Mechanism of Action of Electrospun Chitosan-Based Nanofibers against Meat Spoilage and Pathogenic Bacteria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the antibacterial mechanism of action of electrospun chitosan-based nanofibers (CNFs), against Escherichia coli, Salmonella enterica serovar Typhimurium, Staphylococcus aureus and Listeria innocua, bacteria frequently involved in food contamination and spoilage. CNFs were prepared by electrospinning of chitosan and poly(ethylene oxide) (PEO) blends. The in vitro antibacterial activity of CNFs was evaluated and the susceptibility/resistance of the selected bacteria toward CNFs was examined. Strain susceptibility was evaluated in terms of bacterial type, cell surface hydrophobicity, and charge density, as well as pathogenicity. The efficiency of CNFs on the preservation and shelf life extension of fresh red meat was also assessed. Our results demonstrate that the antibacterial action of CNFs depends on the protonation of their amino groups, regardless of bacterial type and their mechanism of action was bactericidal rather than bacteriostatic. Results also indicate that bacterial susceptibility was not Gram-dependent but strain-dependent, with non-virulent bacteria showing higher susceptibility at a reduction rate of 99.9%. The susceptibility order was: E. coli > L. innocua > S. aureus > S. Typhimurium. Finally, an extension of one week of the shelf life of fresh meat was successfully achieved. These results are promising and of great utility for the potential use of CNFs as bioactive food packaging materials in the food industry, and more specifically in meat quality preservation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it