Chitosan hydrogel loaded with <scp><i>Aloe vera</i></scp> gel and tetrasodium ethylenediaminetetraacetic acid (<scp>EDTA</scp>) as the wound healing material: in vitro and in vivo study
Bibliographic record
Abstract
Abstract The main aim of the current study was to develop a chitosan hydrogel containing Aloe vera gel and Ethylenediaminetetraacetic acid (EDTA) as the wound healing materials. Chitosan with the concentration of (2% w/v) was prepared in AA (0.5%, v/v) and Tetrasodium EDTA (0.01% w/w) and AV (0.5% v/v) were added to the prepared polymer solution. As prepared solution was cross‐linked by β‐GP with the weight ratio of 1/6 w/w (1 chitosan and 6 β‐GP). The characterization of the hydrogels showed that the hydrogels have porous structures and interconnected pores with the pores size range from 41.5 ± 14 to 48.3 ± 11 μm. The swelling and weight loss measurements of the hydrogels showed that the hydrogels could swell up to 240% of their initial weight during 8 h and loss 79.7 ± 3.5% of the initial weight during 14 days. The antibacterial studies depicted that the prepared Cs/tEDTA/AV hydrogel inhibited the growth of Staphylococcus aureus (the minimum inhibition concentration, MIC of 73 ± 4.8) and Pseudomonas aeruginosa (the MIC of 40 ± 7.9). Moreover, the prepared hydrogels were hemocompatible (Cs/tEDTA/AV: OD of 0.24 ± 0.30) and biocompatible (Cs/tEDTA/AV: OD of 0.38 ± 0.01). At the final stage, the wound healing assessments in the animal model revealed that the application of the prepared hydrogels effectively enhanced the wound healing process. In conclusion, the results confirmed the efficacy of the prepared hydrogels as the wound healing materials.
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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".