Orf virus interleukin‐10 and vascular endothelial growth factor‐E modulate gene expression in cultured equine dermal fibroblasts
Bibliographic record
Abstract
BACKGROUND: Wounds in horses often exhibit sustained inflammation and inefficient vascularization, leading to excessive fibrosis and clinical complications such as "proud flesh". Orf virus-derived proteins, vascular endothelial growth factor (VEGF)-E and interleukin (ovIL)-10, enhance angiogenesis and control inflammation and fibrosis in skin wounds of laboratory animals. HYPOTHESIS/OBJECTIVES: The study aimed to determine if equine dermal cells respond to VEGF-E and ovIL-10. Equine dermal cells are expected to express VEGF and IL-10 receptors, so viral protein treatment is likely to alter cellular gene expression and behaviour in a manner conducive to healing. ANIMALS: Skin samples were harvested from the lateral thoracic wall of two healthy thoroughbred horses. METHODS: Equine dermal cells were isolated using a skin explant method and their phenotype assessed by immunofluorescence. Cells were treated with recombinant proteins, with or without inflammatory stimuli. Gene expression was examined using standard and quantitative reverse transcriptase PCR. Cell behaviour was evaluated in a scratch assay. RESULTS: Cultured cells were half vimentin(+ve) fibroblasts and half alpha smooth muscle actin(+ve) and vimentin(+ve) myofibroblasts. VEGF-E increased basal expression of IL-10 mRNA, whereas VEGF-A and collagenase-1 mRNA expression was increased by ovIL-10. In cells exposed to inflammatory stimulus, both treatments dampened tumour necrosis factor mRNA expression, and ovIL-10 exacerbated expression of monocyte chemoattractant protein. Neither viral protein influenced cell migration greatly. CONCLUSIONS AND CLINICAL IMPORTANCE: This study shows that VEGF-E and ovIL-10 are active on equine dermal cells and exert anti-inflammatory and anti-fibrotic effects that may enhance skin wound healing in 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".