Achieving Single-Nucleotide Specificity in Direct Quantitative Analysis of Multiple MicroRNAs (DQAMmiR)
Bibliographic record
Abstract
Direct quantitative analysis of multiple miRNAs (DQAMmiR) utilizes CE with fluorescence detection for fast, accurate, and sensitive quantitation of multiple miRNAs. Here we report on achieving single-nucleotide specificity and, thus, overcoming a principle obstacle on the way of DQAMmiR becoming a practical miRNA analysis tool. In general, sequence specificity is reached by raising the temperature to the level at which the probe-miRNA hybrids with mismatches melt while the matches remain intact. This elevated temperature is used as the hybridization temperature. Practical implementation of this apparently trivial approach in DQAMmiR has two major challenges. First, melting temperatures of all mismatched hybrids should be similar to each other and should not reach the melting temperature of any of the matched hybrids. Second, the elevated hybridization temperature should not deteriorate CE separation of the hybrids from the excess probes and the hybrids from each other. The second problem is further complicated by the reliance of separation in DQAMmiR on single-strand DNA binding protein (SSB) whose native structure and binding properties may be drastically affected by the elevated temperature. These problems were solved by two approaches. First, locked nucleic acid (LNA) bases were incorporated into the probes to normalize the melting temperatures of all target miRNA hybrids allowing for a single hybridization temperature; binding of SSB was not affected by LNA bases. Second, a dual-temperature CE was developed in which separation started with a high capillary temperature required for proper hybridization and continued at a low capillary temperature required for quality electrophoretic separation of the hybrids from excess probes and the hybrids from each other. The developed approach was sufficiently robust to allow its integration with sample preconcentration by isotachophoresis to achieve a limit of detection below 10 pM.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".