Highly-Sensitive Amplification-Free Analysis of Multiple miRNAs by Capillary Electrophoresis
Bibliographic record
Abstract
Sets of deregulated microRNAs (miRNAs), termed miRNA signatures, are promising biomarkers for cancer. Validation of miRNA signatures requires a technique that is accurate, sensitive, capable of detecting multiple miRNAs, fast, robust, and not cost-prohibitive. Direct quantitative analysis of multiple miRNAs (DQAMmiR) is a capillary electrophoresis (CE)-based hybridization assay that was suggested as a methodological platform for validation and clinical use of miRNA signatures. While satisfying the other requirements, DQAMmiR is not sufficiently sensitive to detect low-abundance miRNAs. Here, we solve this problem by combining DQAMmiR with the preconcentration technique, isotachophoresis (ITP). The sensitivity improved 100 times (to 1 pM) allowing us to detect low-abundance miRNAs in an RNA extract. Importantly, ITP-DQAMmiR can be performed in a fully automated mode using a commercial CE instrument making it suitable for practical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".