Urothelial carcinoma and the potential clinical application of urinary extracellular vesicles: Current Status and prospects
Bibliographic record
Abstract
Background: Recently, urinary extracellular vesicles (uEVs) have emerged as promising biomarkers for early diagnosis, prognosis, and treatment monitoring in urothelial carcinoma (UC). uEVs encapsulate nucleic acids, proteins, and other bioactive molecules that reflect the tumor microenvironment, potentially offering a non-invasive approach for real-time cancer assessment. Methodology: A comprehensive literature review was conducted, focusing on recent studies evaluating uEVs in UC, particularly regarding their molecular contents, such as microRNA (miRNA), long non-coding RNAs (lncRNAs), circular RNA (circRNA), messenger RNA (mRNA), and proteins. Studies that assessed the clinical utility of uEVs for diagnosis, prognosis, and individualized treatment guidance in UC were emphasized. Results: Studies have identified a variety of EV-RNAs and EV-proteins as potential diagnostic and prognostic biomarkers, with some showing promise for treatment response. However, challenges in validation, limited cohort sizes, and inconsistent findings have hindered their clinical application. Liquid biopsies using uEVs are advancing UC precision medicine by improving diagnostic accuracy, identifying molecular subtypes, and potentially predicting therapeutic responses. Conclusions: UEVs are promising for UC management, offering a minimally invasive, accessible source of biomarkers for diagnosis, prognosis, and treatment monitoring. Although further research and large-scale validation are needed, the integration of uEVs into clinical practice has the potential to transform UC patient care by providing precise personalized management strategies. Continued advances in EV research and biomarker discovery may ultimately lead to more effective targeted UC therapies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".