Identification and detection of microRNA kidney disease biomarkers in liquid biopsies
Bibliographic record
Abstract
PURPOSE OF REVIEW: MicroRNAs (miRNAs) are emerging rapidly as a novel class of biomarkers of major organ disorders, including kidney diseases. However, current PCR-based detection methods are not amenable to development for high-throughput, cost-effective miRNA biomarker quantification. RECENT FINDINGS: MiRNA biomarkers show significant promise for diagnosis and prognosis of kidney diseases, including diabetic kidney disease, acute kidney injury, IgA nephropathy and delayed graft function following kidney transplantation. A variety of novel methods to detect miRNAs in liquid biopsies including urine, plasma and serum are being developed. As miRNAs are functional transcripts that regulate the expression of many protein coding genes, differences in miRNA profiles in disease also offer clues to underlying disease mechanisms. SUMMARY: Recent findings highlight the potential of miRNAs as biomarkers to detect and predict progression of kidney diseases. Developing in parallel, novel methods for miRNA detection will facilitate the integration of these biomarkers into rapid routine clinical testing and existing care pathways. Validated kidney disease biomarkers also hold promise to identify novel therapeutic tools and targets. VIDEO ABSTRACT: http://links.lww.com/CONH/A43.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".