Zinc, copper and manganese enhanced keratinocyte migration through a functional modulation of keratinocyte integrins
Bibliographic record
Abstract
The migration of keratinocytes plays an important role in the re-epithelialization of cutaneous wounds. Zinc, copper and manganese are used in vivo for their healing properties and their mechanism of action is still only partially known. Thus, they have been shown both to promote keratinocyte proliferation and to modulate integrins expression. The aim of this study was to determine if trace elements induce an increase of the migration of keratinocytes and if this effect is related to the modulation of integrins. Two independent migration assays were used to study keratinocyte migration: the scratch assay using normal human keratinocytes and the modified Boyden chamber using HaCaT cells. Inhibition studies using function-blocking antibodies directed to alpha3, alpha6, alpha(v) and beta1 subunits were performed to investigate the modulator effect of trace elements on integrin function. In this way, zinc and copper gluconates increased alpha3, alpha(v) and beta1 function whereas manganese gluconate seems mainly able to modulate the function of alpha3 and beta1. The stimulating effect of these trace elements on keratinocyte migration does not appear related to alpha6 subunit. Thus, zinc, copper and manganese enhanced keratinocyte migration and one of the mechanisms was going through a modulation of integrin functions.
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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".