Expanding detection windows for discriminating single nucleotide variants using rationally designed DNA equalizer probes
Bibliographic record
Abstract
Combining experimental and simulation strategies to facilitate the design and operation of nucleic acid hybridization probes are highly important to both fundamental DNA nanotechnology and diverse biological/biomedical applications. Herein, we introduce a DNA equalizer gate (DEG) approach, a class of simulation-guided nucleic acid hybridization probes that drastically expand detection windows for discriminating single nucleotide variants in double-stranded DNA (dsDNA) via the user-definable transformation of the quantitative relationship between the detection signal and target concentrations. A thermodynamic-driven theoretical model was also developed, which quantitatively simulates and predicts the performance of DEG. The effectiveness of DEG for expanding detection windows and improving sequence selectivity was demonstrated both in silico and experimentally. As DEG acts directly on dsDNA, it is readily adaptable to nucleic acid amplification techniques, such as polymerase chain reaction (PCR). The practical usefulness of DEG was demonstrated through the simultaneous detection of infections and the screening of drug-resistance in clinical parasitic worm samples collected from rural areas of Honduras.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 teacher head, 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".