The effects of <scp>TGF‐β1</scp> and <scp>IFN‐α2b</scp> on decorin, decorin isoforms and type I collagen in hypertrophic scar dermal fibroblasts
Bibliographic record
Abstract
Hypertrophic scars (HTS) develop from an excessive synthesis of structural proteins like collagen and a decreased expression of proteoglycans such as decorin. Previous research has demonstrated that decorin expression is significantly down-regulated in HTS, deep dermal tissue, and thermally injured tissue, reducing its ability to regulate pro-fibrotic transforming growth factor-beta 1 (TGF-β1) and normal fibrillogenesis. However, treatment of HTS fibroblasts with interferon-alpha 2b (IFN-α2b) has been shown to reduce excessive collagen synthesis and improve HTS by reducing serum TGF-β1 levels. The expression of decorin isoforms in HTS is currently unknown and the effects of TGF-β1 and IFN-α2b on decorin, decorin isoform expression and type 1 collagen are of great interest to our group. Dermal fibroblasts were treated with TGF-β1 and/or IFN-α2b, for 48 h. The expression and secretion of decorin, decorin isoforms and type 1 collagen were quantified with reverse transcription-quantitative polymerase chain reaction, immunofluorescence staining and enzyme-linked immunosorbent assays. The mRNA expression of decorin and each isoform was significantly reduced in HTS fibroblasts relative to normal skin. TGF-β1 decreased the mRNA expression of decorin and decorin isoforms, whereas IFN-α2b showed the opposite effect. IFN-α2b significantly inhibited TGF-β1's effect on the mRNA expression of type I collagen alpha 1 in papillary dermal fibroblasts and overall showed relative effects of inhibiting TGF-β1. These data support that a further investigation into the structural and functional roles of decorin isoforms in HTS pathogenesis is warranted and that IFN-α2b is an important agent in reducing fibrotic outcomes.
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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 | 0.000 |
| 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) | 0.000 | 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".