Universal Amplification-Free RNA Detection by Integrating CRISPR-Cas10 with Aptameric Graphene Field-Effect Transistor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Amplification-free, highly sensitive, and specific nucleic acid detection is crucial for health monitoring and diagnosis. The type III CRISPR-Cas10 system, which provides viral immunity through CRISPR-associated protein effectors, enables a new amplification-free nucleic acid diagnostic tool. In this study, we develop a CRISPR-graphene field-effect transistors (GFETs) biosensor by combining the type III CRISPR-Cas10 system with GFETs for direct nucleic acid detection. This biosensor exploits the target RNA-activated continuous ssDNA cleavage activity of the dCsm3 CRISPR-Cas10 effector and the high charge density of a hairpin DNA reporter on the GFET channel to achieve label-free, amplification-free, highly sensitive, and specific RNA detection. The CRISPR-GFET biosensor exhibits excellent performance in detecting medium-length RNAs and miRNAs, with detection limits at the aM level and a broad linear range of 10 −15 to 10 −11 M for RNAs and 10 −15 to 10 −9 M for miRNAs. It shows high sensitivity in throat swabs and serum samples, distinguishing between healthy individuals (N = 5) and breast cancer patients (N = 6) without the need for extraction, purification, or amplification. This platform mitigates risks associated with nucleic acid amplification and cross-contamination, making it a versatile and scalable diagnostic tool for molecular diagnostics in human health.
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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 it