Platelets accelerate gastric ulcer healing through presentation of vascular endothelial growth factor
Bibliographic record
Abstract
1. Platelets contain an array of growth factors that can modulate healing processes, including both pro- (e.g., vascular endothelial growth factor (VEGF)) and antiangiogenic (e.g., endostatin) factors. Previous studies have shown that circulating platelets contribute significantly to gastric ulcer healing, acting as a delivery system for these growth factors to the site of injury. In this study, we examined the effects of orally administered human platelets on the healing of gastric ulcers in rats, and determined the contribution of VEGF and endostatin to healing in this model. 2. Twice-daily administration of human platelets significantly accelerated ulcer healing, but platelet-poor plasma (PPP), lysed platelets and serum failed to produce this effect. There was no correlation between ulcer healing and the levels of VEGF or endostatin in serum, PPP or platelet-rich plasma (PRP). 3. Accelerated ulcer healing could not be produced by oral administration of the angiogenic factors themselves, at concentrations matching those in PRP. 4. The accelerated healing induced by platelets could be reversed by immuno-neutralization of VEGF. In contrast, immuno-neutralization of endostatin did not affect PRP-induced ulcer healing. 5. These studies indicate that VEGF released from platelets accounts for the accelerated healing of gastric ulcers. However, as intact (rather than lysed) platelets were required for the accelerated healing, the presentation of VEGF by the platelet at the site of injury appears to be crucial for enhancement of the healing process.
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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) | 0.000 | 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".