Skin Tissue Engineering Advances in Burns: A Brief Introduction to the Past, the Present, and the Future Potential
Bibliographic record
Abstract
Burn injuries are a severe form of skin damage with a significant risk of scarring and systemic sequelae. Approximately 11 million individuals worldwide suffer burn injuries annually, with 180,000 people dying due to their injuries. Wound healing is considered the main determinant for the survival of severe burns and remains a challenge. The surgical treatment of burn wounds entails debridement of necrotic tissue, and the wound is covered with autologous skin substitutes taken from healthy donor areas. Autologous skin transplantation is still considered to be the gold standard for wound repair. However, autologous skin grafts are not always possible, especially in cases with extensive burns and limited donor sites. Allografts from human cadaver skin and xenografts from pig skin may be used in these situations to cover the wounds temporarily. Alternatively, dermal analogs are used until permanent coverage with autologous skin grafts or artificial skins can be achieved, requiring staged procedures to prolong the healing times with the associated risks of local and systemic infection. Over the last few decades, the wound healing process through tissue-engineered skin substitutes has significantly enhanced as the advances in intensive care ensuring early survival have led to the need to repair large skin defects. The focus has shifted from survival to the quality of survival, necessitating accelerated wound repair. This special volume of JBCR is dedicated to the discoveries, developments, and applications leading the reader into the past, present, and future perspectives of skin tissue engineering in burn injuries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".