Quantum-enhanced nanodiamond rapid test advances early SARS-CoV-2 antigen detection in clinical diagnostics
Bibliographic record
Abstract
Worldwide, the urgent need for more sensitive rapid lateral flow tests (LFTs) for early disease diagnosis is driving advances in quantum technologies. Spin-enhanced nanodiamond LFTs offer the potential for a sensitivity step change, yet to date only model systems have been studied with few clinical samples. Herein, we report the largest spin-enhanced LFT clinical evaluation, focusing on SARS-CoV-2 antigen as an exemplar. The limit of detection for recombinant antigens was 0.67 pg/ml, and inactivated whole virus wild-type and Omicron variants were 13 TCID 50 /mL and 8.8 TCID 50 /mL, respectively. Our blinded clinical study with 103 patient upper respiratory tract swab samples showed 95.1% sensitivity (Ct ≤ 30) and 100% specificity benchmarked to RT-qPCR, with no cross-reactivity to influenza A, RSV, and Rhinovirus. Analysis of trial data indicates spin-enhanced LFTs could diagnose SARS-CoV-2 on average 2.0 days earlier than conventional gold nanoparticle LFTs with identical antibodies, and just 0.6 days after RT-qPCR. Quantum-enhanced LFT sensitivity would have detected ~69,400 more patients in a single day at the peak of Omicron, reducing the transmission risk and protecting populations. Our findings mark an important milestone in the emerging field of quantum-enhanced diagnostics, with the potential for significant health-economic benefits to patients, populations, health systems and society.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".