Angiostatin—a novel SARS-CoV-2 inhibitor and biomarker underlying COVID-19 pathophysiology
Bibliographic record
Abstract
BACKGROUND: Despite efficacious vaccines, many individuals remain at risk of severe illness and death from COVID-19 due to immune-escape variants. Hence, a better understanding of biomarkers underlying COVID-19 pathophysiology is needed to improve disease progression prediction and identify new drug targets. Angiostatin is a plasmin(ogen)-derived protein generated by platelets. As microvascular thrombosis, a key pathologic feature of COVID-19, can create microenvironments of both high angiostatin concentration and hypoxia/acidosis, conditions known to favor angiostatin's proapoptotic actions on endothelial and epithelial cells, angiostatin may be a biomarker contributing to COVID-19 pathophysiology. OBJECTIVES: To assess the role of angiostatin in COVID-19. METHODS: Plasma angiostatin concentrations were compared between COVID-19 patients and COVID-19-negative controls, as were temporal changes in plasma angiostatin in COVID-19 patients. Subsequent mechanistic cellular studies investigated the effects of angiostatin and its neutralization on both SARS-CoV-2 infection and subsequent cell death. RESULTS: Plasma angiostatin concentrations increased following SARS-CoV-2 infection and remained elevated in COVID-19 patients for 21 to 28 days. Angiostatin at concentration that would be generated within a clot over 7 to 8 hours promoted cell death in acidic microenvironments characteristic of severe COVID-19. Irrespective of pH, angiostatin reduced SARS-CoV-2 cellular entry of multiple variants by interfering with spike protein proteolysis. Selective angiostatin-neutralizing peptides inhibited angiostatin-induced cell death, but not angiostatin's ability to reduce infection. CONCLUSION: Angiostatin has dual roles during COVID-19, both preventing infection and promoting cell death. Selective angiostatin-neutralizing peptides may be novel therapeutics for further preclinical evaluation in models of severe COVID-19.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".