Exploring Urinary Extracellular Vesicles and Immune Mediators as Biomarkers of Kidney Injury in COVID-19 Hospitalized Patients
Bibliographic record
Abstract
Kidney injury is an important outcome associated with COVID-19 severity. In this regard, alterations in urinary extracellular vesicles (uEVs) could be detected in the early phases of renal injury and may be reflective of the inflammatory process. This is an observational study performed with a case series of COVID-19 hospitalized patients presenting mild-to-critical disease. Total and podocyte-derived uEVs were identified by nanoscale flow cytometry, and urinary immune mediators were assessed by a multiplex assay. We studied 36 patients, where 24 (66.7%) were considered as mild/moderate and 12 (33.3%) as severe/critical. Increased levels of total uEVs were observed (p = 0.0001). Importantly, total uEVs were significantly higher in severe/critical patients who underwent hemodialysis (p = 0.03) and were able to predict this clinical outcome (AUC 0.93, p = 0.02). Severe/critical patients also presented elevated urinary levels (p < 0.05) of IL-1β, IL-4, IL-6, IL-7, IL-16, IL-17A, LIF, CCL-2, CCL-3, CCL-11, CXCL-10, FGFb, M-CSF, and CTAcK. Lastly, we observed that total uEVs were associated with urinary immune mediators. In conclusion, our results show that early alterations in urinary EVs could identify patients at higher risk of developing renal dysfunction in COVID-19. This could also be relevant in different scenarios of systemic and/or infectious disease.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".