Real time plasmonic qPCR: how fast is ultra-fast? 30 cycles in 54 seconds
Bibliographic record
Abstract
Polymerase Chain Reaction (PCR) is a critical tool for biological research investigators but recently it also has been making a significant impact in clinical, veterinary and agricultural applications. Plasmonic PCR, which employs the very efficient heat transfer of optically irradiated metallic nanoparticles, is a simple and powerful methodology to drive PCR reactions. The scalability of next generation plasmonic PCR technology will introduce various forms of PCR applications ranging from small footprint portable point of care diagnostic devices to large footprint central laboratory multiplexing devices. In a significant advance, we have introduced a real time plasmonic PCR and explored the ability of ultra-fast cycling compatible with both label-free and fluorescence-based monitoring of amplicon production. Furthermore, plasmonic PCR has been substantially optimized to now deliver a 30 cycle PCR in 54 seconds, with a detectable product. The advances described here will have an immediate impact on the further development of the use of plasmonic PCR playing a critical role in rapid point of care 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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".