Susceptibility to Metabolic Diseases in COVID-19: To be or Not to be an Issue
Bibliographic record
Abstract
Despite the passage of more than 17 months from the beginning of the COVID-19 pandemic, challenges regarding the disease and its related complications still continue in recovered patients. Thus, various studies are underway to assay the long-term effects of COVID-19. Some patients, especially those with severe symptoms, experience susceptibility to a range of diseases and substantial organ dysfunction after recovery. Although COVID-19 primarily affects the lungs, multiple reports exist on the effect of this infection on the kidneys, cardiovascular system, and gastrointestinal tract. Studies have also indicated the increased risk of severe COVID-19 in patients with diabetes. On the other hand, COVID-19 may predispose patients to diabetes, as the most common metabolic disease. Recent studies have shown that Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) binds to Angiotensin-Converting Enzyme 2 (ACE2) receptors, which are expressed in the tissues and organs involved in regulating the metabolic status including pancreas, adipose tissue, gastrointestinal tract, and kidneys. Therefore, SARS-CoV-2 may result in metabolic disturbance. However, there are still many unknowns about SARS-CoV-2, which are required to be explored in basic studies. In this context, special attention to molecular pathways is warranted for understanding the pathogenesis of the disease and achieving therapeutic opportunities. Hence, the present review aims to focus on the molecular mechanisms associated with the susceptibility to metabolic diseases amongst patients recovered from COVID-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".