A sensitive biosensing strategy for DNA detection based on graphene oxide and T7 exonuclease assisted target recycling amplification
Bibliographic record
Abstract
A fluorescence biosensing strategy based on graphene oxide (GO) was reported for simple, rapid, sensitive, and selective DNA detection by T7 exonuclease assisted target recycling amplification. Due to the super fluorescence quenching efficiency of GO, the fluorescein amiditelabeled signal probe was firstly adsorbed onto the surface of GO and the fluorescence was quenched. Owing to its excellent selectivity for double-stranded DNA, T7 exonuclease was chosen as a signal-amplifying biocatalyst to improve the detection sensitivity. In the presence of target DNA, the signal probe could bind with target DNA and form a DNA duplex structure to trigger the digestion of the signal probe by T7 exonuclease, leading to the recycling of target DNA and the increasing of fluorescence intensity. Upon the recycling use of target DNA, this method achieved a high sensitivity towards target DNA with a detection limit of 0.3 pmol/L, which was lower than previously reported for GO-based DNA biosensors. Moreover, it does not require complex modifications of the molecular beacon and time-consuming thermal cycling procedures. Thus, the simple strategy provides a universal biosensing platform for DNA detection and it could find wide applications in DNA damage analysis and diagnostics.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".