Electrical Stimulation Promotes Wound Healing by Enhancing Dermal Fibroblast Activity and Promoting Myofibroblast Transdifferentiation
Bibliographic record
Abstract
Electrical stimulation (ES) has long been used as an alternative clinical treatment and an effective approach to modulate cellular behaviours. In this work we investigated the effects of ES on human skin fibroblast activity, myofibroblast transdifferentiation and the consequence on wound healing. Normal human fibroblasts were seeded on heparin-bioactivated PPy/PLLA conductive membranes, cultured for 24 h, and then exposed to ES of 50 or 200 mV/mm for 2, 4, or 6 h. Following ES, the cells were either subjected to various analyses or re-seeded to investigate their healing capacity. Our findings show that ES had no cytotoxic effect on the fibroblasts, as demonstrated by the similar LDH activity levels in the ES-exposed and non-exposed cultures, and by the comparable cell viability under both conditions. Furthermore, the number of viable fibroblasts was higher following exposure to 6 h of ES than in the non-exposed culture. This enhanced cell growth was likely due to the ES up-regulated secretion of FGF-1 and FGF-2. In an in vitro scratch-wound assay where cell monolayer was used as a healing model, the electrically stimulated dermal fibroblasts migrated faster following exposure to ES and recorded a high contractile behaviour toward the collagen gel matrix. This enhanced contraction was supported by the high level of α-smooth muscle actin expressed by the fibroblasts following exposure to ES, indicating the characteristics of myofibroblasts. Remarkably, the modulation of fibroblast growth continued long after ES. In conclusion, this work demonstrates for the first time that exposure to ES promoted skin fibroblast growth and migration, increased growth factor secretion, and promoted fibroblast to myofibroblast transdifferentiation, thus promoting wound healing.
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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".