Sample-to-answer centrifugal microfluidic droplet PCR platform for quantitation of viral load
Bibliographic record
Abstract
Droplet digital polymerase chain reaction (ddPCR) stands out as a highly sensitive diagnostic technique that is gaining traction in infectious disease diagnostics due to its ability to quantitate very low numbers of viral gene copies. By partitioning the sample into thousands of droplets, ddPCR enables precise and absolute quantification without relying on a standard curve. However, current ddPCR systems often exhibit relatively low levels of integration, and the analytical process remains dependent on elaborate workflows for up-front sample preparation. Here, we introduce a fully-integrated system seamlessly combining viral lysis, RNA extraction, emulsification, reverse transcription (RT) ddPCR, and fluorescence readout in a sample-to-answer format. The system comprises a disposable microfluidic cartridge housing buffers and reagents required for the assay, and a centrifugal platform that allows for pneumatic actuation of liquids during rotation, enabling automation of the workflow. Highly monodisperse droplets (∼50 μm in diameter) are produced using centrifugal step emulsification and automatically transferred to an integrated heating module for target amplification. The platform is equipped with a miniature fluorescence imaging system enabling on-chip read-out of droplets after RT-ddPCR. We demonstrate sample-to-answer detection of SARS-CoV-2 N and E genes, along with RNase P endogenous reference, using hydrolysis probes and multiplexed amplification within single droplets for concentrations as low as 0.1 copy per μL. We also tested 14 nasopharyngeal swab specimens from patients and were able to distinguish positive and negative SARS-CoV-2 samples with 100% accuracy, surpassing results obtained by conventional real-time amplification.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".