Podocyte extracellular vesicles and immune mediators as urinary biomarkers in active lupus nephritis
Bibliographic record
Abstract
Urinary extracellular vesicles (uEVs) and immune mediators have emerged as potential minimally invasive renal biomarkers. Even though active lupus nephritis (LN) is associated with immune complex deposition, tissue inflammation, and podocyte damage, it remains unclear how these parameters are simultaneously altered in systemic lupus erythematosus (SLE). Thus, we aimed to evaluate uEVs as biomarkers in LN, in association with urinary immune mediators. In this cross-sectional study, uEVs were isolated from SLE patients and healthy donors by differential centrifugation and characterized and/or quantified by electron microscopy, nanoscale flow cytometry, and nanoparticle tracking analysis (NTA). Urinary immune mediators were assessed by a multiplex assay. We included 82 patients (42.6 ± 11.3 years-old, 91.4% female), of whom 56.1% (n = 46) had LN, and 18 healthy donors (37.5 ± 8.2 years-old, 83.3% female). No differences were found for particle size/concentration by NTA, but higher counts of total (P = 0.03) and podocyte-derived (P = 0.01) uEVs were observed in SLE patients, especially in active LN (P = 0.02; P = 0.03). We also identified higher urinary levels of cytokines such as IL-6, IL-8, and CCL-2 according to SLE activity and LN (P < 0.05). Significant correlations were observed between uEVs, immune mediators, R-SLEDAI-2K, proteinuria, and albuminuria in active LN. Lastly, the combinatory analysis of podocyte uEVs, IL-6, IFN-γ, IL-8, uCCL-2 and CCL-3 showed a good predictive power to detect active LN (AUC = 0.88, P = 0.0009). Our results suggest that urinary podocyte-derived uEVs and cytokines are associated with LN activity, which may reflect podocyte injury mediated by inflammation. Thus, the combined application of these biomarkers could help to identify patients with podocyte damage and renal inflammation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".