Novel <i>in vitro</i> model for studying ureteric stent‐induced cell injury
Bibliographic record
Abstract
OBJECTIVE: To develop a novel in vitro model for the study of bladder and kidney epithelial cell injury akin to stent movement, as ureteric stents are associated with urinary tract complications that can significantly add to patient morbidity. These sequelae may be linked to inflammation triggered by stent-mediated mechanical injury to the urinary tract. MATERIALS AND METHODS: T24 bladder and A498 kidney cell line monolayers were damaged mechanically by segments of either Percuflex Plus (PP) or Triumph (triclosan-eluting) stents (both from Boston Scientific Corporation Inc. Natick, MA, USA) and the resulting expression profiles of several pro-inflammatory cytokines and growth factors were analysed. RESULTS: After control injury using the PP stent, supernatants of both cell lines had significantly increased levels of interleukin (IL)-6, IL-8, basic fibroblast growth factor and platelet-derived growth factor BB, and A498 cells also had increased tumour necrosis factor alpha. In almost all cases, the presence of triclosan within the media abrogated the pro-inflammatory cytokine increases, while its effects on growth factors varied. CONCLUSION: This study suggests that stent-related symptoms in the bladder and kidney may be partially due to a local inflammatory response to epithelial damage caused by the presence and movement of the stent. Future stent design should take these inflammatory responses, with respect to physical injury, into consideration, using either more biocompatible materials or anti-inflammatory compounds such as triclosan.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".