HTG induces and aggravates AP. In this article, we present a comprehensive review of current knowledge on HTAP pathogenesis, including the role of FFA, microcirculatory disorder, calcium (Cacrobiota . Because2.. The very low-density lipoproteins (VLDL) and the chylomicrons (CMs) represent the two main types of triglyceride-rich lipoproteins, which are respectively secreted by the liver and by the intestine, serve for transport endogenous and exogenous (dietary) lipids to peripheral tissues. Both triglyceride-rich lipoproteins contain apolipoprotein (apo) B as their primary apolipoprotein. They also contain other apolipoproteins, such as apo A-V, apo C-II, apo C-III, and apo E, some of which derive from circulating high density lipoprotein (HDL) [Triglycerides play a key role in efficiently storing the body’s excess energy . Triglyc. The verin (HDL) .2.1.The principal pathway for triglyceride synthesis is glycerol-3-phosphate occurring in the liver and adipose tissue. The last accounts for more than 90% of the total triglyceride synthesis . TriglycThe intestinal absorption by the enterocytes of triglycerides, cholesterol and long-chain saturated and unsaturated fatty acids occurs through the formation of micelles. On the other hand, short and medium chain fatty acids (chain length from 2 to 10 carbon atoms) are able to directly cross enterocyte membranes and, as a consequence, immediately reach the bloodstream. Thereafter, intestinal enterocytes synthesize triglycerides from the absorbed fatty acids and package them in CMs, i.e. lipoprotein particles capable of transporting fats and cholesterol from the small intestine throughout the body . Before These particles, by binding to the lipoprotein lipase (LPL) expressed on the luminal surface, undergo a metabolization process which it serves to convert triglycerides into FFA, thus allowing their absorption by peripheral tissues. Consequently, this overabundance of lipids inevitably leads to an accumulation of triglycerides in the heart, skeletal muscle and liver. Furthermore, lipoproteins, rich in triglycerides, may represent a source of atherosclerotic lipids, and in addition, the same lipolysis of triglycerides could lead to the production of lipids toxic to the vessels .2.2.LPL is an essential enzyme in mediating the hydrolysis of triglyceride-rich lipoproteins, thereby enabling the release of FFA and residual lipoproteins. Lipase maturation factor 1 (LMF1) is required to synthesize the active enzyme, while angiopoietin-like protein 4 is able to inhibit its activity by cleaving the LPL dimer . Once LPLPL hydrolyzes the VLDL particles in the plasma, progressively reducing their size until arriving at the formation of intermediate density lipoproteins (IDL). The catabolism of IDL occurs partly in the hepatocytes by LPL and hepatic triglyceride lipase to generate LDL particles, and partly in the blood. The action of the cholesterol ester transfer protein allows VLDL and LDL particles to acquire additional cholesterol esters in plasma, through the exchange of triglycerides in VLDL and LDL for cholesterol esters in HDL . Along tResidual lipoproteins, absorbed by the vascular endothelium, are able to promote inflammation and atherosclerosis through various mechanisms, including abnormal secretion by endothelial cells and reduced flow-mediated dilatation . The liv3.Animal models are still the primary means of conducting research on the pathogenesis of diseases due to medical ethics requirements. Animal models have many advantages: Firstly, the crucial events in the pathophysiology of HTAP can be studied in animal models . Secondl3.1.The most common experimental animals used for HTAP studies are rats ,41–43, m3.2.The administration of high-fat diet is mainly used to induce HTG in animal models  6,19,4040,48–51.Although there are various methods for inducing AP, only three of them  have been confirmed in establishing the HTAP model . It is kThe retrograde biliopancreatic duct infusion of bile acids can induce moderate to severe AP in animal models. It is based on the theory of severe AP caused by the reflux of biliary and pancreatic juices in the pancreatic duct. However, the pancreatitis induced by this method and is associated with high mortality making it cumbersome to use and limiting its application . Lastly,4.4.1.Triglycerides are not inherently toxic to the pancreas. The pancreatic lipase breaks down triglycerides into FFAs causing lipotoxicity . The sev2+ overload, endoplasmic reticulum stress, increased production of reactive oxygen species (ROS), and recruitment of macrophages [61It is known that FFAs are of two types based on the presence or absence of C-C double bonds: 1) saturated fatty acids (SFA) and 2) unsaturated fatty acids (UFA). Dietary unsaturated/saturated components determine the fatty acid composition in fat cell triglyceride, affecting AP severity . Cellularophages ,6162. Bephages 61.On the other hand, among monounsaturated and polyunsaturated FFA, oleic acid and linoleic acid are the most prevalent in the body. Oleic acid has been found to be the most abundant FFA found in pancreatic necrotic collections . UFA pro2+ causing hypocalcaemia in severe AP. This could be one of the possible mechanisms of HTAP progression as early Ca2+ supplementation delays organ failure in severe AP models [FFA can interact with extracellular CaP models . Apart fP models ,68. MoreP models ,70  generation is lost, ultimately resulting in mitochondrial permeabilization. The loss of mitochondrial membrane permeabilization is known to be a universal trigger for cell death. Moreover, ATP depletion increases the toxic Ca2+ concentration by disrupting the ATP-dependent sarcoendoplasmic reticulum Ca2+ ATPase (SERCA) pumps and plasma membrane Ca2+ ATPase (PMCA) pumps from clearing excessive cytosolic Ca2+. It impairs cytoprotective mechanisms that need ATP, such as autophagy and unfolded protein responses (UPR) [Cytosolic Caar cells . Excessies (UPR) ,89.4.3.2.2+ concentration is significantly associated with lipid metabolism disorders and can be inhibited by pre-treatment with inhibitors of Ca2+ adenosine triphosphatases and chelating agents of intracellular Ca2+ depots [2+ concentrations in acinar cells. These Ca2+ concentrations correlated with a dose-dependent UFA/SFA ratio [2+ is released through the inositol 1,4,5-triphosphate receptors system and the ryanodine receptor system into the cytoplasm, thus increasing the cytosolic Ca2+ concentration [2+ overload is not mediated by inositol 1,4,5-triphosphate [2+ overload.Earlier studies have shown that an increase in intracellular Ca+ depots ,90,91. CFA ratio . Acinar FA ratio . This prntration . High cohosphate . However4.4.4.4.1.2+ storage. ER is extremely sensitive to stress which is triggered by the inability to process and dispose of proteins effectively. It can result in misfolded and unfolded protein accumulation within the ER lumen and can contribute to the early stages of pancreatic injury [The endoplasmic reticulum (ER) is the main site for intracellular protein processing and Cac injury ,94.4.4.2.In vitro studies using fatty acids to act on pancreatic exocrine cells have shown that SFA, particularly high concentrations of PA, can impact the splicing of Xbp1, increase transcriptional levels of proteins related to the UPR, elevate levels of transforming growth factor-β, and upregulate the expression of inflammatory signalling factors such as NF-κB, TNF-α, IL-6, and IL-1β [It was found that a high-fat diet per se induces ER stress, activating the inositol-requiring enzyme 1 (IRE1) a-spliced X-box binding protein 1 (sXbp1) signalling pathway , which is closely linked to lipid metabolism and inflammation, suggesting that disorders of lipid metabolism may promote ER stress and hence, pancreatic injury ,96. In vnd IL-1β ,73,97. And IL-1β .in vitro and in vivo studies have indicated that disruption of normal autophagy function in AP leads to abnormal activation of trypsinogen [ER stress is tightly associated with autophagic destruction during HTAP progression. Autophagy is completed through a series of steps that start with the enucleation of cytosolic contents within an open double membrane formed from the ER, Golgi apparatus, and plasma membrane . Then, tpsinogen , ER strepsinogen .4.5.4.5.1.via receptor-interacting protein 3 (RIP3) pathway, resulting in positive feedback TNF-induced pancreatic necrosis [Oxidative stress has been identified as an important factor in the onset of AP and also contributes to the systemic inflammatory reaction, xanthine oxidase activation, glutathione, and thiol oxidation ,102 Fig. The imbnecrosis ,105. Macnecrosis . Acinar necrosis ,106,107.4.5.2.HTAP is accompanied by a decrease in the production of antioxidants (e.g. superoxide dismutase (SOD), glutathione (GSH), and glutathione peroxidase) in pancreatic tissue and an increase in the production of pro-oxidant enzymes (e.g. reduced nicotinamide adenine dinucleotide phosphate oxidase (NOx), and catalase (CAT)). The antioxidant/oxidant imbalance triggers an inflammatory response and further aggravates pancreatic injury 108,109.4.6.4.6.1.Pancreatic acinar cells are responsible for the productionof diverse chemokines that can promote the infiltration of neutrophils and subsequent monocytes into the pancreas, aggravating AP. The C-X-C motif chemokine ligand 1 (CXCL1) functions as a chemotactic factor specifically for neutrophils. And the C-X-C motif chemokine ligand 2 (CXCL2) promotes cell migration through its interaction with the C-X-C chemokine receptor (CXCR2) ,119. Che2+-activated Cl− channel) expression via activating IL-6 receptor/signal transducer and activator of transcription 3 (STAT3) signal to facilitate the pathogenesis of AP [The pathways mentioned above contribute to the amplification of pro-inflammatory cytokine production, including TNF, IL-1β, IL-6, and IL-18. These cytokines can further worsen pancreatic injury and contribute to systemic inflammation . Inflammis of AP . Followiis of AP ,138. IL-is of AP ,140.4.6.2.Initially, HTAP was thought to be caused by inflammatory cascades involving chemokines and cytokines ,141 Fig. FFA is via the NF-κB pathway [The activity of protein kinase C (PKC) aligns with the degree of pancreatic injury, indicating a strong association with PKC and the exacerbation of HTAP ,146. Stu pathway . The lit4.7.S447X variant was used in HTAP patients in early clinical treatment and temporarily reduced plasma triglyceride levels [For hereditary pancreatitis, heterozygous variants in two mechanistically genes of ‘trypsin-dependent pathway’, PRSS1 (encoding cationic trypsinogen) and SPINK1 (encoding pancreatic secretory