High‐Performance Nucleic Acid Sensors for Liquid Biopsy Applications
Bibliographic record
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 | 0.001 |
| 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) | 0.004 | 0.005 |
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".