DNA hybridization detection with 100 zM sensitivity using piezoelectric plate sensors with an improved noise-reduction algorithm
Bibliographic record
Abstract
We have examined real-time, in situ hybridization detection of target DNA (tDNA) in a buffer solution and in urine using 8 μm-thick lead magnesium niobate-lead titanate (PMN-PT) piezoelectric plate sensors (PEPSs) about 1.1-1.2 mm long and 0.45 mm wide with improved 3-mercaptopropyltrimethoxysilane (MPS) insulation and a new multiple-parabola (>50) resonance peak position fitting algorithm. With probe DNA (pDNA) immobilized on the PEPS surface and by monitoring the first width extension mode (WEM) resonance frequency shift we detected tDNA in real time at concentration as low as 1 × 10(-19) M in urine (100 zM) with a signal to noise ratio (SNR) of 13 without DNA isolation and amplification at room temperature in 30 min. The present multiple-parabola fitting algorithm increased the detection of SNR by about 10 times compared to those obtained using the raw data and by about 5 times compared to those obtained using single parabola fitting. The detection was validated by in situ follow-up detection and subsequent visualization of fluorescent reporter microspheres (FRMs) coated with reporter DNA complementary to the tDNA but different from the probe pDNA.
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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 | 0.000 |
| 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 | 0.000 |
| 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".