<i>Second Prize</i> : <i>In-Vitro</i> Activity of Triclosan-Eluting Ureteral Stents against Common Bacterial Uropathogens
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Ureteral stents are commonly used in urology today, but the biofilms that form on them within hours of placement may harbor bacteria that can result in infection or encrustation. Triclosan is an antimicrobial commonly used in consumer and medical products that inhibits bacterial fatty-acid synthesis. The bactericidal and bacteriostatic effect of a triclosan-eluting ureteral stent was tested against clinical isolates of common bacterial uropathogens in an in-vitro setting. MATERIALS AND METHODS: Triclosan eluted from a drug-loaded ureteral stent was suspended in artificial urine with bacterial pathogens (Escherichia coli C1214, Proteus mirabilis 296, Enterococcus faecalis 1131, Klebsiella pneumoniae 280, Staphylococcus aureus Newman, Pseudomonas aeruginosa AK1) to assess growth, virulence-promoter activity, and bacterial adherence to the stent. Generic stents were utilized as controls. RESULTS: Triclosan inhibited the growth of E. faecalis, K. pneumoniae, S. aureus, and P. mirabilis in a dose-dependent manner. Pseudomonas aeruginosa demonstrated significant resistance. Lower concentrations of triclosan downregulated E. coli virulence-factor promoters of outer membrane protein X and p-fimbriae. Triclosan stents had significantly fewer adherent viable bacteria than control stents. CONCLUSIONS: Triclosan-eluting ureteral stents inhibit the growth of common bacterial uropathogens and thus may reduce the incidence of urinary-tract infections and, potentially, encrustation. This drug-eluting stent provides both mechanical drainage of the upper urinary tract and local antibiosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".