Assessment of chitosan-PVA hydrogels infused with marine collagen peptides for potential wound healing applications
Bibliographic record
Abstract
• Marine collagen peptides (CP) were incorporated into chitosan-PVA hydrogels using non-toxic procedure with no crosslinkers. • Mechanical and biological properties were measured for potential wound-healing activity. • Swelling ratio was high and compressive strength of hydrogels was very good. • Higher PVA amounts did not improve the mechanical properties significantly. • Marine collagen peptide (CP) increased antimicrobial effect and hemostatic properties, yet showed low toxicity. Ideal wound dressings should show enhanced moisture management at the wound site, antibacterial and physical barrier, and mechanical robustness. Additionally, it should be easy to apply to the wound and be biocompatible and non-toxic. In this study, a linker-free freeze-thaw procedure was used to create an array of chitosan/PVA hydrogels blended with commercially available marine collagen peptides. Marine collagen peptides (CP) are easily available as by-products of the marine food industry and are an inexpensive and novel source of biomaterial in this field. The different weight ratios of chitosan, PVA, and CP influenced the hydrogel properties such as swelling, gel content, evaporation, and mechanical properties. Furthermore, SEM and ATR-FTIR were used to characterize the hydrogels generated under ideal conditions. After 24 h, the optimum hydrogel (chitosan:PVA:CP ratio of 1:5:1) showed a water absorption capacity of up to 900 %, a gel content of 80 %, and a 40 % evaporation rate. The physical interactions between marine collagen peptide and gel-forming components were validated by ATR-FTIR spectra, and the hydrogel kept a sufficient porous structure for potential wound dressing application. To test the mechanical integrity of the hydrogels, compression testing was carried out showing a compressive modulus of up to ∼40 kPa. The addition of marine collagen peptide in the chitosan/PVA hydrogel increased its wettability, antimicrobial capabilities, and hemostatic properties. Furthermore, the hydrogel preparation procedure is simple and does not use toxic chemicals, serving as a model for developing safe and effective hydrogel wound dressing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".