Skin biopsy analysis reveals predisposition to stretch mark formation
Bibliographic record
Abstract
BACKGROUND: Stretch marks are a disfiguring skin condition of yet unknown etiology. OBJECTIVE: We wanted to compare the functional disposition of dermal fibroblasts, derived from unaffected skin of patients with stretch marks and fibroblasts from normal age-matched subjects, and develop a test to predict predisposition to stretch mark development. METHODS: Skin biopsies from normal subjects (NS), stretch-marked skin (SM), and normal-looking skin from patients with stretch marks (NL) were analyzed by histochemistry and assays of total protein, DNA, and elastin. Cellular migration, proliferation, and matrix production were also measured in primary cultures of biopsy-derived fibroblasts. RESULTS: We found that NL skin contained less DNA, protein, and elastin than NS skin (-16%, -36%, -44%, respectively) and that such deficiencies were more profound in SM skin (-55%, -64%, -80%, respectively). Both NL- and SM-derived cells had slower than normal outgrowth of their fibroblasts, which also demonstrated low migration and proliferation rates, and produced less elastin, fibrillin 1, collagen 1, and fibronectin than NS-derived cells in primary cultures. All these aberrant features, indicating a dormant phenotype of NL- and SM-derived fibroblasts, were reversed and normalized in the fourth passage of all tested fibroblasts. CONCLUSIONS: A series of in vitro tests led to the discovery of a dormant phenotype in dermal fibroblasts from patients with stretch marks. The described tests may serve as a diagnostic tool for predicting predisposition to stretch marks. The reported reversibility of impaired fibroblast phenotypes opens a new perspective for preventive treatments for people predisposed to stretch mark formation.
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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 | 0.001 |
| 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".