Polymer for advanced wound healing: design and mechanism
Bibliographic record
Abstract
Smart and rapid wound healing has long been a significant challenge for the medical community. Recent advancements in biomaterials and manufacturing technologies are overcoming the limitations of traditional wound dressings. Notably, reversible light-responsive azobenzene derivatives in elastomer form are emerging as intelligent materials for this purpose. Their reversible photoisomerization properties have extensive applications in wound healing. This study systematically reviews the design principles, strategies, and mechanisms of smart elastomers based on drugs, as well as their applications in various stages of wound healing. When classifying drugs-releasing elastomers by response factors and loaded drugs, we emphasize design strategies based on physical blending and temperature or light microenvironments. Comparing smart elastomers to traditional polymer dressings, this review highlights how the dual presence of photoisomerization and dynamic bonds grants these polymers non-contact, reversible, intelligent adhesive properties. This unique combination enhances drugs delivery efficiency at wound sites while minimizing patient discomfort. The review discusses the advantages, challenges, and future prospects of smart elastomers in wound healing, offering new insights into intelligent drugs delivery systems for wound treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".