Differences in Hypertrophic Scar Fibroblasts according to Scar Severity: Expression of Transforming Growth Factor β1 at the mRNA and Protein Levels
Bibliographic record
Abstract
Background Hypertrophic scars result from excessive collagen deposition and increased transforming growth factor beta-1 (TGF-β1) levels.We hypothesized that the expression of TGF-β1 mRNA and protein would increase with the clinical severity of hypertrophic scars.Methods Primary dermal fibroblasts were isolated from cultures of normal skin and hypertrophic scars.The hypertrophic scars were classified by grade based on the Vancouver Scar Scale.After 96 hours of serum starvation, TGF-β1 levels in the supernatant were determined using solid-phase, enzyme-linked immunosorbent assay (ELISA).Quantitative reverse transcription-polymerase chain reaction was performed to quantify TGF-β1 mRNA expression.Results TGF-β1 protein levels of hypertrophic scars tended to increase with increasing severity of the scars, according to the Vancouver Scar Scale.The differences between the normal dermal tissue (NS), hypertrophic scar grade (HS) 1, and HS4 groups were statistically significant (P <0.01).The TGF-β1 mRNA levels of hypertrophic scars also tended to increase according to scar severity.The differences between the NS, HS1, HS2, HS3, and HS4 groups were statistically significant (P<0.01). ConclusionsThe classification of hypertrophic scars according to the Vancouver Scar Scale usually matches the severity of the microenvironment of the hypertrophic scar.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".