Site‐Selective Labeling of Chromium(III) as a Quencher on DNA for Molecular Beacons
Bibliographic record
Abstract
Abstract Molecular beacons typically use organic molecules or nanomaterials as quenchers. Many transition‐metal ions have excellent fluorescence quenching ability, and the aim of this study was to recruit them as small quenchers in DNA detection. Cr3+ has a slow ligand exchange rate, forming stable adducts with DNA. With its strong fluorescence quenching ability, the site‐specific labeling of Cr3+ on DNA to form a new type of molecular beacon was investigated. The kinetics of quenching by Cr3+ were measured for single‐ and double‐stranded DNA as a function of salt concentration, pH, and Cr3+ ion concentration. The goal was to achieve a selective reaction with the single‐stranded but not double‐stranded regions. The reaction mechanism was also probed by adding adenosine triphosphate, revealing two Cr3+‐binding modes: fast but unstable, and slow but stable. A partially complementary duplex was designed with a short polyguanine overhang, which, under optimal conditions, enabled selective labeling of the overhanging region with Cr3+. The resulting sequence was tested as a molecular beacon with a detection limit of 0.3 nm DNA and a saturated fluorescence enhancement of fivefold. With a 13‐nucleotide target DNA, the single mismatch discrimination of the beacon was 22‐fold. This study demonstrates the possibility of forming useful Cr3+ adducts with DNA. Such adducts are not only useful for developing biosensors but also for constructing new materials.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".