Elevated levels of pro-thrombotic eNOS-negative platelets in COVID-19 patients
Bibliographic record
Abstract
BACKGROUND: Platelet-rich microvascular thrombi are common in severe COVID-19. Endogenous nitric oxide (NO)-signaling limits thrombus formation and previously we identified platelet subpopulations with a differential ability to produce NO based on the presence or absence of endothelial nitric oxide synthase (eNOS). eNOS expression is counter-regulated by cytokines, and COVID-19-associated immune/inflammatory responses may affect the transcriptome profile of megakaryocytes and their platelet progeny. OBJECTIVES: We investigated whether the percentage of eNOS-negative to eNOS-positive platelets increases in COVID-19 patients and whether this change may be due to the actions of pro-inflammatory cytokines on megakaryocytes. METHODS: Platelets were isolated from hospitalized COVID-19 patients and COVID-19-negative controls. Platelet eNOS was measured by flow cytometry and plasma inflammatory cytokines by ELISA. Megakaryocytes from eNOS-GFP transgenic mice and the Meg-01 cell line were characterized to identify an appropriate model to study eNOS-based platelet subpopulation formation in response to inflammatory cytokines. RESULTS: COVID-19 patients demonstrated a significant increase in eNOS-negative and a concomitant decrease in eNOS-positive platelets compared to controls, and this change was associated with disease severity as assessed by ICU admission. A higher eNOS-negative to -positive platelet percentage was associated with enhanced platelet activation as measured by surface CD62P. Accordingly, COVID-19 patients demonstrated higher TNF-α, IL-6, and IL-1β plasma concentrations than controls. Inflammatory cytokines associated with COVID-19 promoted eNOS-negative Meg-01 formation and enhanced subsequent eNOS-negative platelet-like particle formation. CONCLUSIONS: COVID-19 patients have a higher percentage of eNOS-negative to -positive platelets, likely as a result of inflammatory response reducing megakaryocyte eNOS expression, which predisposes to thrombosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".