Synthesis and characterization of a polymeric network made of polyethylene glycol and chitosan as a treatment with antibacterial properties for skin wounds
Bibliographic record
Abstract
Polyethylene glycol has been widely investigated for wound healing and dressing applications. Despite its advantages (i.e. great biocompatibility), polyethylene glycol lacks antibacterial activity. For this reason, semi-interpenetrated polymeric networks were prepared by combining a chemically cross-linked polyethylene glycol network with chitosan. The aim of this work was to identify the best amount of chitosan able to improve the antibacterial properties against Staphylococcus aureus. Briefly, the networks were synthesized by a sequential method, adding chitosan in different proportion to the polyethylene glycol. The antibacterial activity was tested following the MGA 0100 of the Pharmacopeia of the United States of Mexico. Fourier-transform infrared with attenuated total reflection spectroscopy, scanning electron microscopy and swelling behavior PBS at 37° C and room temperature were also performed to characterize the polymeric networks. The results showed that PC-2% was able to inhibit the bacterial growth of Staphylococcus aureus even more than Fosfomycin antibiotic. The networks showed cylindrical pores of different sizes (50–100 µm). The maximum swelling of all the networks was achieved in PBS at 37°C (>315%). Free hemoglobin and hemolysis assays were also evaluated to know the compatibility with erythrocytes. Human dermal fibroblasts were used to evaluate direct cytotoxicity. Therefore, the produced gels exerted interesting antibacterial activity and showed good biocompatibility properties.
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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) | 0.000 | 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".