Architecture based on the integration of intermolecular G-quadruplex structure with sticky-end pairing and colorimetric detection of DNA hybridization
Bibliographic record
Abstract
An interesting discovery is reported in that G-rich hairpin-based recognition probes can self-assemble into a nano-architecture based on the integration of an intermolecular G-quadruplex structure with the sticky-end pairing effect in the presence of target DNAs. Moreover, GNPs modified with partly complementary DNAs can intensively aggregate by hybridization-based intercalation between intermolecular G-quadruplexes, indicating an inspiring assembly mechanism and a powerful colorimetric DNA detection. The proposed intermolecular G-quadruplex-integrated sticky-end pairing assembly (called GISA)-based colorimetric system allows a specific and quantitative assay of p53 DNA with a linear range of more than two orders of magnitude and a detection limit of 0.2 nM, suggesting a considerably improved analytical performance. And more to the point, the discrimination of single-base mismatched target DNAs can be easily conducted via visual observation. The successful development of the present colorimetric system, especially the GISA-based aggregation mechanism of GNPs is different from traditional approaches, and offers a critical insight into the dependence of the GNP aggregation on the structural properties of oligonucleotides, opening a good way to design colorimetric sensing probes and DNA nanostructure.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".