Cardiovascular medications and regulation of COVID-19 receptors expression
Bibliographic record
Abstract
INTRODUCTION: Emerging epidemiological studies suggested that Renin-Angiotensin-Aldosterone system (RAAS) inhibitors may increase infectivity and severity of COVID-19 by modulating the expression of ACE2. METHODS: In silico analysis was conducted to compare the blood expression levels of SARS-CoV-2 entry genes between age and gender matched cohort of hypertensive patients versus control, and to determine the effect of common cardiovascular medications on the expression of COVID-19 receptors in vitro using primary human hepatocytes. RESULTS: The transcriptomic analysis revealed a significant increase of ACE2 and TMPRSS2 in the blood of patients with hypertension. Treatment of primary human hepatocytes with captopril, but not enalapril, significantly increased ACE2 expression. A similar pattern of ACE2 expression was found following the in vitro treatments of rat primary cells with captopril and enalapril. Telmisartan, a second class RAAS inhibitors, did not affect ACE2 levels. We have also tested other cardiovascular medications that may be used alone, or in combination with RAAS inhibitors. Some of these medications increased TMPRSS2, while others, like furosemide, significantly reduced COVID-19 receptors. CONCLUSIONS: The increase in ACE2 expression levels could be due to chronic use of RAAS inhibitors or alternatively caused by other hypertension-related factors or presence of other comorbidities. Treatment of common co-morbidities often require chronic use of multiple medications, which may result in an additive increase in the expression of ACE2 and TMPRSS2. Our data suggest that more research is needed to determine the effect of different medications, as well as medication combinations, on COVID-19 receptors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".