Multiplex, Quantitative, Reverse Transcription PCR Detection of Influenza Viruses Using Droplet Microfluidic Technology
Bibliographic record
Abstract
Quantitative, reverse transcription, polymerase chain reaction (qRT-PCR) is facilitated by leveraging droplet microfluidic (DMF) system, which due to its precision dispensing and sample handling capabilities at microliter and lower volumes has emerged as a popular method for miniaturization of the PCR platform. This work substantially improves and extends the functional capabilities of our previously demonstrated single qRT-PCR micro-chip, which utilized a combination of electrostatic and electrowetting droplet actuation. In the reported work we illustrate a spatially multiplexed micro-device that is capable of conducting up to eight parallel, real-time PCR reactions per usage, with adjustable control on the PCR thermal cycling parameters (both process time and temperature set-points). This micro-device has been utilized to detect and quantify the presence of two clinically relevant respiratory viruses, Influenza A and Influenza B, in human samples (nasopharyngeal swabs, throat swabs). The device performed accurate detection and quantification of the two respiratory viruses, over several orders of RNA copy counts, in unknown (blind) panels of extracted patient samples with acceptably high PCR efficiency (>94%). The multi-stage qRT-PCR assays on eight panel patient samples were accomplished within 35–40 min, with a detection limit for the target Influenza virus RNAs estimated to be less than 10 RNA copies per reaction.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".