Increased TGF‐β–producing CD4<b>+</b> T lymphocytes in postburn patients and their potential interaction with dermal fibroblasts in hypertrophic scarring
Bibliographic record
Abstract
The development of hypertrophic scar involves a complex interplay between cells and cytokines. Although the mechanism underlying its pathogenesis is not well understood, a polarized T-helper type 2 immune response has been reported, indicating a role for CD4+ T lymphocytes in hypertrophic scarring. Here, we report an increased frequency of CD4+/transforming growth factor-beta (TGF-beta)-producing T cells in the peripheral blood and hypertrophic scar tissue of burn patients. These cells may play an indirect regulatory role in hypertrophic scar by affecting the functions of dermal fibroblasts. Our results show an increase in cell proliferation and collagen synthesis by dermal fibroblasts treated with medium derived from burn patient CD4+ T lymphocytes but not from the CD4+ T cells of normal subjects. Using confocal microscopy and immunoblotting, we found the level of alpha-smooth muscle actin to be elevated in these treated dermal fibroblasts, which also showed an enhanced ability to contract collagen lattices. TGF-beta levels in medium conditioned by the culture of CD4+ T lymphocytes from burn patients were significantly higher than in the conditioned medium from CD4+ T lymphocytes of normal subjects. In addition, the application of a TGF-beta-neutralizing antibody significantly reduced the effect of burn patient CD4+ T lymphocyte medium on dermal fibroblast proliferation and collagen lattice contraction. Our study suggests that CD4+/TGF-beta-producing T lymphocytes may play an important role in postburn hypertrophic scarring.
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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 | 0.001 | 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) | 0.003 | 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".