Abstract: The COVID-19 pandemic, caused by the coronavirus, SARS-CoV-2, has claimed millions of lives worldwide in the past two years. Fatalities among the elderly with underlying cardiovascular disease, lung disease, and diabetes have particularly been high. A biometrics analysis on author’s keywords was carried out, and searched for possible links between various coronavirus studies over the past 50 years, and integrated them. We found keywords like immune system, immunity, nutrition, malnutrition, micronutrients, exercise, inflammation, and hyperinflammation were highly related to each other. Based on these findings, we hypothesized that the human immune system is a multilevel super complex system, which employs multiple strategies to contain microorganism infections and restore homeostasis. It was also found that the behavior of the immune system is not able to be described by a single immunological theory. However, one main strategy is “self-destroy and rebuild”, which consists of a series of inflammatory responses: 1) active self-destruction of damaged/dysfunctional somatic cells; 2) removal of debris and cells; 3) rebuilding tissues. Thus, invading microorganisms’ clearance could be only a passive bystander response to this destroy–rebuild process. Microbial infections could be self-limiting and promoted as an indispensable essential nutrition for the vast number of genes existing in the microorganisms. The transient nutrition surge resulting from the degradation of the self-destroyed cell debris coupled with the existing nutrition state in the patient may play an important role in the pathogenesis of COVID-19. Finally, a possible coping strategies to mitigate COVID-19, including vaccination is also discussed.
Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)
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Abstract Motivation: Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction. Results: Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance. Availability and Implementation: We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and non-profit usage at http://jjwanglab.org/PRVCS. Contact: wang.junwen@mayo.edu, jliu@stat.harvard.edu, or limx54@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.