Automated sample-to-answer centrifugal microfluidic system for rapid molecular diagnostics of SARS-CoV-2
Bibliographic record
Abstract
, <1 h). In addition, RT-LAMP is compatible with colorimetric detection, facilitating visualization and read-out. However, even with these benefits, RT-LAMP is not yet clinically deployed at its full capacity. Lack of automation and integration of sample preparation, such as RNA extraction, limits the sensitivity and specificity of the method. Furthermore, the need for cold storage of reagents complicates its use at the point of need. The developments presented in this work address these limitations: We describe a fully automated SARS-CoV-2 detection method using RT-LAMP, which also includes up-front lysis and extraction of viral RNA, performed on a centrifugal platform with active pneumatic pumping, a disposable, all-polymer-based microfluidic cartridge and lyophilized reagents. We demonstrate that the limit of detection of the RT-LAMP assay itself is 0.2 copies per μL using N and E genes as target sequences. When combined with integrated RNA extraction, the assay sensitivity is 0.5 copies per μL, which is highly competitive to RT-qPCR. We tested the automated assay using 12 clinical swab specimens from patients and were able to distinguish positive and negative samples for SARS-CoV-2 within 60 min, thereby obtaining 100% agreement with RT-qPCR results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".