Fast Molecular Beacon Hybridization in Organic Solvents with Improved Target Specificity
Bibliographic record
Abstract
DNA hybridization is of tremendous importance in biology, bionanotechnology, and biophysics. Molecular beacons are engineered DNA hairpins with a fluorophore and a quencher labeled on each of the two ends. A target DNA can open the hairpin to give an increased fluorescence signal. To date, the majority of molecular beacon detections have been performed only in aqueous buffers. We describe herein DNA detection in nine different organic solvents, methanol, ethanol, isopropanol, acetonitrile, formamide, dimethylformamide (DMF), dimethyl sulfoxide (DMSO), ethylene glycol, and glycerol, varying each up to 75% (v/v). In comparison with detection in water, the detection in organic solvents showed several important features. First, the molecular beacon hybridizes to its target DNA in the presence of all nine solvents up to a certain percentage. Second, the rate of this hybridization was significantly faster in most organic solvents compared with water. For example, in 56% ethanol, the beacon showed a 70-fold rate enhancement. Third, the ability of the molecular beacon to discriminate single-base mismatch is still maintained. Lastly, the DNA melting temperature in the organic solvents showed a solvent concentration-dependent decrease. This study suggests that molecular beacons can be used for applications where organic solvents must be involved or organic solvents can be intentionally added to improve the molecular beacon performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".