The Interaction of Urinary Components with Biomaterials in the Urinary Tract: Ureteral Stent Discoloration
Bibliographic record
Abstract
Purpose: Ureteral stents are fraught with complications. Not much is known regarding what causes these issues. Studies suggested that discoloration of indwelling stents changes surface characteristics to promote stent-associated complications. Occasional stent discoloration has been observed; however, underlying mechanisms and potential material changes are unknown. In this study, we identify a potential mechanism for stent discoloration and characterize potential changes in stent surface characteristics and their impact on encrustation and bacterial colonization. Materials and Methods: Twenty Polaris Ultra and 20 Percuflex Plus stents (same polymer material) with varying degrees of discoloration were collected from Japanese and Canadian patients. Surface characterization using scanning electron microscopy and Fourier transmission infrared spectroscopy was conducted. Encrustation of variably discolored stents was assessed via atomic absorption spectroscopy and bacterial adhesion to discolored and control stents via colony-forming unit counts. Results: Bismuth subcarbonate was found in control stents, and discoloration was induced via incubation in 1% sodium sulfide and increasing concentrations of hydrochloric acid (HCl) to produce hydrogen sulfide (H 2 S). Discoloration of either patient-derived stents or in vitro discolored stents did not result in significant changes in stent material or increased rates of encrustation or bacterial adhesion. Conclusions: Ureteral stent discoloration may be triggered by sulfur containing urinary components reacting with bismuth subcarbonate in the stent material, rather than the surface deposition of unusual crystals as previously proposed. Stent discoloration does not result in increased levels of encrustation or bacterial adhesion. Given this, stent discoloration appears to be cosmetic and has no effect on the functionality of the indwelling stent.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".