Bacteria-triggered release of a potent biocide from core-shell polyhydroxyalkanoate (PHA)-based nanofibers for wound dressing applications
Bibliographic record
Abstract
Bacterial infections are a serious issue in wound healing. Extensive use of biocides in wound dressings have raised concerns of biocide resistance and unnecessary harm to normal skin cells. In this paper, we report a new approach to realize bacteria-triggered release of a biocide to the sites of bacterial infections from core-shell polyhydroxyalkanoate (PHA)-based nanofibers prepared by coaxial electrospinning. The hydrophobic PHA-based shell can prevent the biocide from undesirable payload release in physiological environments without pathogens. However, in the presence of pathogens, the PHA-based shell is degraded by the pathogens, and the encapsulated biocide is released. The released biocide subsequently can exert targeted antimicrobial effects on the bacteria. Using Pseudomonas aeruginosa as a model bacterium and dodecyltrimethylammonium chloride as a model biocide, we demonstrated that the core-shell PHA-based nanofibers effectively released encapsulated dodecyltrimethylammonium chloride in the presence of Pseudomonas aeruginosa, resulting in targeted inactivation of Pseudomonas aeruginosa cells.HighlightsUnique core-shell nanofibers were successfully fabricated from PHAs generated by bacteria.An on-demand release of biocide was achieved from a PHA-based core-shell nanofibours membrane.The membrane’s mechanical properties closely match those of the human skin.
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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".