The contact activation system as a potential therapeutic target in patients with COVID‐19
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID‐19) is predicted to overwhelm health care capacity in the United States and worldwide, and, as such, interventions that could prevent clinical decompensation and respiratory compromise in infected patients are desperately needed. Excessive cytokine release and activation of coagulation appear to be key drivers of COVID‐19 pneumonia and associated mortality. Contact activation has been linked to pathologic upregulation of both inflammatory mediators and coagulation, and accumulating preclinical and clinical data suggest it to be a rational therapeutic target in patients with COVID‐19. Pharmacologic inhibition of the interaction between coagulation factors XI and XII has been shown to prevent consumptive coagulopathy, pathologic systemic inflammatory response, and mortality in at least 2 types of experimental sepsis. Importantly, inhibition of contact activation also prevented death from Staphylococcus aureus –induced lethal systemic inflammatory response syndrome in nonhuman primates. The contact system is likely dispensable for hemostasis and may not be needed for host immunity, suggesting it to be a reasonably safe target that will not result in immunosuppression or bleeding. As a few drugs targeting contact activation are already in clinical development, immediate clinical trials for their use in patients with COVID‐19 are potentially feasible for the prevention or treatment of respiratory distress.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".