Urine Cell-Free RNA vs Plasma Cell-Free RNA for Monitoring of Kidney Injury and Immune Complications
Bibliographic record
Abstract
BACKGROUND: There is increasing interest in the use of circulating cell-free RNA (cfRNA) in plasma as an analyte for diagnosing and monitoring disease. While it is known that cfRNA can also be isolated from urine, the diagnostic potential of urine cfRNA, particularly relative to plasma cfRNA, remains underexplored. METHODS: Matched plasma and urine were collected from hematopoietic stem cell transplant (HSCT) recipients (n = 24), immune-checkpoint-inhibitor (ICI) recipients with or without acute kidney injury (AKI) (n = 46), and healthy volunteers (n = 5), yielding 297 samples. Unbiased cfRNA sequencing was performed, followed by comparison of molecular diversity, tissue and cellular origin, and diagnostic performance for systemic (HSCT) and renal (AKI) complications. RESULTS: Urine and plasma cfRNA displayed distinct molecular composition and cellular origin across all groups. In HSCT, pronounced changes in plasma cfRNA were detected during the course of treatment, while urine cfRNA changes were minimal. Conversely, when comparing ICI recipients with and without AKI, cfRNA signatures indicative of disease and AKI etiology were observed in urine but not in plasma. These urine-derived signatures included injury markers and immune transcripts consistent with localized renal inflammation. CONCLUSIONS: This study reveals the distinct origin and diagnostic utility of plasma and urine cfRNA and suggests urine cfRNA is a promising analyte to monitor kidney injury, especially in the context of AKI following ICI treatment.
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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 | 0.000 |
| 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".