trypsin inhibitor products), are said to have the effect of occurring human pathogenic variants ,149. Bece levels . In addie levels . Chang ee levels . Later, e levels .4.8.There is a strong association between gut microbiota and lipid metabolism disorders as well as systemic inflammation . Long-te5.5.1.in vivo may provide potential prevention of the progress of this disease [American College of Gastroenterology Guidelines recommend that serum triglycerides should be above 1000 mg/dL (>11.3 mmol/L) to be considered the cause of AP . Based o disease .Emerging genetic drugs, such as alipogene tiparvovec and LCQ908, hold promise for the treatment of patients with LPL deficiency . Some sp5.2.2+ overload, ER stress, oxidative stress, chemokines and cytokines, genetic polymorphism, and gut microbiota. However, limited knowledge exists regarding the specific effects of SFAs on pancreatic endocrine cells, such as beta cells, as well as the intricate interplay between the exocrine and endocrine components of the pancreas in both healthy and diseased states. Further research is necessary to comprehensively understand these interactions and their significance in the development and progression of AP and other pancreatic disorders. Additional research is required to gain a comprehensive understanding of these interactions, the implications allied with developing and progressing AP and other pancreatic disorders. This area of research is likely to be an important focus of future clinical and fundamental research [in vivo and ex vivo studies in both animal models and human subjects. Impaired autophagic blood flow is closely related to endoplasmic reticulum stress and both factors contribute to the development of HTAP, but the pathways that synergize or feedback with each other need further study. As our knowledge of the molecular mechanisms underlying acute pancreatitis progresses, our understanding of the condition deepens and it is becoming increasingly clear that there are common key pathways allied with disease progression and development, such as DAMPs, autophagy, and monocyte polarization. However, much of the underlying genetic and molecular complexity of acute pancreatitis remains to be completely understood, and there are likely to be many more genes and pathways contributing to the HTAP that need to be elucidated. In the future, research into LPL-related gene therapy and the identification of novel HTAP-specific genes can provide insights into the underlying genetic and molecular mechanisms of the disease and pave the way for the development of novel and improved treatment approaches. As our understanding of HTAP expands, it is expected that we will be able to develop more precise and individualized approaches for diagnosing and providing optimal treatment for the condition.Our understanding of the pathophysiology of HTAP is still in its infancy, although the association between HTG and AP has been well-established ,166. Allresearch . Differeresearch . To conf6.HTAP is a complex disease with a multifactorial aetiology, and not fully understood pathophysiology. As our knowledge regarding the disease evolves, we are likely to develop more effective and targeted treatments, which can reduce the occurrence of more severe disease outcomes or prevent the disease altogether. Future research will need to focus on elucidating the underlying genetic and molecular mechanisms of the disease, as well as identifying specific pathways and targets that can be used to develop more effective treatments. This will require a multidisciplinary approach that combines genetics, molecular biology, and clinical research and will likely involve collaborations between basic and clinical scientists across multiple disciplines. Ultimately, developing more effective treatments for HTAP will require a deep understanding of the underlying pathophysiology of the disease, as well as the development of more targeted and personalized approaches to diagnosis and treatment.


Increased variability of GGT may be related to an increased risk of VT. Maintaining a stable GGT level would be beneficial in reducing the risk of VT.Gamma-glutamyl transferase (GGT) is a biomarker of inflammation, and is known to be associated with stroke and atrial fibrillation. Venous thromboembolism (VT), a not uncommon thrombotic disorder, shares similar mechanisms with other thrombotic disorders including these stroke and atrial fibrillation. Given these associations, we intended to investigate the potential association between variability in GGT and VT. The study included data from the National Health Insurance Service-Health Screening Cohort, comprising 1,085,105 participants with health examinations 3 or more times from 2003 to 2008. Variability indexes were the coefficient of variation, standard deviation, and variability independent of the mean. The occurrence of venous thromboembolism (VT) was defined with more than one claim of the following ICD-10 codes: deep VT (I80.2–80.3), pulmonary thromboembolism (I26), intraabdominal venous thrombosis , or other VT . To determine the relationship of quartiles of GGT with incident VT risk, Kaplan–Meier survival curve and logrank test were used. Cox’s proportional hazard regression was used to investigate the risk of VT occurrence by GGT quartile (Q1–Q4). A total of 1,085,105 subjects were incorporated in the analysis, and the average follow-up was 12.4 years (interquartile range 12.2–12.6). VT occurred in 11,769 (1.08%) patients. The GGT level was measured 5,707,768 times in this stud. Multivariable analysis showed that GGT variability were positively associated with the occurrence of VT. Compared to the Q1, the Q4 showed an adjusted HR of 1.15 (95% CI 1.09–1.21,  In order to determine the association between a disease and a specific biomarker, multiple measurements are more reliable than one measurement. Also, since variability itself is an important biomarker, the variability of the biomarker must be measured repeatedly. Moreover, variability in biomarkers such as blood pressure, blood glucose levels, and lipid profiles can affect the progression of diseases. Previous studies have shown that the variability of GGT is closely linked to the risk of acute coronary syndrome, heart failure, and stroke8.Gamma-glutamyl transferase (GGT) is a representative biomarker of liver diseases. Recent studies have reported other diseases related to GGT, particularly end-stage renal disease, cardiovascular and cerebrovascular disease, leading to mortality9. The incidence of VT is increasing globally due to the aging society10. There are known risk factors for VT, such as cancer, fracture, renal failure, chronic inflammatory disorders, antiphospholipid antibody syndrome, and obesity10. Nevertheless, there are needs for preventable and correctable risk factors for VT.Venous thrombosis (VT) is not uncommon and has a high worldwide disease burden, resulting in mortality in certain cases13. These reports provided evidence that GGT variability may be involved in VT. In addition, research has shown a strong association between variations in GGT and other metabolic parameters with inflammation-related metabolism and dysregulation of hematologic homeostasis. These factors could potentially contribute to the development of VT17. However, to date, there is a scarcity of information regarding the link between VT and prolonged changes in GGT levels. We hypothesized that GGT variability is related with VT occurrence. The purpose of this study was to examine the relationship between GGT variability and the occurrence of VT in a population-based nationwide cohort database.In prior studies, increased blood GGT was related with venous thrombosis and atrial fibrillation, which is closely related to venous thrombosisA total of 1,085,105 subjects were entered in the study . VT occurred in 11,769 (1.08%) patients: deep vein thrombosis (4710 (0.43%)), pulmonary thromboembolism (3109 (0.29%)), intraabdominal thrombosis (5214 (0.48%)), and other VT (4793 (0.44%)). The GGT was checked 5,707,768 times . The comparative analysis results on the study subjects according to quartile  of coefficient of variation are presented in Table p < 0.001). Moreover, in multivariable analysis, variability of GGT were positively associated with the occurrence of VT. The Q4 showed an adjusted hazard ratio (HR) of 1.15  when using coefficient of variation, 1.24  when using standard deviation (SD), and 1.10  when using variability independent of the mean .20. In a meta-analysis, GGT was related with stroke, cardiovascular, and all-cause mortality19. High GGT levels had a positive linking with increased stroke risk, and the highest GGT quartile had about 1.5 times higher cardiovascular and all-cause mortality risk than the lowest quartile19. In another study of 698,937 diabetic patients without known cardiovascular disease, chronic liver disease, and heavy alcohol consumption, the risk of stroke and death increased by 6% and 23%, respectively, in the group with increased GGT variability21. In a general population-based study that investigated GGT and hospitalization for heart failure, 1.16% of events occurred during 8.4 years of follow-up, and the risk of hospitalization was high in the group with high GGT variability, with an HR of 1.228. Our study confirmed that the risk of venous thrombosis increased when GGT variability was high. It can be inferred that GGT oscillation is related to the occurrence of thrombotic disease due to homeostasis failure as well as an increase in GGT.Previous studies have shown relationships of stroke and cardiovascular disease with GGTOur study demonstrated the relationship of GGT variability and VT. Moreover, the relationship was consistent in the subgroup analysis, especially in deep VT and pulmonary thromboembolism. While PTE is the one of the diseases with high mortality, our study suggested additional information on VT, especially deep VT and pulmonary thromboembolism occurrence.22. Glutathione is an anti-oxidant synthesized by glutamate-cysteine ligase and glutathione synthase23. Elevated reactive oxygen species (ROS) can cause oxidative damage to cells24, and glutathione has a protective effect on ROS25. GGT is involved in degrading extracellular glutathione and providing cysteine during synthesis of glutathione26. GGT elevation promotes ROS generation and causes oxidative stress27, which seems to be involved in the occurrence of cardiovascular disease. The development of venous thrombosis is also affected by ROS, which influence the formation and degradation of thrombus through the coagulation pathway, fibrinolysis, and effector cells including red blood cells