Equine CTNNB1 and PECAM1 nucleotide structure and expression analyses in an experimental model of normal and pathological wound repair
Bibliographic record
Abstract
BACKGROUND: Wound healing in horses is fraught with complications. Specifically, wounds on horse limbs often develop exuberant granulation tissue which behaves clinically like a benign tumor and resembles the human keloid in that the evolving scar is trapped in the proliferative phase of repair, leading to fibrosis. Clues gained from the study of over-scarring in horses should eventually lead to new insights into how to prevent unwanted scar formation in humans. cDNA fragments corresponding to CTNNB1 (coding for beta-catenin) and PECAM1, genes potentially contributing to the proliferative phase of repair, were previously identified in a mRNA expression study as being up-regulated in 7 day wound biopsies from horses. The aim of the present study was to clone full-length equine CTNNB1 and PECAM1 cDNAs and to study the spatio-temporal expression of mRNAs and corresponding proteins during repair of body and limb wounds in a horse model. RESULTS: The temporal pattern of the two genes was similar; except for CTNNB1 in limb wounds, wounding caused up-regulation of mRNA which did not return to baseline by the end of the study. Relative over-expression of both CTNNB1 and PECAM1 mRNA was noted in body wounds compared to limb wounds. Immunostaining for both beta-catenin and PECAM1 was principally observed in endothelial cells and fibroblasts and was especially pronounced in wounds having developed exuberant granulation tissue. CONCLUSION: This study is the first to characterize equine cDNA for CTNNB1 and PECAM1 and to document that these genes are expressed during wound repair in horses. It appears that beta-catenin may be regulated in a post-transcriptional manner while PECAM1 might help thoracic wounds mount an efficient inflammatory response in contrast to what is observed in limb wounds. Furthermore, data from this study suggest that beta-catenin and PECAM1 might interact to modulate endothelial cell and fibroblast proliferation during wound repair in the horse.
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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) | 0.002 | 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".