Expression of Transforming Growth Factor β<sub>1</sub>, β<sub>3</sub>, and Basic Fibroblast Growth Factor in Full‐Thickness Skin Wounds of Equine Limbs and Thorax
Bibliographic record
Abstract
OBJECTIVE: To map the expression of transforming growth factor (TGF)-beta(1), TGF-beta(3), and basic fibroblast growth factor (bFGF) in full-thickness skin wounds of the horse. To determine whether their expression differs between limbs and thorax, to understand the pathogenesis of exuberant granulation tissue. STUDY DESIGN: Six wounds were created on one lateral metacarpal area and one midthoracic area of each horse. Sequential wound biopsies allowed comparison of the temporal expression of growth factors between limb and thoracic wounds. ANIMALS: Four 2- to 4-year-old horses. METHODS: Wounds were assessed grossly and histologically at 12 and 24 hours, and 2, 5, 10, and 14 days postoperatively. ELISAs were used to measure the growth factor concentrations of homogenates of wound biopsies taken at the same timepoints. RESULTS: TGF-beta(1) peaked at 24 hours in both locations and returned to baseline in thoracic wounds by 14 days but remained elevated in limb wounds for the duration of the study. Expression kinetics of TGF-beta(3) differed from those of TGF-beta(1). TGF-beta(3) concentrations gradually increased over time, showing a trend toward an earlier and higher peak in thoracic compared with limb wounds. bFGF expression kinetics resembled those of TGF-beta(1), but no statistically significant differences existed between limb and thoracic wounds. CONCLUSIONS: Growth factor expression is up-regulated during normal equine wound repair. TGF-beta(1) and TGF-beta(3) show a reciprocal temporal regulation. Statistically significant differences exist between limb and thoracic wounds with respect to TGF-beta(1) expression. CLINICAL RELEVANCE: The persistence of TGF-beta(1) expression in leg wounds may be related to the development of exuberant granulation tissue in this location, because TGF-beta(1) is profibrotic.
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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) | 0.001 | 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".