Deep dermal fibroblast profibrotic characteristics are enhanced by bone marrow–derived mesenchymal stem cells
Bibliographic record
Abstract
Hypertrophic scars are a significant fibroproliferative disorder complicating deep injuries to the skin. We hypothesize that activated deep dermal fibroblasts are subject to regulation by bone marrow-derived mesenchymal stem cells (BM-MSCs), which leads to the development of excessive fibrosis following deep dermal injury. We found that the expression of fibrotic factors was higher in deep burn wounds compared with superficial burn wounds collected from burn patients with varying depth of skin injury. We characterized deep and superficial dermal fibroblasts, which were cultured from the deep and superficial dermal layers of normal uninjured skin obtained from abdominoplasty patients, and examined the paracrine effects of BM-MSCs on the fibrotic activities of the cells. In vitro, deep dermal fibroblasts were found higher in the messenger RNA (mRNA) levels of type 1 collagen, alpha smooth muscle actin, transforming growth factor beta, stromal cell-derived factor 1, and tissue inhibitor of metalloproteinase 1, an inhibitor of collagenase (matrix metalloproteinase 1). As well, deep dermal fibroblasts had low matrix metalloproteinase 1 mRNA, produced more collagen, and contracted collagen lattices significantly greater than superficial fibroblasts. By co-culturing layered fibroblasts with BM-MSCs in a transwell insert system, BM-MSCs enhanced the fibrotic behavior of deep dermal fibroblasts, which suggests a possible involvement of BM-MSCs in the pathogenesis of 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 | 0.000 | 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) | 0.001 | 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".