Low-Cost, Open-Source Device for High-Performance Fluorescence Detection of Isothermal Nucleic Acid Amplification Reactions
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The ability to detect SARS-CoV-2 is critical to implementing evidence-based strategies to address the COVID-19 global pandemic. Expanding SARS-CoV-2 diagnostic ability beyond well-equipped laboratories widens the opportunity for surveillance and control efforts. However, such advances are predicated on the availability of rapid, scalable, accessible, yet high-performance diagnostic platforms. Methods to detect viral RNA using reverse transcription loop-mediated isothermal amplification (RT-LAMP) show promise as rapid and field-deployable tests; however, the per-unit costs of the required diagnostic hardware can be a barrier for scaled deployment. Here, we describe a diagnostic hardware configuration for LAMP technology, named the FABL-8, that can be built for approximately US$380 per machine and provide results in under 30 min. Benchmarking showed that FABL-8 has a similar performance to a high-end commercial instrument for detecting fluorescence-based LAMP reactions. Performance testing of the instrument with RNA extracted from a SARS-CoV-2 virus dilution series revealed an analytical detection sensitivity of 50 virus copies per microliter—a detection threshold suitable to detect patient viral load in the first few days following symptom onset. In addition to the detection of SARS-CoV-2, we show that the system can be used to detect the presence of two bacterial pathogens, demonstrating the versatility of the platform for the detection of other pathogens. This cost-effective and scalable hardware alternative allows democratization of the instrumentation required for high-performance molecular diagnostics, such that it could be available to laboratories anywhere—supporting infectious diseases surveillance and research activities in resource-limited settings.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".