Plasmonic and label-free real-time quantitative PCR for point-of-care diagnostics
Bibliographic record
Abstract
DNA amplification in 20 μL total PCR sample volume. In the second part, we report an ultrasensitive real-time amplicon detection strategy which is based on cycle-by-cycle monitoring of 260 nm absorption of the PCR sample. This was accomplished by irradiating the PCR sample using a UV LED and collecting the transmitted optical power with a photodetector. The UV absorption dependency on the nucleotides' structural degree of freedom gives rise to distinctive features in the shape of UV amplification curves for the determination of PCR results, thus circumventing the need for the complicated design of target-specific probes or intercalating fluorophores. This amplicon quantification method has a high detection sensitivity of one DNA copy. This is the first demonstration of a compact plasmonic thermocycler combined with a real-time fluorophore-free quantitative amplicon detection system. The small footprint of our PCR device stems from hardware miniaturization, while abundant sample volume facilitates highly sensitive detection and fluid handling required for in-field sample analysis, thereby making it an excellent candidate for POC molecular diagnostics.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".