and platelets29. Although the exact mechanism by which GGT variability causes VT is not known, it is presumed that GGT may affect the occurrence of VT as a mechanism similar to how variability in blood pressure or blood sugar adversely affects arteriosclerosis31. Blood pressure variability affects progression of atherosclerosis by increasing inflammation, mechanical stimulation of vessels, and vascular smooth muscle cell dysfunction30. Considering that GGT induction is increased by oxidative stress32, an increase in GGT variability may indicate recurrent oxidative stress.Although our study does not explain the mechanism, there are some possible hypotheses on the results of our study. GGT is involved in glutathione homeostasisThis study had several limitations. First, there is a possibility of other confounding factors such as coagulation tests including d-dimer and C-reactive protein, which were unavailable in our dataset. Second, the study population are Korean, and the results could not be applied to other ethnicities. Third, the retrospective observational design of our study does not allow us to establish a clear causal connection and presents challenges in identifying the exact cause of GGT variability. Although our study goal was to confirm the association of VT with a fixed estimate of the GGT variability for the prior 6 years before index date, we did not consider GGT variability may change in the follow-up periods. Fourth, cerebral VT, which mainly occurs in young women, was excluded in our study because our dataset consists of individuals older than 40 years. Fifth, this study may exhibit selection bias as it exclusively includes participants who have undergone health screening examinations, potentially resulting in a sample comprised solely of healthy individuals. Lastly, diagnostic accuracy of VT with ICD-10 codes in the National Health Insurance Service-National Health Screening (NHIS-HEALS) could not be clearly presented.Despite the limitations, this study has some strengths. This study utilized a nationally representative data over a significant period to examine the impact of GGT variability on VT. Our findings provide compelling evidence confirming the importance of retaining a stable GGT level as a preventive measure against VT.Increased GGT variability may be linked with increased risk of VT. Maintaining stable GGT level would be helpful for reducing the risk of VT. Further studies on the mechanisms responsible for the association between GGT variability and VT development are needed.35. Annual standardized health screenings are provided by NHIS. The cohort used in this study comprised randomly selected individuals between 40 and 79 years of age, who had done at least three health screenings (Dataset number: NIHS-2021-01-715)38. The NHIS-HEALS cohort database used in this study includes demographic data, socioeconomic status, and health screening information, as well as a claims database that contains information on diagnosis, prescription, and treatment methods. The health screening process involved measurements of weight, height, laboratory results, and lifestyle questionnaire such as smoking and alcohol history. The NHIS-HEALS does not have any role in this study. The study analysis was approved by the Institutional Review Board of Ewha Womans University College of Medicine (2021-12-038), and consent was waived. This study is performed in accordance with the Declaration of Helsinki.This study utilized the NHIS-HEALS cohort database from Korea. The NHIS is a government-controlled insurance provider that covers 97% of Koreans. The remaining are covered by the Medical Aid program, which is also administered by the governmentThe participants with health examination 3 times or more between 2003 and 2008 were included from the NIHS-HEALS database . Participants with missing data for analysis  were excluded. Furtherer, participants with a previous history of VT (n = 4414) were excluded. Finally, 1,085,105 participants were investigated in this study . Three variability indexes examined were coefficient of variation, SD, and variability independent of the mean. The formular for variability independent of the mean was 100 × SD/Mean40. The follow-up period was from the index date to VT occurrence, death, or December 2020, whichever came first.The primary outcome of the study was VT occurrence, which was defined as the presence of more than one claims with diagnostic codes corresponding to any of the following ICD-10 codes:  with code for anticoagulants and antiplatelet, based on a previous study41. Kaplan–Meier survival curve along with logrank test were used to access the association of quartiles of GGT with incident VT risk. The study calculated the incidence of VT as the number of cases divided by the sum of person-years. Cox's proportional hazard regression was used to determine the risk of VT occurrence by GGT quartile, and the hazard ratio (HR) and 95% confidence interval (CI) were determined. A multivariable regression model with adjustments for several factors, including age, sex, body mass index, domestic income, regular physical activity, alcohol drinking, smoking status, and comorbidities , aspartate aminotransferase, and alanine aminotransferase was constructed. Shoenfeld's residuals were performed, and no departure from the proportional hazards’ assumption was detected. Subgroup analysis analyses were performed to determine the risk of each kind of VT . Sensitivity analysis was conducted by adjusting for (1) mean GGT level in multivariable analysis, (2) coefficient of variation, SD, and variability independent of the mean according to decile instead of quartile, and (3) excluding participants with VT within 1 year from the index date to minimize the possibility of reverse causality. Statistical Analysis System software  was used in statistical evaluations. A p value < 0.05 was considered statistically significant.The study used the Chi-square test and analysis of variance test to compare the demographics of different groups. All GGT variability was found to have a positive linear association, confirmed by restricted cubic splinesThe Institutional Review Board of Ewha Womans University College of Medicine (2021-12-038) approved the analysis and provided a consent waiver as the data were anonymized and freely accessible by the NHIS for study purposes.Supplementary Information.


Coffea arabica L.). The coffee pulp, which includes phytochemicals with a proven antifungal action, is one of the most insufficiently utilized and neglected byproducts of coffee refining. In the current experiment, we carried out in silico screening of the isolated Arabica coffee phytochemicals for antifungal activity against Aspergillus fumigatus: a foodborne fungus of great public health importance. As determined by the molecular docking interactions of the library compounds indicated, the best interactions were found to occur between the nucleoside-diphosphate kinase protein 6XP7 and the test molecules Naringin , followed by Epigallocatechin gallate . Therefore, Naringin was opted for further validation with molecular dynamic simulations. The ligand–protein complex RMSD indicated a fairly stable Naringin-NDK ligand–protein complex throughout the simulation period (2–16 Å). In ADME and gastrointestinal absorbability testing, Naringin was observed to be orally bioavailable, with very low intestinal absorption and a bioavailability score of 0.17. This was further supported by the boiled egg analysis data, which clearly indicated that the GI absorption of the Naringin molecule was obscure. We found that naringin could be harmful only when swallowed at a median lethal dose between 2000 and 5000 mg/kg. In accordance with these findings, the toxicity prediction reports suggested that Naringin, found especially in citrus fruits and tomatoes, is safe for human consumption after further investigation. Overall, Naringin may be an ideal candidate for developing anti-A. fumigatus treatments and food packaging materials. Thus, this study addresses the simultaneous problems of discarded coffee waste management and antifungal resistance to available medications.In the tropics, coffee has been one of the most extensively cultivated economic crops, especially Arabica coffee ( Aspergillus fumigatus (A. fumigatus), a saprophytic and thermophilic ascomycete fungus, reportedly causes the most serious diseases in humans among all the foodborne Filamentous Fungal Human Pathogens (FFHPs) [A. fumigatus is regarded as the most dangerous fungal pathogen to be found in food [Aspergillus species, A. fumigatus is regarded as the biggest threat when trailed by A. flavus, A. niger, A. terrus and A. nidulans. Invasive aspergillosis caused by the A. fumigatus is one of the foremost reasons for decease in those patients suffering from hematological malignancies and those undergoing chemotherapy [A. flavus [A. fumigatus, is known for the creation of reactive oxygen species (ROS) through reactions associated with redox cycling, which led to immunosuppression and necrosis [A. fumigatus had the potential to evade the immune responses and enhance AIDS and substance-induced immunodeficiencies due to GTX poisoning [A. fumigatus, is a mycotoxin encoded in chromosome 8 of the fungus [A. fumigatus contamination in food, there is a mounting necessity for food-grade antifungal drugs and preservatives to address both public health and food security concerns. (FFHPs) . Account in food . Within otherapy  and hemaotherapy . This de. flavus . Gliotoxnecrosis ,6. In adnecrosis . GTX, atnecrosis . Meanwhinecrosis . Owing toisoning . Fumagile fungus ,12,13. Te fungus , therebye fungus , eryptose fungus , damagine fungus , and ante fungus . Given tA. fumigatus infections and reports of antifungal resistance, there has been a necessity to identify and develop novel antifungal agents. With the focus now more oriented toward understanding the molecular intricacies underlying the propagation of the fungus, there are new avenues open to identify potential drug targets which could inhibit fungal growth and development. The A. fumigatus nucleoside-diphosphate kinase (NDK) enzyme catalyzes the final step of nucleotide biosynthesis by phosphorylating the nucleoside-diphosphates (NDPs) to nucleoside-triphosphates (NTPs) is an essential enzyme for the survival of the fungus. Regarding the antifungal activity evaluation, the biosynthesis of nucleotide purine and salvage pathways have been extensively studied against fungi of clinical importance, such as C. neoformans, C. albicans, and A. fumigatus. They all indicated a correlation between the disruption of the purine biosynthesis pathway and