Sensory neurons accelerate reepithelialization through Substance P release in an innervated tissue‐engineered model of skin wound healing.
Bibliographic record
Abstract
The keratinocytes are responsible for the restoration of the epidermal barrier through reepithelialization during the wound healing process in humans. The influence of sensory neurons on this mechanism is poorly understood. We tested whether sensory neurons influence reepithelialization via a secretion of the neuropeptide substance P (SP). We developed a new tissueengineered model of skin wound healing. It consists of a perforated tissue‐engineered skin made of keratinocytes expressing GFP and that was stacked on a dermis to reconstitute the wound bed. The dermal construct is endothelialized by human endothelial cells and innervated or not by mouse sensory neurons. The migration of keratinocytes was imaged and quantified with GFP fluorescence. Sensory neurons produce SP in the construct as shown with ELISA and immunofluorescence analysis. Keratinocytes were found to express the NK1 cell receptor for SP in the reconstructed epidermis. The rate of reephithelialization was increased in presence of sensory neurons and SP was shown to contribute to this effect using antagonist to NK1 and recombinant SP instead of neurons. We conclude that this new model is promising for the study of the influence of the peripheral nervous system on reepithelialization. Grant Funding Source : N/A
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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 | 0.001 |
| 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".