Three-Mode Electrochemical Sensing of Ultralow MicroRNA Levels
Bibliographic record
Abstract
MicroRNAs (miRNAs) are an emerging class of biomarkers that are frequently deregulated in cancer cells and have shown great promise for cancer classification and prognosis. In this work, we developed a three-mode electrochemical sensor for detection and quantitation of ultralow levels of miRNAs in a wide dynamic range of measured concentrations. The sensor facilitates three detection modalities based on hybridization (H-SENS), p19 protein binding (P-SENS), and protein displacement (D-SENS). The combined three-mode sensor (HPD-SENS) identifies as low as 5 aM or 90 molecules of miRNA per 30 μL of sample without PCR amplification, and can be operated within the dynamic range from 10 aM to 1 μM. The HPD sensor is made on a commercially available gold nanoparticles-modified electrode and is suitable for analyzing multiple miRNAs on a single electrode. This three-mode sensor exhibits high selectivity and specificity and was used for sequential analysis of miR-32 and miR-122 on one electrode. In addition, the H-SENS can recognize miRNAs with different A/U and G/C content and distinguish between a fully matched miRNA and a miRNA comprising either a terminal or a middle single base mutation. Furthermore, the H- and P-SENS were successfully employed for direct detection and profiling of three endogenous miRNAs, including hsa-miR-21, hsa-miR-32, and hsa-miR-122 in human serum, and the sensor results were validated by qPCR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".