Cholesterol in Relation to COVID-19: Should We Care about It?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Current data suggest that infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing corona virus disease-19 (COVID-19) seems to follow a more severe clinical course in patients with cardiovascular disease (CVD), hypertension, and overweight/obesity. It appears that lipid-lowering pharmacological interventions, in particular statins, might reduce the risk of cardiovascular complications caused by COVID-19 and might potentially have an additional antiviral activity. It has been shown that high cholesterol levels are associated with more lipid rafts, subdomains of the plasma membrane that can harbour angiotensin-converting enzyme 2 (ACE2) receptors for the S-protein of SARS-CoV-2. Evidence of the importance of cholesterol for viral entry into host cells could suggest a role for cholesterol-lowering therapies in reducing viral infectivity. In addition to their lipid-lowering and plaque-stabilisation effects, statins possess pleiotropic effects including anti-inflammatory, immunomodulatory, and antithrombotic activities. Lower rates of mortality and intubation have been reported in studies investigating statin therapy in influenza infection, and statin therapy was shown to increase viral clearance from the blood during chronic hepatitis C infection. Statins may also serve as potential SARS-CoV-2 main protease inhibitors, thereby contributing to the control of viral infection. In this review, we elaborate on the role of cholesterol level in the process of the coronavirus infection and provide a critical appraisal on the potential of statins in reducing the severity, duration, and complications of COVID-19.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.822 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.012 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it