Antibacterial activity of combination of synthetic and biopolymer non-woven structures
Bibliographic record
Abstract
BACKGROUND: Fibrous structures and synthetic polymer blends offer potential usages in making biomedical devices, textiles used in medical practices, food packaging, tissue engineering, environmental applications and biomedical arena. These products are also excellent candidates for building scaffolds to grow stem cells for implantation, to make tissue engineering grafts, to make stents to open up blood vessels caused by atherosclerosis or narrowed by blood clots, for drug delivery systems for micro- to nano-medicines, for transdermal patches, and for healing of wounds and burn care. The current study was designed to evaluate the antimicrobial activity of woven and non-woven forms of nano- and macro-scale blended polymers having biocompatible and biodegradable characteristics. METHODS: The antimicrobial activity of non-woven fibrous structures created with the combination of synthetic and biopolymer was assessed using Gram-negative, Gram-positive bacteria, such as Staphylococcus aureus, Proteus vulgaris, Escherichia coli and Enterobacter aerogenes using pour plate method. Structural evaluation of the fabricated samples was performed by Fourier transform infrared spectroscopy. RESULTS: Broad spectrum antibacterial activities were found from the tested materials consisting of polyvinyl alcohol (PVA) with chitosan and nylon-6 combined with chitosan and formic acid. CONCLUSIONS: The combination of PVA with chitosan was more bactericidal or bacteriostatic than that of nylon-6 combined with chitosan and formic acid. PVA combination with chitosan appears to be a broad-spectrum antimicrobial agent.
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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".