Anti-fibrotic Actions of Equine Interleukin-10 on Transforming Growth Factor-Beta1-Stimulated Dermal Fibroblasts Isolated From Limbs of Horses
Bibliographic record
Abstract
Fibroproliferative disorders occur in both humans and horses following skin injury. In horses, wound healing on the limb is often complicated by the formation of fibroproliferative exuberant granulation tissue, characterized by persistent expression of pro-fibrotic transforming growth factor-beta1 (TGF-β1) and deficient expression of anti-inflammatory interleukin-10. Interleukin-10 directly modulates fibrotic gene expression in human fibroblasts, so we hypothesized that equine IL-10 (eIL-10) may exert similar anti-fibrotic effects on equine dermal fibroblasts. Cell-lines were created from the limb skin of six individual horses. Recombinant eIL-10 was produced and purified, and its effects on the cells investigated in the presence and absence of equine TGF-β1. Myofibroblast differentiation and collagen production were examined using immunofluorescent cytometry, cell contractility in a collagen gel assay, and fibrotic gene expression using quantitative PCR. In response to TGF-β1, fibroblasts increased in contractility and expression of alpha-smooth muscle actin, collagen types 1 and 3, and matrix metalloproteinases 1, 2 and 9. Interleukin-10 limited cell contractility and production of alpha-smooth muscle actin and type 3 collagen, and decreased mRNA levels of eCol3a1 and eMMP9, while increasing that of eMMP1. Opposing effects on eTGF-βR3 and eIL-10R1 gene expression were also observed, with mRNA levels decreasing following TGF-β1 treatment, and increasing with eIL-10 treatment. These findings indicate that eIL-10 limits the pro-fibrotic effects of equine TGF-β1, at least in part, through the modulation of fibrotic and receptor gene expression. Further investigations are warranted to assess the therapeutic utility of eIL-10 for the treatment of exuberant granulation tissue
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".