Rapid DNA Amplification: Recent Approaches to Accelerating Nucleic Acid Diagnostic Methods
Bibliographic record
Abstract
The covid19 pandemic has made evident the essential role that diagnostic tests based on the polymerase chain reaction (PCR) can play in infection control. Based on the selective amplification of nucleic acids, PCR is able to detect the presence of a very low concentration of specific DNA or RNA molecules (down to single molecules in some cases). Since its invention 40 years ago, PCR has become the standard diagnostic procedure for a wide variety of infections, and also finds application in many other fields such as agriculture, forensic science, forestry and environmental health. The PCR amplification process requires that the sample under test be thermocycled between the DNA annealing temperature (around 55C) and melting temperature (around 95C) 30-40 times. Conventional PCR thermocyclers make use of thermoelectric heaters and coolers to accomplish this, and as a result are often bulky and have a high power consumption. Typically they require at least 30 minutes (and up to one hour) to deliver a result. Recently there have been a number of innovations in methods to reduce the time to result, and also to decrease the bulk, cost and power requirements of PCR thermocyclers. Often the objective of these innovations is to transform PCR into a point-of-care (POC) diagnostic tool. This talk will describe some of these approaches, with a particular focus on thermocycling using laser heating of plasmonic nanoparticles or films, but also considering other aspects including microfluidics and biological factors. It will also highlight some of the significant challenges that remain in translating PCR to the POC arena.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".