Effect of ascorbate on plasminogen activator inhibitor-1 expression and release from platelets and endothelial cells in an in-vitro model of sepsis
Bibliographic record
Abstract
The microcirculation during sepsis fails due to capillary plugging involving microthrombosis. We demonstrated that intravenous injection of ascorbate reduces this plugging, but the mechanism of this beneficial effect remains unclear. We hypothesize that ascorbate inhibits the release of the antifibrinolytic plasminogen activator inhibitor-1 (PAI-1) from endothelial cells and platelets during sepsis. Microvascular endothelial cells and platelets were isolated from mice. Cells were cultured and stimulated with lipopolysaccharide (LPS), tumor necrosis factor alpha (TNFα), or thrombin (agents of sepsis), with/without ascorbate for 1-24 h. PAI-1 mRNA was determined by quantitative PCR. PAI-1 protein release into the culture medium was measured by ELISA. In platelets, PAI-1 release was measured after LPS, TNFα, or thrombin stimulation, with/without ascorbate. In endothelial cells, LPS and TNFα increased PAI-1 mRNA after 6-24 h, but no increase in PAI-1 release was observed; ascorbate did not affect these responses. In platelets, thrombin, but not LPS or TNFα, increased PAI-1 release; ascorbate inhibited this increase at low extracellular pH. In unstimulated endothelial cells and platelets, PAI-1 is released into the extracellular space. Thrombin increases this release from platelets; ascorbate inhibits it pH-dependently. The data suggest that ascorbate promotes fibrinolysis in the microvasculature under acidotic conditions in sepsis.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".