the subsequent attenuation of virulence in the fungi [With the increasing instance of he fungi ,19. NDK he fungi  and makiCoffea arabica alone were cultivated in the year 2022 [Candida spp. and Trichophyton spp. [Areca catechu leaf sheath for the packaging of different foods, concluding that coffee phytochemicals exhibited significant antifungal activity against Aspergillus spp., Penicillium spp., and Eurotium spp. [Alternaria brassicicola, Pestalotiopsis sp. and Paramyrothecium breviseta, which were found to infest vegetables, fruits, and coffee, respectively. The polyphenol composition of the above study comprised flavonoids such as epigallocatechin gallate (31.8%), naringin (9.63%), epicatechin gallate (8.66%), catechin (2.2%), gallocatechin gallate (0.12%), quercetin (5.42%), and phenolic acids and caffeine alkaloids such as caffeic acid (68.1%), caffeine (21.59%), p-coumaric acid (11.04%), rosmarinic acid (6.41%), o-coumaric acid (6.24%), and gallic acid (2.41%) [Coffee, a tropical cash crop found across the continents of Asia, America, and Africa, is a beverage of choice globally. Coffee has been generated in quantities as high as 171 million (60 kg) bags in the current year, and 98 million bags of ear 2022 . Coffee ton spp. . Anotherium spp. . Sangta  (2.41%) . All theOver the past few decades, computational techniques have developed into useful tools for drug-related investigations. Bioinformatics is the application of these techniques to biochemical phenomena. Using bioinformatics, drug design can be more precisely and accurately guided by the knowledge of new chemical structures, functions, and targets. Since 1980, the concept of the “rational use of drugs” (structure-guided drug design) has gained popularity. It involves the creation of target proteins and small molecules in silico as the basis for novel medication development . The advHigh throughput and other compound screens are designed and carried out in order to uncover compounds that interact with the therapeutic target. Chemistry programs are then executed to strengthen the potency, selectivity, and physiochemical nature of the molecule. Additionally, information is still being gathered to back up the claim that treating illnesses by intervening at the drug target could be successful. A number of naturally available sources (plants and microbes) have been thus investigated via computational techniques for their various bioactive nature, including antibacterial, antiviral, antifungal and anti-cancer properties ,31,32,33The molecular docking interactions of the library compounds  indicateOf all the interactions observed above, the best-docked complex of NDK against Naringin opted for an MD simulation. This simulation was carried out for a total duration of 100 ns, throughout which the stability of the protein, ligand (Naringin), and the protein–ligand complex was observed via parameters such as the Root Mean Square Deviation and fluctuation, amino acid contacts between the protein and ligand, changes occurring in the structure of the protein, and the properties of Naringin.The Root Mean Square Deviation (RMSD) is a quantitative measure that can understand the similarity between two superimposed atomic coordinates. In this study, the protein RMSD value ranged between 0.8 and 3.2 Å, with one outlying peak at 6.4 Å indicating a stable protein throughout the 100 ns simulation period . The ligThe Root Mean Square Fluctuation (RMSF) correlated to the time-average position of a particle, indicating the average deviation undergone by the particle over a particular period from its reference position. The point of interaction between amino acids was mostly stabilized between 0.6 and 1.8 Å, with fluctuating high peaks at 4.2, 4.8 and 5.4 Å .The SSE measure indicated the positional changes that occurred in the secondary structural elements of the protein, which were subjected to MD simulation. The SSEs of the NDK protein during the 100 ns simulation period were tracked, which revealed 32.41% Helixes and 14.75 strands, making up a 47.16% Total SSE, as indicated (helixes in red and strands in blue) .The RMSF of Naringin indicated that it was made of 40 atoms, with some interaction atomic fluctuations in the atoms between 2 and 14. Ligand RMSF was found to be stabilized between 4 and 6 Å, which suggested a few atomic fluctuations .The NDK amino acid residue ASP120 formed Hydrogen bonds, while PHE59 and VAL111 formed hydrophobic bonds with the Naringin molecule for more than 30% of the simulation time in the chosen trajectory (0.00 to 100.00 ns) . A timel2, while the polar surface area was between 375 and 420 Å2, and the radius of gyration (rGyr) ranged between 5.25 and 5.5 Å. The ligand RMSD varied from 0.8 to 2.4 Å during the simulation period of 100 ns . While Naringin and Epigallocatechin gallate both showed seven hydrogen bonding interactions, the glide score for the Epigallocatechin gallate–6XP7 interactions was −5.687 kcal/mol, and hence, Naringin was opted for further validation with molecular dynamic simulations. The lowest number of interactions was observed for Caffeine with 6XP7, demonstrating only two hydrogen bonds. The lowest glide score of −4.186 kcal/mol was observed for the caffeic acid–6XP7 complex. Hydrogen bonds, occurring between the ligand and proteins, have always been crucial in determining the specificity of observed interactions. Adding to this, interactions involving hydrogen bonds, pi–pi stacking, and salt bridges were found to be crucial when attributed to the increased inhibitory value of the ligand. The docking glide score and MD simulation, including RMSD and RMSF findings, indicated these interactions to be in a range from moderate to strong . The molC. albicans [Botrytis cinerea, Trichoderma glaucum, and Aspergillus fumigatus [Aspergillus parasiticus, Aspergillus flavus, Fusarium semitectum and Penicillium expansum [Aspergillus fumigatus activity of the molecule to ascertain the underlying mechanistic basis to indicate a possible consideration toward its interactions with the NDK.Naringin, a flavone glycoside, has been reported for its biomedical significance and for its antioxidant, anti-inflammatory, antiviral, bone regenerative, and potential to inhibit/reduce neurodegeneration, genetic damage, cardiovascular diseases, and human malignancies . Naringialbicans , Botrytiumigatus , Aspergiexpansum . Nonetheper os [The ADME and gastrointestinal absorbability of Naringin indicated that the molecule was orally bioavailable with very low possibilities of intestinal absorption. This was further supported by the boiled egg analysis data, which clearly indicated that the GI absorption of the Naringin molecule was obscure. Drug-target molecules which target body parts other than the brain should ideally be impermeable to the brain to avoid psychotropic side effects. Naringin has been reported to be non-toxic for Sprague Dawley rats with no-observed-adverse-effect-level (NOAEL) in the molecule being >1250 mg/kg/day of the rodent upon exposure for 13 weeks  and 6 moper os . In aligA. fumigatus infections.However, the findings of the current study should be validated with appropriate in vitro and in vivo antifungal experimentation, which could lead to the development of a plant-based non-toxic, antifungal agent to combat The current study was performed using an in silico approach involving compound library preparation based on a review of the literature, molecular docking, molecular dynamics simulations, ADME , gastrointestinal (GI) absorbability, and toxicity evaluation for the lead molecule ,52,53.Aspergillus fumgiatus (PDB ID: 6XP7)   with a rThe prepared protein chain was further used to generate a grid box. The grid box was defined around the pre-bound ligand in such a way that the center of the docked ligand was in identical dimensions to the binding box. Glide’s standard precision mode (SP) was employed for the rigid receptor docking technique, which was founded on the OPLS-3e force field and scaling factor 1.0 .Caffea arabica phytochemicals isolated and identified in previous research as per Sangta J, et al. (2021) were used to prepare a library of compounds for the in silico evaluation of anti-A. fumigatus properties. The structures of the compounds epigallocatechin gallate, naringin, epicatechin gallate, catechin, gallocatechin gallate, quercetin, caffeic acid, caffeine, p-coumaric acid, rosmarinic acid, o-coumaric acid, and gallic acid, were obtained in a .sdf format from Pubchem and were transformed into 3D structures using the Maestro Schrödinger 13.1 Ligprep module. The Ligprep assigned proper bond ordering and corrected the protonation and ionization states of the ligands. The minimization of energy was carried out at a stable pH of 7.5, with specific chirality.The The protein was immobile, whereas the ligands were flexible during the docking process. A regular precision mode was used for docking. Molecular docking was executed after synthesizing the ligand and protein and defining the grid in place of the pre-existing ligand on the protein. GLIDE was used to dock the identified ligands with the protein’s X-ray crystal structure. The best compounds for each target were chosen based on their glide g score, thermodynamic ideal energy value, the types of interactions, bonding potential, and conformations.Based on various parameters of molecular docking, the best-docked molecule was selected for further validation. This technique is a bioinformatic method that simulates the actual movements of the atoms in the protein–ligand complex for a set duration. It is used to study the movement of the protein–ligand complex in a solvent system. The best-docked model of the docked complex was used to achieve this using the DESMOND v 13.1 System Builder workflow. The best pose with an appropriate glide score obtained from molecular docking was further taken up for the molecular dynamics simulation using the DESMOND v 13.1 System Builder workflow. The boundary was set at 10 × 10 × 10 Å orthorhombic box, and the TIP3P water model of the solvent system was introduced using the OPLS4 force field. These proteins were neutralized with the addition of salt ions (sodium and Chlorine) in order to balance the overall charge of the system. A molecular simulation was run for 100 ns at the recording energy interval of a 12 ps simulation run. The whole system contained 20,692 atoms. The pressure and temperature were sustained at 1.01325 bar and 300 K, respectively. The results were analyzed with the help of a simulated interaction diagram tool that was built within the DESMOND panel to generate a report.http://www.swissadme.ch/index.php (accessed on 2 April 