Integration of Functional Polymers and Biosensors to Enhance Wound Healing
Bibliographic record
Abstract
Biosensors have led to breakthroughs in the treatment of chronic wounds. Since the discovery of the oxygen electrode by Clarke, biosensors have evolved into the design of smart bandages that dispense drugs to treat wounds in response to physiological factors, such as pH or glucose concentration, which indicate pathogenic tendencies. Aptamer-based biosensors have helped identify and characterize pathogenic bacteria in wounds that often form antibiotic-resistant biofilms. Several functional polymers have served as indispensable parts of the fabrication of these biosensors. Beginning with natural polymers such as alginate, chitosan, and silk-based fibroin, which are biodegradable and absorptive, advances have been made in formulating biocompatible synthetic polymers such as polyurethane and polyethylene glycol designed to reduce non-specific binding of proteins and cells, making biosensors less painful or cumbersome for patient use. Recently, polycaprolactone has been developed, which offers ductility and a large surface-area-to-volume ratio. There is still room for advances in the fabrication and use of biosensors for wound healing and in this review, the trend in developing biosensors from biomarker detection to smart dressings to the incorporation of machine learning in designing customized wound patches while making application easier is highlighted and can be used for a long time.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".