Vimentin coordinates fibroblast proliferation and keratinocyte differentiation in wound healing via TGF-β–Slug signaling
Bibliographic record
Abstract
Vimentin has been shown to be involved in wound healing, but its functional contribution to this process is poorly understood. Here we describe a previously unrecognized function of vimentin in coordinating fibroblast proliferation and keratinocyte differentiation during wound healing. Loss of vimentin led to a severe deficiency in fibroblast growth, which in turn inhibited the activation of two major initiators of epithelial-mesenchymal transition (EMT), TGF-β1 signaling and the Zinc finger transcriptional repressor protein Slug, in vimentin-deficient (VIM(-/-)) wounds. Correspondingly, VIM(-/-) wounds exhibited loss of EMT-like keratinocyte activation, limited keratinization, and slow reepithelialization. Furthermore, the fibroblast deficiency abolished collagen accumulation in the VIM(-/-) wounds. Vimentin reconstitution in VIM(-/-) fibroblasts restored both their proliferation and TGF-β1 production. Similarly, restoring paracrine TGF-β-Slug-EMT signaling reactivated the transdifferentiation of keratinocytes, reviving their migratory properties, a critical feature for efficient healing. Our results demonstrate that vimentin orchestrates the healing by controlling fibroblast proliferation, TGF-β1-Slug signaling, collagen accumulation, and EMT processing, all of which in turn govern the required keratinocyte activation.
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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".