Reduction of <i>Staphylococcus aureus</i> and <i>Pseudomonas aeruginosa</i> colonisation on PVC through covalent surface attachment of fluorinated thiols
Bibliographic record
Abstract
Abstract Objectives This study reports the development, characterisation and microbiological testing of surface-modified polyvinylchloride (PVC) films for the purpose of reducing bacterial adherence. Methods Irreversible covalent surface modification was achieved via nucleophilic substitution of fluorinated thiol-terminated compounds onto the polymer backbone. Four fluorinated modifiers, 2,3,5,6-tetrafluorothiophenol (TFTP), 4-(trifluoromethyl)thiophenol (TFMTP), 3,5-bis(trifluoromethyl)benzenethiol (BTFMBT) and 3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,10-heptadecafluoro-decane-1-thiol (HDFDT), were investigated. Modification was confirmed using attenuated total reflectance infrared spectroscopy; Raman mapping demonstrated that modification was homogenous on the macroscopic scale. The influence of fluorination on surface hydrophobicity was studied by contact angle analysis. The effect on microbial adherence was examined using Pseudomonas aeruginosa and Staphylococcus aureus. Key findings The resultant changes in contact angle relative to control PVC ranged from −4° to +14°. In all cases, adherence of P. aeruginosa and S. aureus was significantly reduced relative to control PVC, with adherence levels ranging from 62% and 51% for TFTP-modified PVC to 32% and 7% for TFMTP-modified PVC. Conclusions These results demonstrate an important method in reducing the incidence of bacterial infection in PVC medical devices without compromising mechanical 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) | 0.001 | 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".