Fabrication of Wound Dressing Cotton Nano-Composite Coated with Tragacanth/Polyvinyl Alcohol: Characterization and In Vitro Studies
Bibliographic record
Abstract
Wound dressing made from biomaterials has been illustrated promising to treat subcutaneous injuries. The paper presents a novel method for the in situ synthesis of silver nanoparticle on cotton fabric with reducing agent and in vitro characterization of tragacanth/polyvinyl alcohol (PVA) wound dressing with curcumin. For synthesizing the wound dressings, nanosilver was used as the carrier for controlled release of curcumin and then coated, along with tragacanth/PVA hydrogels, on the cotton fabric that was used to provide mechanical support to the dressing. For characterizing the wound dressings, scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR), contact angle measurement were performed. Also, these wound dressings were evaluated in vitro for drug release, cell culture and MTT analysis. Our results showed that the addition of curcumin could decrease the cell cytotoxicity, thus improving cell viability of the wound dressings. The measurements of contact angle indicated that with the addition of the PVA and tragacanth, the hydrophobicity of the wound dressing could be improved, while the SEM results illustrate the presence of the in situ synthesized coated nanosilver in the dressings. The loading efficiency on the fabric was around 85% and the in-vitro release profile of curcumin showed 42% burst release. Taken together, this study illustrates that fabricated wound dressing composite have the appropriate swelling capacity, mechanical and biological properties for wound healing.
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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".