Anti-Inflammatory Properties of Drugs Used to Control COVID-19 and their Effects on the Renin-Angiotensin System and Angiotensin-Converting Enzyme-2
Bibliographic record
Abstract
COVID-19 infection is associated with systemic inflammation, and sometimes hyperinflammatory responses with cytokine storm. This plays a major role in COVID-19 severity and poor disease prognosis, even death. Higher levels of inflammatory hallmarks including C-reactive protein, ferritin, D-dimers, and cytokines such as interleukin (IL) -6, IL-10 and tumor necrosis factor- α (TNF-α) have been reported. Many anti-viral drugs have been tried, but none were proven fully effective. Supportive care and management of the complications that are caused mainly by inflammation might be the key to greater survival rates and shorter hospitalization (e.g., the use of remdesivir, lopinavir, ritonavir, umifenovir (arbidol), oseltamivir, ganciclovir, favipiravir, darunavir, hydroxychloroquine, chloroquine, colchicine, azithromycin, anakinra, canakinumab, tocilizumab, siltuximab, sarilumab, Type 1 interferon, interferon β-1a, interferon α- 2b, baricitinib, ruxolitinib, fedratinib, methylprednisolone and dexamethasone). However, the efficacy of these treatments still needs well-planned clinical trials. In such trials, careful attention must be paid to the duration of the treatment, the onset of beneficial effects, and the severity of the disease, otherwise, the outcomes may still remain inconclusive. Herein, we present a review of the current drugs, which are being used in the management of the disease and their anti-inflammatory properties. We also investigated if these drugs directly interact with Angiotensin-Converting Enzyme (ACE 2), which is a crucial component of the virus entry to the cells.
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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 | 0.001 |
| 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 | 0.002 |
| 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".