QBox: An Automated Portable System for Multiplex Quantum Dot Barcode Diagnostics
Bibliographic record
Abstract
The COVID-19 pandemic accelerated the development of automated systems for detecting molecular targets for the point-of-care. However, these systems have limited multiplexing capabilities because of the need to alter their hardware to accommodate additional targets and probes. Quantum dot barcodes address this multiplexing obstacle, but their assays have multiple steps that rely on extensive training and laboratory equipment. Here, we built a portable cartridge-and-instrument system that automates the extraction, reverse transcription, amplification, and detection steps of a quantum dot barcode assay. This entire workflow can be completed in 40 minutes. We clinically validated the system with SARS-CoV-2 patient samples ( n = 50, 92% sensitivity, 100% specificity). We then demonstrated multiplexing with 4-barcode respiratory and 5-barcode bloodborne pathogen panels. Our portable system opens quantum dot barcodes for broad use in rapid multiplexed detection of infectious pathogens with the potential for detecting cancer and other genetic diseases.
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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".