Fluorescence Quenching of Graphene Oxide Integrating with the Site-Specific Cleavage of the Endonuclease for Sensitive and Selective MicroRNA Detection
Bibliographic record
Abstract
A considerable effort is currently focused on identifying microRNA (miRNA) biomarkers because they could serve for early disease diagnosis as well as for assessing the prognosis and monitoring the response to treatment. The efficient use of the biomarkers requires precise analysis of miRNAs. This work reports a rapid, sensitive, and selective miRNA assay by coupling the fluorescence quenching of graphene oxide (GO) with site-specific cleavage of an endonuclease. The method is developed by designing a single-stranded probe that carries both a binding region responsible for facilitating the interaction with GO, which induces fluorescence quenching of the 5'-terminus-labeled fluorophore (FAM, 6-carboxyfluorescein), and a sensing region for specifically recognizing the target and hybridizing with it to form a duplex. The duplex is released from the GO surface through cleavage of RsaI endonuclease, resulting in fluorescence recovery, which shows a trend in the target concentration. The assay can detect down to ∼3.0 fM miR-126 with a linear range of 4 orders of magnitude and has an ability to discriminate the target sequence from even single-base mismatched sequence or other miRNA sequences. Moreover, it can also be used for estimating the miR-126 expressions in cells. The advantage of this assay is that it operates via the detection of the recovered fluorescence signal, which is a combined result of the specific hybridization and the site-specific cleavage, and thus should be impervious to false signals arising due to the nonspecific adsorption of interferants. It could be a great potential tool for selective analysis of miRNAs in cells.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".