Ascorbate inhibits platelet-endothelial adhesion in an in-vitro model of sepsis via reduced endothelial surface P-selectin expression
Bibliographic record
Abstract
Plugging of the capillary bed can lead to organ failure and mortality in sepsis. We have reported that intravenous ascorbate injection reduces platelet adhesion to the capillary wall and capillary plugging in septic mice. Both platelet adhesion and capillary plugging require P-selectin, a key adhesion molecule. To elucidate the beneficial effect of ascorbate, we hypothesized that ascorbate reduces platelet-endothelial adhesion by reducing P-selectin surface expression in endothelial cells. We used mouse platelets, and monolayers of cultured microvascular endothelial cells (mouse skeletal muscle origin) stimulated with lipopolysaccharide, to examine platelet-endothelial adhesion. P-selectin mRNA expression in endothelial cells was determined by real-time PCR and P-selectin protein expression at the surface of these cells by immunofluorescence. Secretion of von Willebrand factor from cells into the supernatant (a measure of P-selectin-containing granule exocytosis) was determined by ELISA. Lipopolysaccharide (10 μg/ml, 1 h) increased platelet-endothelial adhesion. P-selectin-blocking antibody inhibited this adhesion. Lipopolysaccharide also increased P-selectin mRNA in endothelial cells, P-selectin expression at the endothelial surface, and von Willebrand factor secretion. Ascorbate pretreatment (100 μmol/l, 4 h) inhibited the increased platelet adhesion, surface expression of P-selectin, and von Willebrand factor secretion, but not the increase in P-selectin mRNA. The lipopolysaccharide-induced increase in platelet-endothelial adhesion requires P-selectin presence at the endothelial surface. Ascorbate's ability to reduce this presence could be important in reducing both platelet adhesion to the capillary wall and capillary plugging 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) | 0.001 | 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".