Sensory Neurons Accelerate Skin Reepithelialization via Substance P in an Innervated Tissue-Engineered Wound Healing Model
Bibliographic record
Abstract
Keratinocytes are responsible for reepithelialization and restoration of the epidermal barrier during wound healing. The influence of sensory neurons on this mechanism is not fully understood. We tested whether sensory neurons influence wound closure via the secretion of the neuropeptide substance P (SP) with a new tissue-engineered wound healing model made of an upper-perforated epidermal compartment reconstructed with human keratinocytes expressing green fluorescent protein, stacked over a dermal compartment, innervated or not with sensory neurons. We showed that sensory neurons secreted SP in the construct and induced a two times faster wound closure in vitro. This effect was partially reproduced by addition of SP in the model without neurons, and completely blocked by a treatment with a specific antagonist of the SP receptor neurokinin-1 expressed by keratinocytes. However, this antagonist did not compromise wound closure compared with the control. Similar results were obtained when the model with or without neurons was transplanted on CD1 mice, while wound closure occurred faster. We conclude that sensory neurons play an important, but not essential, role in wound healing, even in absence of the immune system. This model is promising to study the influence of the nervous system on reepithelialization in normal and pathological conditions.
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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".