Spatial and Temporal Expression of Types I and II Receptors for Transforming Growth Factor β in Normal Equine Skin and Dermal Wounds
Bibliographic record
Abstract
OBJECTIVE: To describe immunolocalization of TGF-beta receptors (RI and RII) in normal equine skin and in thoracic or limb wounds, healing normally or with exuberant granulation tissue (EGT). STUDY DESIGN: Group A: six wounds on one metacarpus and one midthoracic area. Group B: six wounds on both metacarpi, one of which was bandaged to stimulate EGT. Immunohistochemistry was used to detect RI and RII expression in wound margins. ANIMALS: Eight horses, randomly assigned to one of two study groups. METHODS: Neutralizing polyclonal anti-rabbit RI and RII antibodies were used to detect spatial expression of RI and RII in biopsies obtained before wounding, at 12 and 24 hours, and 5, 10 and 14 days after wounding. RESULTS: RI and RII were co-localized in both unwounded and wounded skin. There were no differences in cell types staining positively between tissues obtained from the limb and the thorax, or from normally healing limb wounds and limb wounds with EGT, at any time. Because of increased cellularity within EGT, staining intensity of limb wounds with 'proud flesh' was greater than limb wounds healing normally, and thoracic wounds, during the proliferative phase of repair. CONCLUSIONS: Strong expression of RI and RII, particularly in limb wounds with EGT, suggested that signalling for stimulation of matrix proteins is in place to contribute to scarring. CLINICAL RELEVANCE: This information may help determine the appropriate time for using receptor antagonists to prevent scarring of limb wounds of 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) | 0.001 | 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".