2023)). The canonical smiles of the 12 molecules were obtained from the Pubchem database and were added to the web browser and run for the calculation of ADME properties. Further, a boiled-egg plot of the 12 molecules was obtained from the same tool to identify and represent the molecules that had the capability to cross the blood–brain barrier and to be absorbed via the gut.ADME  parameters, pharmacokinetic, drug-like properties, and the medicinal chemistry friendliness of selected phytochemicals were performed using the SWISS ADME tool by the Swiss Institute of Bioinformatics (https://tox-new.charite.de/protox_II/ (accessed on 2 April 2023)) was used. These molecules were classified based on their toxicity ranging from Class I to VI and from fatal to non-toxic depending on the lethal dose calculations, which ranged from an LD50 value lower than 5 (Class I) to an LD50 value of more than 5000 (Class VI) The 6 classes of toxicity ranged from fatal if swallowed to non-toxic.For the estimation of toxicity in small molecules, such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, and immunotoxicity, the ProTox-II tool by The Charite University of Medicine, Berlin, Germany (A. fumigatus. Upon the necessary in vitro and in vivo investigations to ascertain the mechanistic basis of the observed bioactivity and toxicity, Naringin may be an ideal candidate to produce anti-Aspergillus fumigatus treatments and packaging material for foods and food products. Thereby, this could address the dual concerns of spent coffee waste management and antifungal resistance to the available first-line Azole drugs.These findings were made as a part of this study, and earlier reports indicate that Naringin from coffee pulp is a good antifungal candidate that acts via the NDK pathway to inhibit


Acute exacerbations of chronic obstructive pulmonary disease (COPD) with severe hyperglycemia may require insulin to lower glucose levels in people with coexisting type 2 diabetes (T2D) and COPD. We conducted this study to examine the risk of hospitalization for COPD, pneumonia, ventilator use, lung cancer, hypoglycemia, and mortality with and without insulin use in people with T2D and COPD. We adopted propensity-score-matching to identify 2370 paired insulin users and non-users from Taiwan’s National Health Insurance Research Database between 1 January 2000 and 31 December 2018. Cox proportional hazards models and the Kaplan–Meier method were utilized to compare the risk of outcomes between study and control groups. The mean follow-up for insulin users and non-users was 6.65 and 6.37 years. Compared with no insulin use, insulin use was associated with a significantly increased risk of hospitalization for COPD (aHR 1.7), bacterial pneumonia (aHR 2.42), non-invasive positive pressure ventilation (aHR 5.05), invasive mechanical ventilation (aHR 2.72), and severe hypoglycemia (aHR 4.71), but with no significant difference in the risk of death. This nationwide cohort study showed that patients with T2D and COPD requiring insulin therapy may have an increased risk of acute COPD exacerbations, pneumonia, ventilator use, and severe hypoglycemia without a significant increase in the risk of death. Genetic factors, early-life events, infections, smoking, and environmental pollutants are the risk factors for developing chronic obstructive pulmonary disease (COPD) ,2,3. TheInsulin was discovered in 1921 and has been in use to treat and save many people with type 1 diabetes (T1D), with little or no insulin production . It alsoFrom the 1 January 2000 until the 31 December 2018, we identified 28,399 insulin users and 25,592 nonusers among persons with coexisting T2D and COPD. After excluding unsuitable patients, there were 3490 insulin users and 5667 nonusers. Finally, we identified 2370 insulin users and 2370 nonusers through 1:1 matching of the propensity scores .The basic characteristics, comorbidities, and medication were well matched between the study group and the control group with SMD values <0.1 . In the p = 0.3076; In the cohorts that were matched, 357 (15.06%) insulin users and 326 (13.76%) insulin non-users died during the follow-up period (incidence rate: 22.28 versus 21.45 per 1000 person-years). Multivariable analysis showed that the adjusted HR (aHR) for insulin users vs. non-users was 1.08 , bacterial pneumonia , and IMV  were significantly higher in insulin users than in non-users  of insulin use had significantly increased risks of hospitalization for COPD, bacterial pneumonia, and IMV than insulin no-use, and longer cumulative insulin use appeared to increase the risk of these outcomes .This nationwide cohort study demonstrated that patients with coexisting T2D and COPD requiring insulin therapy might increase the risk of hospitalization for COPD, bacterial pneumonia, ventilation use, and severe hypoglycemia; however, it did not increase the risk of death. Longer cumulative insulin use had association with increased risk of hospitalization for COPD, bacterial pneumonia, and invasive mechanical ventilation than insulin no-use.Acute attacks of COPD often require hospitalization and can lead to respiratory failure or death ,2,19. ThPatients with COPD may exhibit alteration of the airway microbiome and impaired airway immune response, thus increasing susceptibility to bacterial infections ,3,19. HyHypoxia or respiratory failure can be a medical emergency in patients receiving COPD treatment  and ofteSmoking is the main cause of lung cancer ,27,28,29Almost all studies on insulin therapy demonstrate the side effect of hypoglycemia ,13,14. PCOPD has been the third leading cause of death worldwide for years ,4. DiabeThis study had several advantages. First, this was a nationwide population-based study. As 99% of Taiwan’s 23 million people are covered by National Health Insurance, the patients enrolled in the study had less selection bias. Second, the study had a large sample size and an observational period of 18 years, which allowed for subgroup analysis and assessment of outcomes that took years to develop. Third, although this was only an observational study, no known randomized insulin studies have been conducted in patients with COPD recently, and the possibility of other randomized studies on insulin use in patients with COPD is low in the future ,8,9. TheOur study had a number of limitations. First, national health administrative data lacked complete information on family history, individual lifestyle, smoking, and alcohol consumption, which could have influenced the findings of our study. In any case, we had well-matched baseline characteristics  and tried to reduce the difference between the study and control groups. Second, this administrative database lacked results of hemoglobin A1C levels, biochemical tests, pulmonary function tests, and imaging methods, which precluded accurate evaluation of T2D and COPD severity. However, we utilized DCSI scores, antidiabetics, and cardiovascular medications as representative factors to assess T2D status and the clinical records of moderate and severe COPD exacerbations to assess COPD severity. Third, patients on insulin therapy often exhibit a long duration of T2D, suboptimal glycemic control, and more comorbidities. Although we considered these factors, unknown residual confounders could bias the results. Fourth, the participants in this study were mainly of Taiwanese descent; therefore, the results may not be applicable to other racial groups. Finally, in a retrospective cohort study, there are always unobserved confounders. Therefore, there is a need for randomized control trials to confirm our findings.We identified patients from the Taiwan’s National Health Insurance Research Database (NHIRD), which was described in our previous study . About 4We identified persons with coexisting T2D and COPD from the NHIRD between 1 January 2000 and 31 December 2018, and followed them until 31 December 2019 . T2D andWe defined the day of simultaneous diagnosis of T2D and COPD as the comorbid date . In our The outcomes of interest in this study were all-cause mortality , hospitalization for COPD, bacterial pneumonia, invasive mechanical ventilation (IMV), noninvasive positive pressure ventilation (NIPPV), lung cancers, and severe hypoglycemia . To calculate the outcomes, we followed patients until the date of death, respective outcomes, or at the end of follow-up on 31 December 2019, whichever came first.We used the Kelsey method and adopted the two-sided significance level of 95%, power of 80%, ratio of sample size between unexposed/exposed of 1, percentage of unexposed with outcome of 5%, and odds ratio of 1.5 to estimate the required exposed and unexposed sample size of 1690 and 1690, respectively . Propenst-tests were used to determine statistical differences in continuous variables and between the study and control groups. To examine outcomes between insulin users and non-users, Cox proportional hazards models with multivariable adjustment were used. Results are presented as hazard ratios (HRs) and 95% confidence intervals (CIs). The Kaplan–Meier method and the log-rank test were used to compare the cumulative incidence of hospitalization for COPD, bacterial pneumonia, and invasive mechanical ventilation between insulin use and non-use. We have also compared the cumulative duration  of insulin use and the risks of hospitalization for COPD, bacterial pneumonia, and invasive mechanical ventilation with insulin non-use. The cumulative duration of insulin was calculated by adding up from the index day to the end day of insulin use during the follow-up time.Chi-squared tests were used to determine statistical differences in categorical variables, and Student’s p-value of less than 0.05. Analyses of this study were performed using SAS .Statistical significance was defined as a two-sided Patients with coexisting T2D and COPD require integrated, patient-centered, and multidisciplinary care. Pitifully, research on treatment strategies is scarce. Insulin may be indicated in diabetic patients with acute exacerbation of COPD to lower blood glucose. Our study has shown that patients with coexisting T2D and COPD requiring insulin therapy was associated with a higher risk of hypoglycemia, hospitalization for COPD, bacterial pneumonia, and ventilator use. However, we should be aware that this could be the result of the patients’ intrinsic severe metabolic decompensation, which led to the previous administration of insulin. Insulin administration requires effective doctor–patient communication for careful monitoring of hypoglycemia, pneumonia, acute exacerbation of COPD, and respiratory failure with immediate and appropriate disposal.


