Concise Review: Tissue-Engineered Skin and Nerve Regeneration in Burn Treatment
Bibliographic record
Abstract
Burns not only destroy the barrier function of the skin but also alter the perceptions of pain, temperature, and touch. Different strategies have been developed over the years to cover deep and extensive burns with the ultimate goal of regenerating the barrier function of the epidermis while recovering an acceptable aesthetic aspect. However, patients often complain about a loss of skin sensation and even cutaneous chronic pain. Cutaneous nerve regeneration can occur from the nerve endings of the wound bed, but it is often compromised by scar formation or anarchic wound healing. Restoration of pain, temperature, and touch perceptions should now be a major challenge to solve in order to improve patients' quality of life. In addition, the cutaneous nerve network has been recently highlighted to play an important role in epidermal homeostasis and may be essential at least in the early phase of wound healing through the induction of neurogenic inflammation. Although the nerve regeneration process was studied largely in the context of nerve transections, very few studies have been aimed at developing strategies to improve it in the context of cutaneous wound healing. In this concise review, we provide a description of the characteristics of and current treatments for extensive burns, including tissue-engineered skin approaches to improve cutaneous nerve regeneration, and describe prospective uses for autologous skin-derived adult stem cells to enhance recovery of the skin's sense of touch.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".