A cellulose nanofibril-reinforced hydrogel with robust mechanical, self-healing, pH-responsive and antibacterial characteristics for wound dressing applications
Bibliographic record
Abstract
BACKGROUND: Bacterial infection in wounds has become a major threat to human life and health. With the growth use of synthetic antibiotics and the elevated evolution of drug resistant bacteria in human body cells requires the development of novel wound curing strategies. Herein, a novel pH-responsive hydrogel (RPC/PB) was fabricated using poly(vinyl alcohol)-borax (PB) and natural antibiotic resveratrol grafted cellulose nanofibrils (RPC) for bacterial-infected wound management. RESULTS: In this hydrogel matrix, RPC conjugate was interpenetrated in the PB network to form a semi-interpenetrating network that exhibited robust mechanical properties (fracture strength of 149.6 kPa), high self-healing efficiency (> 90%), and excellent adhesion performance (tissue shear stress of 54.2 kPa). Interestingly, the induced RPC/PB hydrogel showed pH-responsive drug release behavior, the cumulative release amount of resveratrol in pH 5.4 was 2.33 times than that of pH 7.4, which was adapted well to the acidic wound microenvironment. Additionally, this RPC/PB hydrogel exhibited excellent biocompatibility and antioxidant effect. Moreover, in vitro and in vivo results revealed that such RPC/PB hydrogel had excellent antibacterial, skin tissue regeneration and wound closure capabilities. CONCLUSION: Therefore, the generated RPC/PB hydrogel could be an excellent wound dressing for bacteria-infected wound healing.
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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) | 0.000 | 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) | 0.001 | 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 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".