Polymer for advanced wound healing: design and mechanism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it