Extracellular Vesicles: Cell-Derived Biomarkers of Glomerular and Tubular Injury
Bibliographic record
Abstract
Extracellular vesicles (EVs) are important mediators of intercellular communication. Since EVs are also released during pathological conditions, there has been considerable interest in their potential as sensitive biomarkers of cellular stress and/or injury. In the context of kidney disease, urinary EVs are promising indicators of glomerular and tubular damage. In the present review we discuss the role of urinary EVs in kidney health and disease. Our focus is to explore urinary large EVs (lEVs, often referred to as microparticles or microvesicles) as direct and noninvasive early biomarkers of renal injury. In this regard, studies have been demonstrating altered levels of urinary lEVs, especially podocyte-derived lEVs, preceding the decrease of renal function assessed by classical markers. In addition, we discuss the role of small EVs (sEVs, often referred to as exosomes) and their contents in kidney pathophysiology. Even though results concerning the production of sEVs during diseased conditions are varied, there has been a consensus on the importance of urinary sEV content assessment in kidney disease. These mediators, including EV-released miRNAs and mRNAs, are responsible for EV-mediated signaling in the regulation of renal cellular homeostasis, pathogenesis and regeneration. Finally, steps necessary for the validation of EVs as reliable markers will be discussed.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".