Protein Electrocatalysis for Direct Sensing of Circulating MicroRNAs
Bibliographic record
Abstract
MicroRNAs (miRNAs) are potentially useful biomarkers for diagnosis, classification, and prognosis of many diseases, including cancer. Herein, we developed a protein-facilitated electrocatalytic quadroprobe sensor (Sens(PEQ)) for detection of miRNA signature of chronic lymphocytic leukemia (CLL) in human serum. The developed signal-ON sensor provides a compatible combination of two DNA adaptor strands modified with four methylene blue molecules and electrocatalysis using glucose oxidase in order to enhance the overall signal gain. This enhanced sensitivity provided the response necessary to detect the low-abundant serum miRNAs without preamplification. The developed Sens(PEQ) is exquisitely sensitive to subtle π-stack perturbations and capable of distinguishing single base mismatches in the target miRNA. Furthermore, the developed sensor was employed for profiling of three endogenous miRNAs characteristic to CLL, including hsa-miR-16-5p, hsa-miR-21-5p, and hsa-miR-150-5p in normal healthy serum, chronic lymphocytic leukemia Rai stage 1 (CLL-1), and stage 3 (CLL-3) sera, using a non-human cel-miR-39-3p as an internal standard. The sensor results were verified by conventional SYBR green-based quantitative reverse-transcription polymerase chain reaction (RT-qPCR) analysis.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".