Ascorbate Reduces Mouse Platelet Aggregation and Surface P‐Selectin Expression in an <i>Ex Vivo</i> Model of Sepsis
Bibliographic record
Abstract
OBJECTIVE: Compromised perfusion of the capillary bed can lead to organ failure and mortality in sepsis. We have reported that intravenous injection of ascorbate inhibits platelet adhesion and plugging in septic capillaries. In this study, we hypothesized that ascorbate reduces aggregation of platelets and their surface expression of P-selectin (a key adhesion molecule) in mice. METHODS: Platelets were isolated from control mice and subjected to agents known to be released into the bloodstream during sepsis (thrombin, ADP or U46619, thromboxane A2 analog). Platelet aggregation was analyzed by aggregometry and P-selectin expression by flow cytometry. RESULTS: Platelet-activating agents increased aggregation and P-selectin expression. Ascorbate inhibited these increases. This inhibitory effect was NOS-independent (LNAME had no effect). In contrast to the platelet-activating agents, direct stimuli lipopolysaccharide, TNFα, or plasma from septic mice did not increase aggregation/expression, a finding consistent with the literature. The results suggest a complex mechanism of platelet aggregation and P-selectin expression in sepsis, where generation of platelet-activating stimuli is required first, before platelet aggregation and adhesion in capillaries occur. CONCLUSION: The ability of ascorbate to reduce platelet aggregation and P-selectin expression could be an important mechanism by which ascorbate inhibits capillary plugging in sepsis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".