Effect of Common Medications on the Expression of SARS‐CoV‐2 Entry Receptors in Kidney Tissue
Why this work is in the frame
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Bibliographic record
Abstract
Besides the respiratory system, severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection was shown to affect other essential organs such as the kidneys. Early kidney involvement during the course of infection was associated with worse outcomes, which could be attributed to the direct SARS-CoV-2 infection of kidney cells. In this study, the effect of commonly used medications on the expression of SARS-CoV-2 receptor, angiotensin-converting enzyme (ACE)2, and TMPRSS2 protein in kidney tissues was evaluated. This was done by in silico analyses of publicly available transcriptomic databases of kidney tissues of rats treated with multiple doses of commonly used medications. Of 59 tested medications, 56% modified ACE2 expression, whereas 24% modified TMPRSS2 expression. ACE2 was increased with only a few of the tested medication groups, namely the renin-angiotensin inhibitors, such as enalapril, antibacterial agents, such as nitrofurantoin, and the proton pump inhibitor, omeprazole. The majority of the other medications decreased ACE2 expression to variable degrees with allopurinol and cisplatin causing the most noticeable downregulation. The expression level of TMPRSS2 was increased with a number of medications, such as diclofenac, furosemide, and dexamethasone, whereas other medications, such as allopurinol, suppressed the expression of this gene. The prolonged exposure to combinations of these medications could regulate the expression of ACE2 and TMPRSS2 in a way that may affect kidney susceptibility to SARS-CoV-2 infection. Data presented here suggest that we should be vigilant about the potential effects of commonly used medications on kidney tissue expression of ACE2 and TMPRSS2.
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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 | 0.003 | 0.027 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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