Carbon Nanostructure-Based DNA Sensor Used for Quickly Detecting Breast Cancer-Associated Genes
Bibliographic record
Abstract
Abstract The early diagnosis of breast cancer highly relies on the detection of mutant DNA at low concentrations. Förster resonance energy transfer (FRET) quenching may offer a solution to quickly detect a small amount of single-strand DNA (ssDNA) through the combination of nanomaterials with special luminescence and unique structures of DNA double helix structure. Here, carbon quantum dots (CDs) modified with Capture ssDNA act as the FRET donor which interact with the two-dimensional fluorescence quencher, i.e., graphene oxide nanosheets (GO), to detect breast cancer-associated Target ssDNA at a low concentration. CDs bioconjugated with the designed Capture ssDNA (named CDs-Capture ssDNA) have the maximum fluorescence intensity (Imax) at the emission (λem) = 510 nm. The fluorescence of CDs-Capture ssDNA is quenched, while they interact with GO due to the π–π* interaction between ssDNA and GO. In the presence of Target ssDNA, the Imax is restored because of the stronger interaction between Target ssDNA and CDs-Capture ssDNA through the hydrogen bond. The restored fluorescence intensity of CDs has a linear relationship with the concentration of Target ssDNA from 0.25 to 2.5 μM with a detection limit around 0.24 μM. The selectivity of the sensing system has been further evaluated by testing the 3-base mismatched and non-base matched in which efficient restoration of photoluminescence of the sensing system cannot be observed. This carbon nanostructure-based DNA sensing system offers a user-friendly and quick detection of single-strand DNA at lower concentration.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".