Endothelial cell hypertrophy is associated with microvascular occlusion in horse wounds.
Bibliographic record
Abstract
Wound repair in horse limbs is often complicated by excessive fibroplasia and scarring. Occlusion of the microvessels populating the granulation tissue appears to be involved in the excessive accumulation of extracellular matrix during the repair of limb wounds. This study aimed to determine whether endothelial cell hypertrophy or hyperplasia, or both, contribute to microvascular occlusion and whether the pericyte is involved in this anomaly. We created 5 wounds, each 2.5 x 2.5 cm, on both forelimbs and on the body of 6 horses. One limb was bandaged to stimulate excessive wound fibroplasia. Weekly biopsy specimens were evaluated by transmission electron microscopy to measure microvessel luminal diameters and the surface area of endothelial cells and to count endothelial cells and pericytes. Microvessels were occluded significantly more often in limb wounds than in body wounds. The surface area of endothelial cells lining occluded microvessels (mean +/- standard error, 28.4013 +/- 1.5154 microm2) was significantly greater (P = 0.05) than that of cells lining patent microvessels (26.2220 +/- 1.5268 microm2). Conversely, neither the number of endothelial cells nor the number of pericytes differed between patent and occluded microvessels or between limb and body wounds. Furthermore, the wound location and the status of the microvessels (patent or occluded) did not alter the ratio of endothelial cells to pericytes. These data suggest that endothelial cell hypertrophy might play a role in the microvascular occlusion present in granulation tissue of limb wounds in horses, but the contribution of the pericyte remains obscure.
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 | 0.001 |
| 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 | 0.000 |
| 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".