Extracellular Vesicles in Kidney Diseases: Moving Forward
Bibliographic record
Abstract
Extracellular vesicles (EVs) are evolving as novel cell mediators, biomarkers, and therapeutic targets in kidney health and disease. They are naturally derived from cells both within and outside the kidney and carry cargo which mirrors the state of the parent cell. Thus, they are potentially more sensitive and disease-specific as biomarkers and messengers in various kidney diseases. Beside their role as novel communicators within the nephron, they likely communicate between different organs affected by various kidney diseases. Study of urinary EVs (uEVs) can help to fill current knowledge gaps in kidney diseases. However, separation and characterization are challenged by their heterogeneity in size, shape, and cargo. Fortunately, more sensitive and direct EV measuring tools are in development. Many clinical syndromes in nephrology from acute to chronic kidney and glomerular to tubular diseases have been studied. Yet, validation of biomarkers in larger cohorts is warranted and simpler tools are needed. Translation from in vitro to in vivo studies is also urgently needed. The therapeutic role of uEVs in kidney diseases has been studied extensively in rodent models of AKI. On the basis of the current exponential growth of EV research, the field of EV diagnostics and therapeutics is moving forward.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".