Formulation and Evaluation of Vancomycin Loaded Chitosan/Aloe Vera Hydrogel: A Novel Antibacterial Biopolymeric System
Bibliographic record
Abstract
The combination of herbal and biopolymeric agents holds significant potential for enhancing wound healing. Aloe vera, known for its anti-inflammatory, antimicrobial, and regenerative properties, has long been used to treat wounds and burns. Chitosan, as a well-known biopolymer, promotes collagen synthesis, fibroblast recruitment and aiding granulation tissue formation. This study explored the formulation of a chitosan/Aloe vera hydrogel loaded with vancomycin, as a potential wound care product. The hydrogel was prepared using chitosan and aloe vera in 1:1 and 1:2 ratios. After homogenization, 1% vancomycin was incorporated. All physical characterizations, drug loading and drug release studies were performed on prepared formulations. Antimicrobial activity also was evaluated against Staphylococcus aureus and Pseudomonas aeruginosa. Moreover, both physical and performance properties of gels were assessed over three months under room temperature and refrigerated conditions. The study found that the gels remained stable, with no changes in color, flowability, uniformity, or viscosity during stability assessments. Both formulations released their entire drug content within two hours when kept at room temperature and in the refrigerator. No signs of separation or degradation were observed over the three-month period, demonstrating the gel’s stability. Formulations showed acceptable antimicrobial activity against both mentioned bacterial strains. In conclusion, the chitosan/Aloe vera gel containing vancomycin showed desirable properties, making it a promising candidate for wound healing. Its antimicrobial activity and ability to support tissue regeneration suggest it as a valuable treatment for accelerating the wound-healing process.
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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".