p38 MAPK inhibition: A promising therapeutic approach for COVID-19
Bibliographic record
Abstract
COVID-19, caused by the SARS-CoV-2 virus, is a major source of morbidity and mortality due to its inflammatory effects in the lungs and heart. The p38 MAPK pathway plays a crucial role in the release of pro-inflammatory cytokines such as IL-6 and has been implicated in acute lung injury and myocardial dysfunction. The overwhelming inflammatory response in COVID-19 infection may be caused by disproportionately upregulated p38 activity, explained by two mechanisms. First, angiotensin-converting enzyme 2 (ACE2) activity is lost during SARS-CoV-2 viral entry. ACE2 is highly expressed in the lungs and heart and converts Angiotensin II into Angiotensin 1-7. Angiotensin II signals proinflammatory, pro-vasoconstrictive, pro-thrombotic activity through p38 MAPK activation, which is countered by Angiotensin 1-7 downregulation of p38 activity. Loss of ACE2 upon viral entry may tip the balance towards destructive p38 signaling through Angiotensin II. Second, SARS-CoV was previously shown to directly upregulate p38 activity via a viral protein, similar to other RNA respiratory viruses that may hijack p38 activity to promote replication. Given the homology between SARS-CoV and SARS-CoV-2, the latter may employ a similar mechanism. Thus, SARS-CoV-2 may induce overwhelming inflammation by directly activating p38 and downregulating a key inhibitory pathway, while simultaneously taking advantage of p38 activity to replicate. Therapeutic inhibition of p38 could therefore attenuate COVID-19 infection. Interestingly, a prior preclinical study showed protective effects of p38 inhibition in a SARS-CoV mouse model. A number of p38 inhibitors are in the clinical stage and should be considered for clinical trials in serious COVID-19 infection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".