Unlocking the potential of extracellular vesicles in nephrology: what does MISEV2023 add?
Bibliographic record
Abstract
Extracellular vesicles, small membrane-bound packages secreted by virtually all cells of the body, have become a focus of interest in nephrology over the recent years. After the first characterization of their proteomic and transcriptomic content, scientific attention shifted toward their potential as biomarkers for kidney diseases both as diagnostic and monitoring tools. More recently, researchers have begun exploring whether extracellular vesicles mediate intercellular signaling inside the nephron and between the kidney and other organs throughout the body. Nevertheless, the field of extracellular vesicle research has struggled to translate major findings to the clinical context due to numerous methods to separate extracellular vesicles, yielding fractions of different sizes and varying purity, unclear terminology, and, hence, limitations concerning reproducibility. The International Society of Extracellular Vesicles, therefore, has striven to reduce these barriers by an ongoing initiative to increase rigor and standardization of extracellular vesicle research. The "Minimal Information for Studies of Extracellular Vesicles" guideline is the result of this initiative and, in its now third iteration, provides the most concise suggestions for investigating extracellular vesicles to date. This mini review illustrates the advances made in extracellular vesicle research in nephrology so far using informative examples, outlines the advances made by the former Minimal Information for Studies of Extracellular Vesicles guidelines, and shows what potential using the latest iteration holds.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".