Biotechnological Management of Skin Burn Injuries: Challenges and Perspectives in Wound Healing and Sensory Recovery
Bibliographic record
Abstract
Many wound management protocols have been developed to improve wound healing after burn with the primordial aim to restore the barrier function of the skin and also provide a better esthetic outcome. Autologous skin grafts remain the gold standard in the treatment of skin burn, but this treatment has its limitation especially for patients presenting limited donor sites due to extensive burn areas. Deep burn injuries also alter the integrity of skin-sensitive innervation and have an impact on patient's quality of life by compromising perceptions of touch, temperature, and pain. Thus, patients can suffer from long-term disabilities ranging from cutaneous sensibility loss to chronic pain. The cellular mechanisms involved in skin reinnervation following injury are not elucidated yet. Depending on the depth of the burn, nerve sprouting can occur from the wound bed or the surrounding healthy tissue, but somehow this process fails to provide correct reinnervation of the wound during scarring. In addition, several clinical observations indicate that damage to the peripheral nervous system influences wound healing, resulting in delayed wound healing or chronic wounds, underlining the role of innervation and neuromediators for normal cutaneous tissue repair development. Promising tissue engineering strategies, including the use of biomaterials, skin substitutes, and stem cells, could provide novel alternative treatments in wound healing and help in improving patient's sensory recovery.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".