Cranberry Derived Proanthocyanidins Reduce Bacterial Adhesion to Selected Biomaterials
Bibliographic record
Abstract
Catheter associated urinary tract infections (CAUTI) linked with the uropathogens Escherichia coli (E. coli) and Enterococcus faecalis (E. faecalis) account for the majority of nosocomial infections acquired in the clinical environment. Because these infections develop following initial adhesion of the bacterial pathogens to the catheter surface, there is increased interest in developing effective methods to inhibit attachment of cells to biomaterials used in the manufacture of indwelling devices. High molecular weight proanthocyanidins (PAC) extracted from the North American cranberry (Vaccinium macrocarpon) were examined for their potential to reduce the initial adhesion of uropathogenic bacteria (E. coli CFT073 and E. faecalis 29212) to two model biomaterials, poly(vinyl chloride) (PVC) and polytetrafluoroethylene (PTFE). Well-controlled experiments conducted in a parallel-plate flow chamber (PPFC) demonstrated decreased attachment of both bacteria to PVC and PTFE when either the bacteria, biomaterial or both surfaces were treated with PAC. Most significant inhibition of bacterial adhesion was observed for the condition where both the bacteria and biomaterial surfaces were coated with PAC. Additional experiments conducted with nonbiological model particles demonstrate comparable extents of adhesion inhibition, supporting a nonbiospecific mechanism of PAC action. The results of this study are promising for the implementation of PAC in the clinical milieu for prevention of device associated infection as the proposed functional modification is independent of antibacterial mechanisms that may give rise to resistant strains.
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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".