NanoHDA: A nanoparticle-assisted isothermal amplification technique for genotyping assays
Bibliographic record
Abstract
Isothermal methods, such as helicase-dependent amplification (HDA), have an advantage over polymerase chain reaction for DNA amplification owing to their ease of operation. Here, we developed a new HDA method that is nanoparticle-assisted, termed nanoHDA. This method uses gold nanoparticles (AuNPs) to improve the sensitivity and specificity of the isothermal method. In HDA, the denaturation of DNA templates is mediated by helicases, but this method is limited by the low denaturation efficiency of helicases. In this report, AuNPs with preferential affinity for single-stranded DNA (ssDNA) were utilized to improve the denaturation efficiency of helicases. The same affinity property of nanoparticles can also enhance specificity by suppressing primer-dimer formation. This nanoHDA method was employed to genotype the KRAS gene in genomic DNA samples from colorectal cancer patients, as achieved by the hybridization of nanoHDA amplicons using the NanoBioArray chip.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".