Extracellular Vesicles in Kidney Diseases: Moving Forward
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it