Homogeneous Extraction-Free Dual-ctDNA Detection via DNA Nanomaterial Fusion for Rapid Breast Cancer Diagnosis
Bibliographic record
Abstract
The rapid, sensitive, and multiplexed detection of circulating tumor DNA (ctDNA) is crucial for improving breast cancer diagnosis. Here, we present a homogeneous dual-signal sensing platform that integrates rolling circle amplification (RCA)-based DNA hydrogels and DNA@Cu 2+ nanospheres (NS) for the simultaneous detection of dual ctDNA markers. This design enables a fully enzyme-free detection process after initial material synthesis, operating isothermally within 40 min and achieving an attomolar-level detection limit. The mechanism relies on target-triggered hydrogel disruption that exposes G-quadruplex sequences to enhance ThT fluorescence, while the concomitant release of Cu 2+ quenches quantum dot emission, together enabling a self-referencing fluorescent output for improved accuracy. Clinical proof-of-concept validation was performed on a cohort of 58 breast cancer patients and 30 healthy controls, showing a sensitivity of 88.0% and a specificity of 93.3%, with high concordance to clinical and qPCR results. The platform effectively differentiated not only cancer patients from healthy individuals but also showed significant signal differences between early- and late-stage disease. With its minimal sample processing, low instrumental requirements, and reliable performance, this assay represents a promising concept for rapid, cost-effective prescreening and stratification in breast cancer management.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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 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".