To develop and validate machine learning models for predicting COVID-19 related hospitalization as early as CDC contact tracing using integrated CDC contact tracing and South Carolina medical claims data.Using the dataset , we identified 3,305 patients with COVID-19 and were captured by contact tracing. We developed and validated machine learning models , followed by multi-level validations and pilot statewide implementation.Using 10-cross validation, random forest outperformed other models , followed by XGBoost (F1=0.845 and 0.682) and support vector machine (F1=0.845 and 0.644). We identified new self-reported symptoms from contact tracing  that are highly predictive of hospitalization.Our study demonstrated the feasibility of identifying individuals at risk of hospitalization at the time of contact tracing for early intervention and prevention.Our findings demonstrate existing promise for leveraging CDC contact tracing for establishing a cost-effective statewide surveillance and generalizability for nationwide adoption for enhancing pandemic preparedness in the US. According to the World Health Organization (WHO) COVID Dashboard globally, the number of deaths is 6.9 million2. Among the severity levels of SARS-CoV-2 infection 3, COVID-19 associated hospitalization exhibited the highest mortality rate, ranging from 20.7% (March 2020) to 2.7% (April 2022), according to the analysis by the National Center for Health Statistics (NCHS).1 Existing therapeutics is most effective for individuals at early stage of disease. Data from clinical trials suggested that a number of antiviral agents show the most therapeutic benefits for patients who have not required hospitalization and Intensive Care Unit (ICU); and that combined antiviral agents  must be administrated at the earliest stage of infection to show therapeutic benefits 4.As of May 30, 2023, COVID-19 has claimed 1.1 million deaths in the United States (US), according to the COVID-19 Data Tracker administrated by the US Centers for Disease Control and Prevention (CDC)6, severity levels 7, and mortality 8 using in-hospital records in conjunction with historical medical records.These findings confirmed a crucial role of early detection of at-risk individuals in order to provide timely medical treatment and proactive intervention. Nevertheless, early detection of individuals who are at risk of disease progression and hospital admission remains an understudied and unaddressed research question. A handful of studies have demonstrated the feasibility of predicting COVID-19 progression 4. A recent systematic review of 731 studies focusing on predictive models for COVID-19 diagnosis and clinical progression and outcomes suggested overly optimistic performance but limited value for clinical practices. One of the root causes is the fact that the selection of predictive model input and output was often determined by data that are available rather than data that are clinically meaningful for medical specialists and public health practitioners 9.Despite robust methodology, such approaches may be limited in terms of clinical usability and plausibility if the prediction cannot identify at-risk individuals early before irreversible adverse disease progression 10. With respect to predictive modeling, the earlier the risk factors can be identified for predicting hospitalization the more valuable such a model would be for early intervention. However, this approach has been limited by the lack of data prior to a recorded hospital admission . An important source of data is contact tracing records administrated by CDC. In the United States and many other countries, contact tracing has been a key public health surveillance and base of evidence for timely public health intervention and strategies 12. However, data collected from contact tracing have not been successfully used for making prediction for at-risk individuals, in part due to limited variables from contact tracing and the nature of contact tracing data being self-reported. Studies using healthcare utilization data alone, such as electronic health records (EHR), can be used to make prediction of severe COVID-19 outcomes at point of hospital admission 13, but such predictive models are limited in interpreting risk factors outside of EHR . We argue that a better practice would be an integration of medical records together with contact tracing, which could harness the power of contact tracing by detecting risk factors for severe disease outcomes as early as at the time of exposure an individual to COVID-19 . However, such data integration has been challenging due to gaps with respect to the Health Insurance Portability and Accountability Act (HIPAA) restrictions, state data privacy regulations, privacy-preserving record linkage (PPRL) techniques 14, uneven testing supply by geographic areas, and inconsistent contact tracing coordination in local states 15.A predictive model to identify individuals at risk of hospitalization is highly valuable for effective early therapeutics and intervention, as well as optimization of healthcare resources. These predictive models are potentially valuable for the public health preparedness of future pandemics 10.In this study, we reported design, development, multi-level evaluation of machine learning (ML) predictive models that can identify individuals who would progress to COVID-19 associated hospitalization at the time of contact tracing administration or earlier. We used a National Institute of Allergy and Infectious Diseases (NIAID) funded state-wide surveillance dataset that incorporates medical claims and contact tracing administrated through South Carolina (SC) Department of Health and Environmental Control (DHEC). The overarching goal of this study responds to the NIAID’s plan for pandemic preparedness, aiming for expanding the pre-clinical infrastructure capacity [1] An in-hospital death is defined by an encounter with a discharge status of died or died in a medical facility. Data are UB-04 administrative claims data, not nationally representative.22.1Funded by NIAID, we used a multi-center dataset that integrates SC outpatient and inpatient medical claims data and CDC contact tracing data , with which the data are linked at the individual level by using unified patient IDs determined by SC Revenue and Fiscal Affairs (RFA). Using this dataset, we were able to identify individuals who have COVID-19 related hospitalization and were previously captured by contact tracing between March 01, 2020, and December 31, 2020, and retrieved their sociodemographic data and medical history from two years before the time of the identified hospitalization .To establish a cohort of interest, we used the following inclusion criteria 1) individuals who had hospitalization records from medical claims, 2) individuals who were previously captured by contract tracing. The only exclusion criterion was a hospitalization dated before contact tracing records. All medical claims data from two years before a hospitalization record were retrieved.2.2Based on the linked contact tracing and medical claims data, we split the cohort into two datasets, which then became two different ML models with different outputs. For one dataset, the output of ML model was determined by whether a patient was hospitalized after contact tracing, namely, “general hospitalization set”. For another dataset, the output of the ML model was determined by whether a patient was hospitalized caused by COVID-19, namely, “COVID-19 related hospitalization set”. COVID-19 related hospitalization is defined as the hospitalization of patients with a documented positive COVID-19 record using ICD-10 code U07.1.After data cleaning, there were 941 features. Sex was represented by female or male. There were seven classes for the race/ethnicity including non-Hispanic White, non-Hispanic Black, Hispanic/Latino, non-Hispanic Asian, non-Hispanic American Indian, other race, and missing value, corresponding to data collected from contact tracing. Age was divided into six age groups:18–29, 30–39, 40–49, 50–59, 60–69, and 70 or older. From medical claims data, the insurance payor variable was categorized into nine groups: out-of-pocket (self-pay), Medicare, Medicaid, commercial insurance, workers compensation, indigent/charitable organization, other government, Health Maintenance Organization (HMO), and not stated. The location variables included 46 counties in SC, representing the residential location of a patient. The symptom variables extracted from the PUI form included nasal congestion, fatigue, fever, chills, myalgia, running nose, sore throat, cough, breathing difficulty, etc., and medical conditions extracted from medical claims data including primary hypertension, type 2 diabetes mellitus without complications, morbid (severe) obesity due to excess calories, short of breath, etc. It is worth noting that some symptoms from contact tracing overlap with those from medical claims data. For example, cough, fever, and pneumonia appeared in both contact tracing and medical claims data, but were treated as different features in the models because they were extracted from different sources and using different data collection methods. For example, symptoms from contact tracing are self-reported whereas symptoms from medical claims are documented by providers. There were 25 symptom variables extracted from contact tracing and 847 medical conditions from medical claims data.We applied supervised ML models to determine predictors of hospitalizations using algorithms including Random Forest (RF) (max depth = 10), Support Vector Machine (SVM) , and XGBoost (XGB) (max depth = 10). Selection of algorithms represents a collection of well performed algorithms that have been tested on patient data . To retain the generalizability of tested machine learning models, we only did not alter algorithms other than their parameters.2.3We developed a stream of approaches to validate the models including mathematical and clinical validation. The mathematical validation consisted of the following steps: First, we used 10-fold cross-validation as validation framework. Using this method, we randomly created 10 splits of data. In each of the 10 iterations, nine splits of data were used for model training and the remaining one split was used for testing, without overlapping with any other iterations. Second, the performance of the models was evaluated using the area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score. Third, we examined the feature importance using SHapley Additive explanations (SHAP).The clinical validation included two major steps. Step one was designed to validate the consistency between machine and clinical experts on individual ML features. Based on the SHAP results, two clinical experts (SW and ND) independently rated the top 20 significant features identified by SHAP using a Likert scale of  for each set of models. Being rated as −5 meant that the feature was very likely to be contributing to non-hospitalization; and being rated as 5 meant that the feature was very likely to be contributing to hospitalization. The robustness of the two ranked lists from the experts were assessed by the Content Validity Index (CVI). Then, we calculated the mean values between the ratings of the features from the two experts and scale the corresponding SHAP values to , and compared the mean rating to the scaled SHAP values by Spearman’s rank correlation coefficient and Mann-Whitney U test. Step two was designed to test to what extent clinical experts’ prediction is consistent with machine’s prediction  upon reviewing a masked real-world patient case from our dataset. We randomly selected five cases from each quadrant of the performance matrix  for each set of models. The narrative summary of a case consisted of machine ranked top-20 features, then polished by ChatGPT for better linguistic nuances. These cases were assigned to the two clinical experts, and each case was rated independently based on a Likert scale of , with 0 representing least likely and 5 representing most likely that the patient would be hospitalized. The interrater reliability was evaluated by CVI. Then we calculated the mean of rating for each quadrant between the two experts. We considered the quadrant to be negatively correlating to machine prediction if the mean value was smaller than or equal to 2.5 and to be positively correlating to machine prediction if the mean value was greater than 2.5. Supplement 1 demonstrates the Likert scale design for both steps of clinical evaluation as well as narrative summaries of cases.3In a total of 82,073 individuals in our dataset, we identified 3,305 patients who had COVID-19 and were previously captured by contact tracing. Among these patients, 2,022 individuals were at least admitted into the inpatient setting once and 217 patients had at least one COVID-19 related hospitalization. Among all the patients, female patients accounted for 60% and male patients accounted for 40%, with similar distribution when being stratified by COVID-19 infection. White patients were the largest racial group in the cohort (54.6%), followed by Black patients (37.1%), Hispanic/Latino patients (4.9%), and other races. In COVID-19 positive group, Black patients accounted for 52.4%, followed by White patients (30.0%), Hispanic/Latino patients (11.9%), and other races. Two sets of ML models were fitted for making predictions, where each set was trained using three algorithms . The first set  of models specified whether the patient had been admitted to the hospital as the output , while the second set  of models specified whether the patient had COVID-19 related hospitalization as the output (n = 217 for positive). These 2,022 and 217 individuals, respectively, were matched with control cases at a ratio of 1:1 to balance the data in each set of models. With 10-cross validation, RF outperformed other models , followed by XGB  and SVM . See Among patients admitted to inpatient settings during the COVID-19 pandemic , our findings revealed that the most significant predictors of hospitalization are from the contact tracing data, which included breathing difficulty, fever, pneumonia, cardiovascular disease, cough, chronic obstructive pulmonary disease (COPD), renal disease, and other respiratory conditions . NotablyFor patients who experienced COVID-19 related hospitalization , features from medical claims data played a more prominent role than the other set . The topResults from step one clinical evaluation reached a CVI of 0.7 for general hospitalization set and 0.9 for COVID-19 related hospitalization set, indicating a fair to good agreement. We set a significance level of 0.05 for the tests. The Spearman’s rank correlation coefficients between the mean rating and the scaled SHAP values for the top 20 features were 0.57 with a p-value of 0.009 and 0.52 with a p-value of 0.019 correspondingly, suggesting a significant positive correlation. The Mann-Whitney U test statistics were 199.0 with a p-value of 0.998 and 191.5 with a p-value of 0.828 correspondingly, indicating there was no significant difference between the two ranked lists.For the step two clinical evaluation, the mean values for FN, FP, TN, and TP were 1.7, 4.1, 2.1, and 3.8, respectively, for the general hospitalization set, which are in alignment with the ML results. The mean values for FN, FP, TN, and TP were 2.5, 2.6, 2.6, and 3.2, respectively, for the COVID-19 related hospitalization set, which remain in alignment with the ML results, but the effect is weaker than the other data set.16. Generally, we found that some of the data captured by contact tracing are highly predictive of COVID-19 related hospitalization such as self-reported fever, pneumonia, breathing difficulty, cough, cardiovascular disease, and vomiting. These data were self-reported in this study but in the real world we have ample opportunities to capture these data. For example, cardiovascular disease could be found in contact tracing data, historical claims data, and EHR data  reviewed these predictors and organized them into several healthcare systems where public health practitioners or clinicians can respond as early as these predictors are detected in individuals. EHR data . In addi44.1This study is among the first to integrate individual-level contact tracing data with statewide medical claims to identify individuals who will be hospitalized due to COVID-19 at the time of contact tracing. Our study demonstrated the feasibility of leveraging CDC contact tracing for early detection of at-risk individuals with exposure to COVID-19 in South Carolina, and the potential for nation-wide implementation.In this study, we tuned and implemented three well-performed ML algorithms  on two sets of SC data: the general hospitalization set, and the COVID-19 hospitalization set. In general, RF models performed the best with an F1 score of 0.872 for the general hospitalization set and 0.763 for the COVID-19 hospitalization set. Although both F measures indicate good predictive performance, the small difference between the two data sets suggest challenges in predicting COVID-19 related hospitalization as comparing to hospitalization following exposure to COVID-19 but may or may not be clinically attributed to SARS-CoV-2 infection. The most significant predictors for individuals progressing to hospitalization were breathing difficulty, pneumonia, fever, cardiovascular disease, higher age (70 or older), cough, primary hypertension, being Black race, type 2 diabetes, COPD, renal disease, and other respiratory conditions. In contrast, symptoms such as fatigue, congestion, headache, and taste loss, and factors such as being younger age (18–29), commercial insurance coverage, and being White race were associated with a lower likelihood of hospitalization. The most significant predictors for COVID-19 related hospitalization included comorbidities and symptoms such as fever, cough, shortness of breath, pneumonia, type 2 diabetes mellitus, breathing difficulty, cardiovascular disease, primary hypertension, vomiting, and obesity. Individuals of Black race had a higher risk of COVID-19 related hospitalization. In contrast, fatigue, congestion, and White race indicated a lower likelihood of COVID-19 related hospitalization.In both the general hospitalization set and the COVID-19 hospitalization set, certain features consistently emerged as significant indicators of hospitalization. These features include breathing difficulty, pneumonia, fever, cough, primary hypertension, and type 2 diabetes mellitus. Such findings suggest that these factors are consistently associated with a higher risk of severe illness and the need for hospitalization among individuals with COVID-19 infection during the pandemic. Either providers or other clinicians should be alarmed for early intervention  when one or more of these signals in self-reported contact tracing data, healthcare utilizations, or other possible data sources. For both models, we found several self-reported symptoms such as fatigue and congestion are strongly predictive of non-hospitalization, which have not been previously reported. The findings of this study need confirmation with prospective evaluation. If the model remains robust in a prospective study this has the potential to be used by SC DHEC and healthcare systems for screening individuals into different risk categories as early as contact tracing data are obtained. However, we should be careful when interpreting these findings. Fatigue and congestion are mild conditions and sometimes can progress to severe symptoms, meaning that without further clinical examination they should not be regarded as independent predictive factors for non-hospitalization.4.217. Our findings demonstrated the unique advantages of contact tracing data for early identification of at-risk individuals, suggesting existing potentials for public health implementation. However, to enable such predictive analysis, contact tracing data needs to be linked with healthcare utilization and medical records  at individual level to enrich the data fed to predictive models. In this regard, the S3C dataset has successfully demonstrated the benefits of this approach.There have been debates for the effectiveness of contact tracing in measuring and containing COVID-19 for reasons such as timeliness of data reporting and unequal deployment due to different healthcare recourse by geographic locationsOur study demonstrated a new approach to establishing a cost-effective surveillance system in South Carolina. Establishing a surveillance for either clinical or public health setting would be costly given the exceeding large population base with exposure to COVID-19 in the US. Our study demonstrated how machine-learning models can be advantageous and less costly for making predictions. First, because machine learning models are not assumption based, they can identify highly predictive features without assuming candidate features . Second, because machine learning models are sustainable for high dimensional data, they are competent for processing exceedingly large number of variables, as is the norm in large datasets especially when combining dataset such as contact tracing and medical records.The capability of proactively identifying at-risk individuals as early as contact tracing demonstrates the potential for clinical implementation. This unique advantage not only benefits individuals by ensuring timely care but also preserves the limited healthcare resources during the pandemic by optimizing safety measures and interventions towards for those with urgent medical needs. For example, if an individual exhibits high-risk features identified in this study through historical claims data or EHR, proactive measures can be taken by social workers or other healthcare workers to assess the individual’s health status by telehealth measures, which would reduce the demand for healthcare resources and the risk of community spread. By promptly initiating patient outreach with these individuals, proactive interventions and timely therapeutics can be put into action to mitigate the likelihood of hospitalization.4.3There are some limitations to the presented study. First, because of the data quality for contact tracing and medical claims, we removed 60% of missing values, which reduced the sample size and may exclude a few predictive features with too many missing values. Second, COVID-19 related hospitalization is identified by medical codes, which may have missed clinical visits that have not been appropriately coded. Third, we did not test external validity in this study, which would limit the study findings to be generalizable for different healthcare systems and/or datasets. Despite limitations, this study demonstrates how CDC contact tracing when integrated with state-level healthcare utilization data can be used to predict COVID-19 related hospitalizations at the time of contact tracing, which encourages research opportunities for statewide implementation and nationwide generalizability validation.


