The treatment of SARS-CoV2 with antivirals and mitigation of the cytokine storm syndrome: the role of gene expression
Bibliographic record
Abstract
In the absence of a vaccine, the treatment of SARS-CoV2 has focused on eliminating the virus with antivirals or mitigating the cytokine storm syndrome (CSS) that leads to the most common cause of death: respiratory failure. Herein we discuss the mechanisms of antiviral treatments for SARS-CoV2 and treatment strategies for the CSS. Antivirals that have shown in vitro activity against SARS-CoV2, or the closely related SARS-CoV1 and MERS-CoV, are compared on the enzymatic level and by potency in cells. For treatment of the CSS, we discuss medications that reduce the effects or expression of cytokines involved in the CSS with an emphasis on those that reduce IL-6 because of its central role in the development of the CSS. We show that some of the medications covered influence the activity or expression of enzymes involved in epigenetic processes and specifically those that add or remove modifications to histones or DNA. Where available, the latest clinical data showing the efficacy of the medications is presented. With respect to their mechanisms, we explain why some medications are successful, why others have failed, and why some untested medications may yet prove useful.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".