Fibrocytes from burn patients regulate the activities of fibroblasts
Bibliographic record
Abstract
Wound healing requires an elaborate interplay between numerous cell types that orchestrate a series of regulated and overlapping events. Fibrocytes are a unique leukocyte subpopulation implicated in this process. One role proposed for these cells in wound healing is to synthesize extracellular matrix. Interestingly, using mass spectrometry to quantify hydroxyproline, we discovered that the capacity of fibrocytes from normal subjects or from burn patients to produce collagen is much less than that of dermal fibroblasts. Therefore, we investigated whether fibrocytes could play an indirect, regulatory, role in the healing of burn wounds by affecting the functions of dermal fibroblasts. Dermal fibroblasts treated with medium conditioned by burn patient fibrocytes, but not by those derived from normal subjects, showed an increase in cell proliferation and migration. Using confocal microscopy, flow cytometry, and immunoblotting, we found the level of alpha-smooth muscle actin (alpha-SMA) expression to be increased in these treated dermal fibroblasts, which also showed an enhanced ability to contract collagen lattices. To determine whether these effects could be attributed to transforming growth factor beta (TGF-beta1) or to connective tissue growth factor (CTGF), we measured total TGF-beta1 levels in the conditioned medium by an enzyme-linked immunosorbtion assay and assessed levels of CTGF mRNA and protein in fibroblasts and fibrocytes by reverse transcription-polymerase chain reaction and Western blotting. The results showed significantly higher levels of TGF-beta1 and CTGF produced by burn patient fibrocytes. In addition, the application of a TGF-beta1 neutralizing antibody significantly reduced the effect of burn patient fibrocyte medium on dermal fibroblast proliferation, migration, and collagen lattice contraction. Our results suggest that in healing burn wounds, fibrocytes could regulate the activities of local fibroblasts.
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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) | 0.003 | 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".