High‐Performance Nucleic Acid Sensors for Liquid Biopsy Applications
Bibliographic record
Abstract
Abstract Circulating tumour nucleic acids (ctNAs) are released from tumours cells and can be detected in blood samples, providing a way to track tumors without requiring a tissue sample. This “liquid biopsy” approach has the potential to replace invasive, painful, and costly tissue biopsies in cancer diagnosis and management. However, a very sensitive and specific approach is required to detect relatively low amounts of mutant sequences linked to cancer because they are masked by the high levels of wild‐type sequences. This review discusses high‐performance nucleic acid biosensors for ctNA analysis in patient samples. We compare sequencing‐ and amplification‐based methods to next‐generation sensors for ctDNA and ctRNA (including microRNA) profiling, such as electrochemical methods, surface plasmon resonance, Raman spectroscopy, and microfluidics and dielectrophoresis‐based assays. We present an overview of the analytical sensitivity and accuracy of these methods as well as the biological and technical challenges they present.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".