Colorectal cancer (CRC) is a common cancer among both men and women and is one of the leading causes of cancer death worldwide. It is important to identify risk factors that may be used to help reduce morbidity and mortality of the disease. We used a case-control study design to explore the association between CRC, polygenic risk scores (PRS), and other factors. We extracted data about 2,585 CRC cases and 9,362 controls from the UK Biobank, calculated the PRS for these cases and controls based on 140 single nucleotide polymorphisms, and performed logistic regression analyses for the 11,947 cases and controls, for an older group (ages 50+), and for a younger group (younger than 50). Five significant risk factors were identified when all 11,947 cases and controls were considered. These factors were, in descending order of the values of the adjusted odds ratios (aOR), high PRS , male sex , unemployment , family history of CRC , and age . These five risk factors also remained significant in the older group. For the younger group, only high PRS  and family history of CRC  were significant risk factors. These findings indicate that genetic risk for the disease is a significant risk factor for CRC even after adjusting for family history. Additional studies are needed to examine this association using larger samples and different population groups. Colorectal cancer (CRC) represents a significant global health challenge. It is the third most common malignancy and the second leading cause of cancer-related deaths worldwide , 2. The Although genetic factors are known to play an important role in the risk associated with CRC, genetic data were rarely combined with other factors in the analyses reported in the literature. The current study fills this research void by exploring the association between genetic factors and CRC risk, along with other relevant factors such as family history, sociodemographic, socioeconomic, and lifestyle factors. Additionally, most CRC studies reported in the literature focused on people aged 50 years or older –16. GiveResearchers have extensively utilized various datasets in their analysis of CRC, including the Genetics and Epidemiology of Colorectal Cancer Consortium, the Colon Cancer Family Registry, and the Colorectal Transdisciplinary Study –23. HoweWe used data from the UK Biobank, a population-based cohort study that collected blood samples from over 500,000 adults aged 40–70 years between 2006 and 2010, primarily across England, Scotland, and Wales. Samples underwent genotyping from blood derived cells using two arrays with a shared 95% marker content: the UK BiLEVE Axiom  and the UK Biobank Axiom . Genotype imputation was performed using reference panels from the Haplotype Reference Consortium, UK10K, and 1000 Genomes phase 3. In the biobank, 487,409 samples had imputed genotyping data available for this study. In addition to genetic data, the biobank also contains imaging data, health-related data, as well as sociodemographic and socioeconomic details for each participant. All participants were coded to protect their privacy.2. The IMD score was measured from seven distinct domains: income deprivation, employment deprivation, health deprivation and disability, education skills and training deprivation, barriers to housing and services, living environment deprivation, and crime.In this study, we incorporated the following individual level data from the UK Biobank: family history, age, sex, body mass index (BMI), index of multiple deprivation (IMD), current tobacco smoking status, maternal smoking around birth, alcohol intake frequency, qualifications of education, current employment status, number of vehicles in the household, and average total house income. Family history of CRC, whether the father, mother, and siblings had CRC or not, is also available for this study. The units of BMI measurement were kg/mThe income deprivation domain examines income-related deprivation by counting individuals with low income across five indicators, such as those in income support families or receiving income-based Jobseeker’s allowances. The employment deprivation domain focuses on labor market exclusion, combining indicators like Jobseeker’s allowance claimants and Incapacity Benefit claimants. The health deprivation and disability domain evaluates premature mortality and reduced quality of life due to poor physical and mental health. The education skills and training deprivation domain assesses educational disadvantages for both children and adults. The barriers to housing and services domain considers geographical and financial obstacles to accessing housing and local services. The living environment deprivation domain evaluates indoor and outdoor living conditions. Lastly, the crime domain examines the recorded crime rate, including violence, burglary, theft, and criminal damage, as a reflection of personal and material victimization risk at a local level.We dichotomized the variables based on their nature . For exaWe selected the CRC cases from the Biobank based on ICD-10 codes of C18.0-C18.9, C19, C20, and C26.0. Given that the majority of individuals in the genome-wide association study (GWAS) are of European ancestry, and considering the linkage disequilibrium (LD), allele frequency and gene-environment differences between populations, we included only samples of participants who are White British with complete imputed genotype information in the analyses . IndividEthics approval was not required for this study because UK Biobank data is open to all researchers, and the data has been de-identified. We did not have access to any information that could identify individual participants during or after data collection.We calculated the PRS using 140 risk single nucleotide polymorphisms (SNPs) identified in a case-control study of CRC conducted by Thomas et al. . This stWe computed the odds ratios (OR) using logistic regression analysis based on a case-control study design for the cases and controls in the final dataset. The variables included in the analysis were age, BMI, IMD, sex, PRS, family history, current tobacco smoking, alcohol intake frequency, household income, number vehicles in the household, maternal smoking around birth, education, and employment. In addition, we divided the cases and controls into two groups: the older group, consisting of participants who were 50 years of age or older, and the younger group, who were less than 50 years old. The older group contained 2,387 cases and 8,579 controls and the younger group included 198 cases and 783 controls. We performed logistic regression analysis to compare the influence of the risk factors between these two age groups.When performing the logistic regression analyses, we conducted univariate analysis to explore the impact of each variable on CRC individually. Additionally, we conducted multivariate analysis with all variables included in the model and compared the results with the univariate model. To examine how the results would differ when family history is excluded from the analysis and when only participants with top 5% and middle 41–60% PRS are considered, we conducted the same statistical analysis process on two sub-datasets selected from the current dataset: one comprising 10,142 participants without a family history of CRC, and the other consisting of participants with top 5% and middle 41–60% PRS.Among 11,947 participants with complete data used in this study, more than half of them  were female (50.8%), especially in the younger group (54.6%). The BMI values ranged from 15.27 to 54.52, with a standard deviation of 4.5; the mean and median BMI were 27.2 and 26.7, respectively. The IMD values ranged from 0.82 to 81.07, with a standard deviation of 12.1; the mean and median IMD were 14.5 and 10.8, respectively. The older group had a slightly higher proportion of participants with less than a university education (59.8% vs 57.8%), a family history of CRC (15.3% vs 12.8%), and a significantly higher proportion of participants who drank daily (26.5% vs 17.0%) and had household incomes below the poverty level (20.2% vs 8.5%), compared to the younger group . ConversA brief examination of the data indicates that: (1) among the 2,585 CRC cases, 251 participants (9.7%) had a high PRS, and 2,334 participants (90.3%) had a low PRS; (2) among the 9,362 controls, 355 participants (3.8%) had a high PRS, and 9,007 participants (96.2%) had a low PRS. A higher proportion of participants with a high PRS was observed in the case group compared to that in the control group. A two-proportions z-test (α = 0.05) indicates that the difference between these two observed proportions is significant .Results of the analysis involving all 11,947 cases and controls revealed five significant risk factors. These five factors are, in descending order of the values of the adjusted odds ratios (aOR), high PRS , male , unemployment , family history of CRC , and age  . These fTo better understand the association between PRS and CRC, we analyzed a sub-dataset from the original case-control dataset, which included 10,142 participants without a family history of CRC. Among these participants, 9,287  are in the older group, and 855 (161 cases and 694 controls) are in the younger group. Tables Furthermore, we analyzed a sub-dataset extracted from the original case-control dataset to compare it with prior findings, which included participants with a top 5% and middle 41–60% PRS, both with and without a family history of CRC. The sub-dataset that consisted of individuals with a family history of CRC comprised 2,988 participants, with 729 cases and 2,259 controls. The sub-dataset without a family history of CRC comprised 2,537 participants, with 590 cases and 1,947 controls. In the older group with a family history of CRC, there were 667 cases and 2,079 controls, whereas in the older group without a family history of CRC, there were 541 cases and 1,791 controls. Tables Findings from this study suggest that a high PRS is a potential risk factor associated with CRC, regardless of whether individuals are older than 50 or younger. In addition, results from this study indicate that the risk for people younger than 50 with a PRS in the top 5% to develop CRC is 3.65 times greater than those whose PRS falls within the other 95%. This relative risk is higher than that for people without a family history of CRC compared to those with a family history of CRC. It is worth noting that high PRS had a higher odds ratio than family history of CRC based on the results of all logistic regression analyses. These findings have implications for the implementation of CRC screening programs aimed at preventing CRC or detecting it at an early stage. We suggest that additional research is needed to evaluate the findings, and we recommend that individuals with a high PRS should consider participating in CRC screening, even if they do not have a family history of CRC. In addition, our results demonstrate that while family history encompasses some form of genetic disease risk, having additional information from the PRS adds to risk stratification.th quantile), the top 5% weighted PRS, had a four-fold greater risk of developing CRC compared to those in the reference quantile (the 10th quantile), the middle 46% -50% weighted PRS.Previous studies have suggested that PRS is associated with CRC and has a stronger impact on early-onset CRC. Archambault et al. used 95 CRC-associated SNPs to study whether a PRS was associated with the risk of early-onset CRC . Their rJia et al. used risk variants to identify high-risk individuals for eight common cancers. The results showed that individuals with the highest 5% PRS had a two-to-three-fold elevated risk for developing CRC . Ping etOther previous studies have examined the association between PRS and CRC along with various risk factors, including lifestyle , 30, phyStudies also evaluated whether a healthy lifestyle can offset increased genetic risk in CRC , 40. HeaOne strong aspect of our study is that it incorporates PRS with several other relevant factors compared to previous studies reported in the literature. These factors included sociodemographic, socioeconomic, lifestyle, and family history of CRC. Findings from this study hence fill some gaps in the literature. A limitation of our study is the small sample size for participants younger than 50 years old. The limited number of young participants in the study may account for the observed results, where only family history and PRS remain as statistically significant risk factors, while sex, employment status, and age lose their significance within the younger age group. Further investigation is warranted to validate these findings once a larger dataset of younger participants becomes available. The results are limited to British Whites only, as PRS calculations based on the identified risk SNPs from previous GWAS primarily involved individuals of European ancestry. Future research endeavors should examine whether the results would hold in other population groups based on large sample sizes.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.