Effect of unfocused extracorporeal shock wave therapy on growth factor gene expression in wounds and intact skin of horses
Bibliographic record
Abstract
OBJECTIVE: To compare the effect of extracorporeal shock wave therapy (ESWT) on expression of fibroblast growth factor-7 (FGF-7), transforming growth factor-β1 (TGF-β1), insulin-like growth factor-1 (IGF-1), platelet-derived growth factor-A (PDGF), and vascular endothelial growth factor-A (VEGF) in skin with surgically created skin wounds and intact skin in horses. ANIMALS: 14 healthy horses. PROCEDURE: 8 horses were treated with ESWT at 6 locations along the neck at 36, 24, 12, 6, 2, or 1 hour prior to collection of full-thickness biopsy specimens from each location; a control specimen was collected from a sham-treated location. In 6 horses, 5 full-thickness wounds were created in each forelimb. Wounds in 1 forelimb/horse received ESWT immediately after creation and subsequently on days 7, 14, and 21; wounds in the contralateral forelimb remained untreated. Biopsy specimens were collected from 1 wound on each forelimb on days 7, 14, 21, 28, and 35. Expression levels of FGF-7, TGF-β1, IGF-1, PDGF, and VEGF were assessed in tissue samples from the horses' necks and forelimbs. RESULTS: In surgically created wounds, ESWT treatment was associated with reduced TGF-β1 expression, compared with expression in control wounds, during the entire study period. At 28 days following wound creation, IGF-1 expression was significantly increased for treated and untreated wounds, compared with findings on days 7, 14, 21, and 35. There was no significant effect of treatment on FGF-7, TGF-β1, IGF-1, PDGF, or VEGF expression in intact skin. CONCLUSIONS AND CLINICAL RELEVANCE: Intervention with ESWT to suppress TGF-β1 may decrease granulation tissue production, resulting in improved wound healing on the distal portion of horses' limbs.
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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".