The interplay between bacterial biofilms, encrustation, and wall shear stress in ureteral stents: a review across scales
Bibliographic record
Abstract
Ureteral stents are hollow tubes that are inserted into the ureter to maintain the flow of urine from the kidney to the bladder. However, the use of these indwelling stents is associated with potential complications. Biofilm, an organized consortium of bacterial species embedded within a self-producing extracellular matrix, can attach to the outer and inner surfaces of ureteral stents. Furthermore, encrustation - defined as the buildup of mineral deposits on the stent surface - can occur independently or in parallel with biofilm formation. Both phenomena can cause stent obstruction, which can lead to obstructive pyelonephritis and make stent removal difficult. Understanding the influence of flow on the development of biofilm and encrustation and the impact of small mechanical environmental changes (e.g., wall shear stress distribution) is key to improve the long-term performance of stents. Identifying the optimal stent properties to prevent early bacterial attachment and/or crystal deposition and their growth, would represent a breakthrough in reducing biofilm-/encrustation-associated complications. This review identifies the most prevalent bacterial strains and crystal types associated with ureteral stents, and the process of their association with the stent surface, which often depends on patient comorbidities, stent material, and indwelling time. Furthermore, we focus on the often-overlooked role of fluid dynamics on biofilm and encrustation development in ureteral stents, across a range of physical scales (i.e., from micro- to macro-scale) with the aim of providing a knowledge base to inform the development of safer and more effective ureteral stents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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".