Fabrication of Xanthan gum: Gelatin (Xnt:Gel) Hybrid Composite Hydrogels for Evaluating Skin Wound Healing Efficacy
Bibliographic record
Abstract
With the background of snowballing threat of skin wound to public health and economy, this study was undertaken utilizing xanthan gum (Xnt), citric acid (C), gelatin (Gel), glutaraldehyde (G) and HPLC-grade water to fabricate a series of composite hydrogels i.e. Xnt, Xnt:C, Xnt:Gel(3):G, Xnt:C:Gel(3):G, Xnt:Gel(5):G, Xnt:C:Gel(5):G for investigating their wound healing efficacy in experimental rat skin wound model. Physicochemical characterization revealed that all the composite hydrogels contained more than 90% water. The hydrogels displayed swelling ability, biodegradability, good polymeric networks and porosity. Fourier Transform Infrared Spectroscopy (FT-IR) studies confirmed the presence of bound water and free, intra and inter molecular bound hydrogen bonded OH and NH in the hydrogels. All the hydrogels showed significant wound healing potency in experimental deep second degree skin burns in rats compared to controls. 20 days post-application of hydrogels, Xnt:Gel(3):G, Xnt:Gel(5):G and Xnt:C:Gel(5):G-treated wounds showed better recovery compared to other composite hydrogels. We conclude that, Xnt:Gel(3):G, Xnt:Gel(5):G and Xnt:C:Gel(5):G might be effective wound dressing material.
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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".