Direct Electrocatalytic mRNA Detection Using PNA-Nanowire Sensors
Bibliographic record
Abstract
We report an electrochemical nucleic acids sensing system that exhibits high sensitivity and specificity when challenged with heterogeneous samples of RNA. The platform directly detects specific RNA sequences in cellular and clinical samples without any sample labeling or PCR amplification. The sensor features an electrode platform consisting of three-dimensional gold nanowires, and DNA or RNA hybridization is detected using an electrocatalytic reporter system. In this study, probes made of peptide nucleic acid (PNA) are used to detect a newly identified cancer biomarkera gene fusion recently associated with prostate cancer. The system is able to detect the fusion sequence with 100 fM sensitivity, and retains high sensitivity even in the presence of a large excess of non-complementary sequences. Moreover, the sensor is able to detect the fusion sequence in as little as 10 ng of mRNA isolated from cell lines or 100 ng total RNA from patient tissue samples. The PNA-nanowire nucleic acids sensor described is one of the first electrochemical sensors to directly detect specific mRNAs in unamplified patient samples.
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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 | 0.000 |
| 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".