MicroRNA 181b Regulates Decorin Production by Dermal Fibroblasts and May Be a Potential Therapy for Hypertrophic Scar
Bibliographic record
Abstract
Hypertrophic scarring is a frequent fibroproliferative complication following deep dermal burns leading to impaired function and lifelong disfigurement. Decorin reduces fibrosis and induces regeneration in many tissues, and is significantly downregulated in hypertrophic scar and normal deep dermal fibroblasts. It was hypothesized that microRNAs in these fibroblasts downregulate decorin and blocking them would increase decorin and may prevent hypertrophic scarring. Lower decorin levels were found in hypertrophic scar as compared to normal skin, and in deep as compared to superficial dermis. A decorin 3' un-translated region reporter assay demonstrated microRNA decreased decorin in deep dermal fibroblasts, and microRNA screening predicted miR- 24, 181b, 421, 526b, or 543 as candidates. After finding increased levels of mir-181b in deep dermal fibroblasts, it was demonstrated that TGF-β1 stimulation decreased miR-24 but increased miR-181b and that hypertrophic scar and deep dermis contained increased levels of miR-181b. By blocking miR-181b with an antagomiR, it was possible to increase decorin protein expression in dermal fibroblasts. This suggests miR-181b is involved in the differential expression of decorin in skin and wound healing. Furthermore, blocking miR-181b reversed TGF-β1 induced decorin downregulation and myofibroblast differentiation in hypertrophic scar fibroblasts, suggesting a potential therapy for hypertrophic scar.
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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 | 0.001 |
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".