A Survey of Current Trends in Urinary Extracellular Vesicle Research
Bibliographic record
Abstract
Background: The Urine Task Force of the International Society of Extracellular Vesicles (ISEV) was created to advocate for best practices in this emerging area of research. Here we present the results of a survey, conducted using SurveyMonkey by the Urine Task Force, to better understand current research practices in the study of urinary extracellular vesicles (uEVs). Methods: Two separate, identical surveys were administered: one directed to Urine Task Force members (28 respondents at the time of reporting) and one directed to the ISEV community (42 respondents). Results: The mean time studying uEVs was 7.8 years for task force members and 5.4 years for community respondents. For task force members: 48.3% of respondents primarily focus on kidney, 44.8% prostate and 6.9% bladder. For the community focus was 29.4% kidney, 21.5% prostate, 19.6% bladder and 29.4% “other”. Both communities largely collect spot urines compared with timed collection (Task force: 78.6% spot vs 21.4% timed, Community: 75.8% spot vs 24.2% timed). Urine storage was a significant focus of the survey. For the Task Force: 92.9% of respondents studied samples stored >3 months, 57.1% samples stored <3 months, and 50% studied fresh samples. The community studied less often fresh samples. Both groups predominantly stored samples as “cell-free urine”: 85.7% for task force and 65.6% for community. All task force respondents study samples frozen at -80 °C with 10.7% of respondents also studying samples stored at 4 °C. By contrast, 93.8% -of community respondents stored samples at 80 °C, 9.4% at -20 °C and 6.3% at 4 °C. The task force ranked the following isolation methods in order of priority 1) centrifugation, 2) size exclusion chromatography, and 3) filtration. For the community survey this was similar. Both surveys prioritized the same downstream applications: 1) protein analysis, 2) RNA analysis, 3) functional assays. The urine task force identified “understanding of approaches to normalization” while the community identified “Impact of renal disease and comorbidities on EV analysis” as key knowledge gaps. Conclusions: In summary, the present survey identified key similarities and differences between current practices for the Urine Task Force and the urinary EV research community. Such information will be used to help guide future efforts to address key knowledge gaps.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".