Tissue‐engineering approaches to enhance nerve regeneration during wound healing
Bibliographic record
Abstract
Deep and extensive burns induce a partial or total destruction of cutaneous peripheral nerves, and a subsequent loss of tactile sensibility. Conventional treatments of deep wounds do not achieve a complete restoration of touch perception. Hence, innervation deficiency of skin constitutes a significant handicap to rehabilitation of burn patients. Our aim was to investigate two different approaches to enhance nerve regeneration in a reconstructed skin transplantated on mice. First, we developed a collagen sponge enriched with 1, 10 or 50μg of laminin in which human fibroblasts and keratinocytes were grown to produce a reconstructed skin, that was then grafted on the back of athymic mice for 120 days. Immunohistochemical studies demonstrated that 10μg of laminin induced a 5 times increase in the number of nerve fibers in the graft, compared to a control. The sense of touch recovery was evaluated by testing A‐Beta, A‐Delta and C sensory nerve fibers with a Neurometer (Neurotron Inc. Baltimore, MD). An improvement in the sensory function was observed for the A‐Beta and A‐Delta nerve fibers on grafts enriched with laminin. In the second approach, we incorporated in the reconstructed skin immature hair follicles obtained from mouse embryos. We observed nerve fibers migrating around these hair follicles only 30 days after graft. The incorporation of laminin, as a global approach, or hair follicles, as a targeted approach to enhance nerve regeneration in a tissue‐engineered autologous skin graft could be an efficient solution to promote a better tactile recovery for patients with deep and extensive burns. This work was supported by the Canadian Institute of Health Research and the Fonds de Recherche en Santé du Québec
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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".