In vitro responses of human dermal fibroblasts to mechanical strain: A systematic review and meta-analysis
Bibliographic record
Abstract
In vitro research in the field of mechanotransducive regulation of dermal fibroblasts is characterized by highly variable methodology and contradictory results. The primary objective of this systematic review was to establish how in vitro mechanical stretch affects human dermal fibroblast function, by means of a quantitative synthesis of all available evidence. The secondary objectives were to examine the effects of covariates related to donor age, fibroblast origin, experimental treatments, and mechanical stimulation parameters on dermal fibroblast responsiveness to mechanical strain. Summary outcomes for fibroblast proliferation and collagen production were combined using a fixed-effects meta-analytical model. Subgroup analysis and meta-regression were used to investigate the effects of different conditions on the summary outcomes. Mechanical strain was found to not affect fibroblast proliferation in neonatal fibroblasts, while adult fibroblasts proliferation was significantly increased. Collagen production was significantly increased in response to mechanical stimulation, with Vitamin C stimulation as the most important covariate. Stretching frequency emerged as positively associated with fibroblast proliferation and negatively associated with collagen production. We conclude from this study that distinct differences exist in the effects of mechanical stretching between dermal fibroblasts from neonatal and adult donors, which will help to further elucidate the pathophysiological mechanism behind tension-induced scarring.
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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".