Combinatorial Probes for High‐Throughput Electrochemical Analysis of Circulating Nucleic Acids in Clinical Samples
Bibliographic record
Abstract
The analysis of circulating tumour nucleic acids (ctNAs) provides a minimally invasive way to assess the mutational spectrum of a tumour. However, effective and practical methods for analyzing this emerging class of markers are lacking. Analysis of ctNAs using a sensor-based approach has notable challenges, as it is vital to differentiate nucleic acids from normal cells from mutation-bearing sequences emerging from tumours. Moreover, many genes related to cancer have dozens of different mutations. Herein, we report an electrochemical approach that directly detects genes with mutations in patient serum by using combinatorial probes (CPs). The CPs enable detection of all of the mutant alleles derived from the same part of the gene. As a proof of concept, we analyze mutations of the EGFR gene, which has more than 40 clinically relevant alterations that include deletions, insertions, and point mutations. Our CP-based approach accurately detects mutant sequences directly in patient serum.
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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".