Sodium Alginate Substrate Coated with PVA/Nanosilver Composite Nanofibers for Skin Tissue Engineering
Bibliographic record
Abstract
The established potential of sodium alginate (SA) in tissue engineering and regenerative medicine underscores its significance.This study involved coating a sodium alginate substrate (SA) with nanofibers through the electrospinning of a polyvinyl alcohol (PVA) solution loaded with various antimicrobial agents, specifically silver nanoparticles (AgNPs).The coated nanofibers underwent comprehensive physiochemical, biological, and morphological characterization.The analysis of the coated nanofibers included techniques such as Field Emission Scanning Electron Microscopy (FESEM) and Fourier Transform Infrared Spectroscopy (FTIR).The contact angle was measured using the sessile drop method.Microbiological assays were conducted to evaluate the effectiveness against Staphylococcus aureus (S. aureus).Additionally, cell viability was assessed using MTT assays on the AD-MSC cell line.In vitro assays demonstrated the excellent biocompatibility of the coated nanofibers in cell culture.The SA/PVA/AgNPs-coated nanofibers exhibited inhibitory effects on the growth and proliferation of Staphylococcus aureus bacteria.The findings suggest that these novel coated nanofibers hold promise for the development of sustainable biomaterials for skin tissue engineering.
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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".