Three-Mode Electrochemical Sensing of Ultralow MicroRNA Levels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it