Antibacterial and Hemocompatible pH-Responsive Hydrogel for Skin Wound Healing Application: In Vitro Drug Release
Bibliographic record
Abstract
The treatment of successive skin wounds necessitates meticulous medical procedures. In the care and treatment of skin wounds, hydrogels produced from natural polymers with controlled drug release play a crucial role. Arabinoxylan is a well-known and widely available biological macromolecule. We produced various formulations of blended composite hydrogels (BCHs) from arabinoxylan (ARX), carrageenan (CG), and reduced graphene oxide (rGO) using and cross-linked them with an optimal amount of tetraethyl orthosilicate (TEOS). The structural, morphological, and mechanical behavior of the BCHs samples were determined using Fourier-transform infrared spectroscopy (FT-IR), Scanning electron microscope (SEM), mechanical testing, and wetting, respectively. The swelling and degradation assays were performed in phosphate-buffered saline (PBS) solution and aqueous media. Maximum swelling was observed at pH 7 and the least swelling in basic pH regions. All composite hydrogels were found to be hemocompatible. In vitro, silver sulfadiazine release profile in PBS solution was analyzed via the Franz diffusion method, and maximum drug release (87.9%) was observed in 48 h. The drug release kinetics was studied against different mathematical models (zero-order, first-order, Higuchi, Hixson–Crowell, Korsmeyer–Peppas, and Baker–Lonsdale models) and compared their regression coefficient (R2) values. It was observed that drug release follows the Baker–Lonsdale model, as it has the highest value (0.989) of R2. Hence, the obtained results indicated that, due to optimized swelling, wetting, and degradation, the blended composite hydrogel BCH-3 could be an essential wound dressing biomaterial for sustained drug release for skin wound care and 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) | 0.000 | 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 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".