Equine lumican (LUM) cDNA sequence and spatio‐temporal expression in an experimental model of normal and pathological wound healing
Bibliographic record
Abstract
The development of exuberant granulation tissue, a situation that in some ways resembles the human keloid, compromises both the aesthetic and functional outcomes of wound repair in horses. To help elucidate the underlying molecular mechanisms the spatio-temporal expression of lumican (LUM) mRNA and protein for their potential contributions to tissue remodelling of body and limb wounds, was examined in an established experimental model. Expression was studied in intact skin and in samples of 1-, 2-, 3-, 4- and 6-week-old wounds of the body and forelimb. Temporal gene expression was determined by reverse transcriptase polymerase chain reaction, and protein expression was mapped immunohistochemically. A significant increase in LUM mRNA expression was observed in response to wounding at both anatomical locations, and a significantly higher mRNA level was recorded in thoracic than in limb wounds at weeks 1, 3 and 6 of repair. The immunohistochemical observations partially corroborated the mRNA data. To the authors' knowledge this study is the first to document that the cDNA for LUM is expressed over the different phases of wound repair in horses and suggests that LUM might be involved in both inflammation and remodelling in response to dermal injury. Further studies are now required to verify and quantify the temporal expression of this protein to provide the basis for targeted therapies that might prevent the development of exuberant granulation tissue in horse wound repair.
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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".