A pH and Temperature Dual-Responsive Microgel-Embedded, Adhesive, and Tough Hydrogel for Drug Delivery and Wound Healing
Bibliographic record
Abstract
Stimuli-responsive hydrogels have attracted much attention over the past decade for potential bioengineering applications such as wound dressing and drug delivery. In this work, a pH and temperature dual-responsive microgel-embedded hydrogel has been fabricated by incorporating poly( N -isopropylacrylamide- co -acrylic acid) (PNIPAAm- co -AAc) based microgel particles into polyacrylamide (PAAm)/chitosan (CS) semi-interpenetrating polymer network (semi-IPN), denoted as microgel@PAM/CS. The resultant hydrogel possesses excellent mechanical properties including stretchability, compressibility, and elasticity. In addition, the microgel@PAM/CS hydrogels can tightly adhere to the surfaces of a variety of tissues such as porcine skin, kidney, intestine, liver, and heart. Moreover, it shows controlled dual-drug release profile of both bovine serum albumin (BSA) (as a model protein) and sulfamethoxazole (SMZ), an antibiotic. Excellent antimicrobial properties are obtained for SMZ-loaded microgel@PAM/CS hydrogels. Compared with traditional drug administration methods such as by mouth, injection, and inhalation, the microgel@PAM/CS hydrogels possess advantages such as higher drug loading efficiency (by more than 80%) and controllable and sustained (over 48 h) release. The microgel@PAM/CS hydrogels can significantly enhance the wound healing process. This work provides a facile approach for the fabrication of multifunctional stimuli-responsive microparticle-embedded hydrogels with semi-IPN structures, and the as-prepared microgel@PAM/CS hydrogels have great potential for applications as smart wound dressing materials in biomedical engineering.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".