Effects of electrical stimulation on human skin keratinocyte growth and the secretion of cytokines and growth factors
Bibliographic record
Abstract
Abstract Electrical stimulation (ES) has been widely explored and found effective in promoting wound healing. However, the role of ES on keratinocytes, a major player in wound healing, has not been well established. The present work investigated the cellular and molecular behaviors of human skin keratinocytes being exposed to ES. HaCaT keratinocytes were seeded on a novel electrically conductive and soft PPy-PU/PLLA membrane and cultured under electrical intensities of 100 or 200 mV mm −1 for 6 and 24 h. The factors assessed after ES include cell proliferation, colony formation, cytokines, keratins, as well as phosphorylated ERK1/2 (pERK1/2) kinases. The results showed that the electrically stimulated cells exhibited a higher proliferative ability and secreted more IL-6, IL-1 α , IL-8, GRO α , FGF2, and VEGF-A. Interestingly, the 24 h ES induced a ‘stimulus memory’ by showing a significant rise in colony-forming efficiency in post-ES cells that were sub-cultured. Additionally, after stopping the 24 h ES, the productions of keratin 5 and keratin 14 were continuously increased for 3 d. The productions of keratin 10 and keratin 13 were significantly increased post the 6 h ES. Finally, the ES increased pERK1/2 kinases. The overall results demonstrated that the proliferation of keratinocytes and their secretion of cytokines and growth factors can be activated through appropriate ES to benefit skin 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".