Wound‐Healing with Mechanically Robust and Biodegradable Hydrogel Fibers Loaded with Silver Nanoparticles
Bibliographic record
Abstract
The objective of this study is to provide a novel synthetic approach for the manufacture of wound-healing materials using covalently cross-linked alginate fibers loaded with silver nanoparticles. Alginate fibers are prepared by wet-spinning in a CaCl(2) precipitation bath. Using this same approach, calcium cross-links in alginate fibers are replaced by chemical cross-links that involve hydroxyl groups for subsequent cross-linking by glutaraldehyde. The cross-linked fibers become highly swollen in aqueous solution due to the presence of carboxyl functional groups, and retain their mechanical stability in physiological fluids owing to the stabilized network of covalent bonds. Alginate fibers can then be loaded with silver ions via the ion-exchange reaction. Silver ions are reduced to yield 11 nm silver nanoparticles incorporated in the polymer gel. This method provides a convenient platform to incorporate silver nanoparticles into alginate fibers in controlled concentrations while retaining the mechanical and swelling properties of the alginate fibers. Our study suggests that the silver nanoparticles loaded fibers may be easily applied in a wound healing paradigm and promote the repair process though the promotion of fibroblast migration to the wound area, reduction of the inflammatory phase, and the increased epidermal thickness in the repaired wound area, thereby improving the overall quality and speed of 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".