P-Selectin de-ACTIVation in COVID-19: What Have We Learned?
Bibliographic record
Abstract
Symptomatic COVID-19 usually presents as a febrile respiratory illness and is frequently complicated by systemic involvement of the vasculature, which is characterized by endothelial injury, inflammation and coagulation activation, and an increased risk microand macrovascular thrombosis.1,2 Interventions that target inflammation or coagulation activation have been extensively evaluated in patients hospitalized with COVID-19 with mixed results.Several therapies targeting inflammation (e.g., dexamethasone, tocilizumab, baricitinib) were demonstrated in randomized trials to reduce mortality, whereas others (e.g., colchicine) did not provide consistent benefits, and none of these treatments reduced thrombotic events.Regimens of intensified compared with standard-dose prophylactic anticoagulation reduced the risk of venous thromboembolism but increased bleeding and -perhaps with the exception of full-intensity heparin for non-critically-ill patients -did not confer consistent benefit, and did not reduce mortality.3,4 Similarly, antiplatelet drugs used alone 5,6 , or in combination with reduced intensity anticoagulation 7 provided no benefits.Accordingly, investigators have begun testing antithrombotic treatments directed against other targets.Microvascular thrombosis in patients with COVID-19 is believed to be mediated by immunothrombosis, a process characterized by activation of innate immunity induced by pathogens to prevent their spread.8 P-selectin is expressed on endothelial cells and activated platelets to recruit leukocytes to the sites of endothelial injury and has been hypothesized to play a key role in mediating the thrombo-inflammatory response in COVID-19.9 Crizanlizumab is a monoclonal antibody that binds to P-selectin and blocks its interaction with the P-selectin receptor on neutrophils and monocytes.It is currently approved in the United States and Europe for prevention of recurrent vaso-occlusive crises in patients with sickle cell disease.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.049 | 0.038 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".