Reducing deposition of encrustation in ureteric stents by changing the stent architecture: A microfluidic-based investigation
Bibliographic record
Abstract
Ureteric stents are clinically deployed to retain ureteral patency in the presence of an obstruction of the ureter lumen. Despite the fact that multiple stent designs have been researched in recent years, encrustation and biofilm-associated infections remain significant complications of ureteral stenting, potentially leading to the functional failure of the stent. It has been suggested that "inactive" side-holes of stents may act as anchoring sites for encrusting crystals, as they are associated with low wall shear stress (WSS) levels. Obstruction of side-holes due to encrustation is particularly detrimental to the function of the stent, since holes provide a path for urine to by-pass the occlusion. Therefore, there is an unmet need to develop novel stents to reduce deposition of encrusting particles at side-holes. In this study, we employed a stent-on-chip microfluidic model of the stented and occluded ureter to investigate the effect of stent architecture on WSS distribution and encrustation over its surface. Variations in the stent geometry encompassed (i) the wall thickness and (ii) the shape of side-holes. Stent thickness was varied in the range 0.3-0.7 mm, while streamlined side-holes of triangular shape were evaluated (with a vertex angle in the range 45°-120°). Reducing the thickness of the stent increased WSS and thus reduced the encrustation rate at side-holes. A further improvement in performance was achieved by using side-holes with a triangular shape; notably, a 45° vertex angle showed superior performance compared to other angles investigated, resulting in a significant increase in WSS within "inactive" side-holes. In conclusion, combining the optimal stent thickness (0.3 mm) and hole vertex angle (45°) resulted in a ∼90% reduction in encrustation rate within side-holes, compared to a standard design. If translated to a full-scale ureteric stent, this optimised architecture has the potential for significantly increasing the stent lifetime while reducing clinical complications.
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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) | 0.000 | 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 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".