Preliminary Observations on Expression of Transforming Growth Factors β1 and β3 in Equine Full‐Thickness Skin Wounds Healing Normally or with Exuberant Granulation Tissue
Bibliographic record
Abstract
OBJECTIVE: To determine whether transforming growth factor (TGF)-beta1 and -beta3 expression differs between equine limb wounds healing normally and those healing with experimentally induced exuberant granulation tissue (EGT). STUDY DESIGN: Six wounds were created on the lateral aspect of both metacarpi of each horse; one forelimb was untreated, and the other was bandaged to stimulate the development of EGT. Sequential wound biopsies allowed comparison of growth factor expression between the two types of wound. ANIMALS: Four horses (2 to 4 years of age; 350 to 420 kg). METHODS: Wounds were assessed grossly, histologically, and by enzyme-linked immunosorbent assay (ELISA) for TGF-beta1 and -beta3 expression at 12 and 24 hours and 2, 5, 10, and 14 days postoperatively. RESULTS: Bandaged wounds developed EGT. In all wounds, TGF-beta1 peaked early and remained elevated at 14 days. Peak TGF-beta1 concentration was higher in wounds with EGT, but not significantly so. Expression of TGF-beta3 differed from TGF-beta1, with peak TGF-beta3 concentrations being delayed. Concentrations of TGF-beta3 were higher in wounds healing normally, but this difference was not significant. CONCLUSIONS: During both normal and exuberant wound repair, the expression of TGF-beta1 occurred earlier than TGF-beta3 expression. Wounds healing with EGT tended to have higher concentrations of fibrogenic TGF-beta1 and lower concentrations of antifibrotic TGF-beta3 than wounds healing normally, although these differences were not statistically significant. CLINICAL RELEVANCE: This study suggests that the production of EGT in bandaged wounds may be related to increased expression of fibrogenic TGF-beta1 and decreased expression of antifibrotic TGF-beta3. Further investigation of the roles of TGF-beta1 and -beta3 may be important in understanding the molecular control of EGT in horses.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".