Understanding urinary conditioning film components on ureteral stents: profiling protein components and evaluating their role in bacterial colonization
Bibliographic record
Abstract
Ureteral stents are fraught with problems. A conditioning film attaches to the stent surface within hours of implantation; however, differences between stent types and their role in promoting encrustation and bacterial adhesion and colonization remain to be elucidated. The present work shows that the most common components do not differ between stent types or patients with the same indwelling stent, and contain components that may drive stent encrustation. Furthermore, unlike what was previously thought, the presence of a conditioning film does not increase bacterial adhesion and colonization of stents by uropathogens. Genitourinary cytokeratins are implicated in playing a significant role in conditioning film formation. Overall, stent biomaterial design to date has been unsuccessful in discovering an ideal coating to prevent encrustation and bacterial adhesion. This current study elucidates a more global understanding of urinary conditioning film components. It also supports specific focus on the importance of physical characteristics of the stent and how they can prevent encrustation and bacterial adhesion.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".