A Versatile Biomimic Nanotemplating Fluidic Assay for Multiplex Quantitative Monitoring of Viral Respiratory Infections and Immune Responses in Saliva and Blood
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The last pandemic exposed critical gaps in monitoring and mitigating the spread of viral respiratory infections at the point-of-need. A cost-effective multiplexed fluidic device (NFluidEX), as a home-test kit analogous to a glucometer, that uses saliva and blood for parallel quantitative detection of viral infection and body's immune response in an automated manner within 11 min is proposed. The technology integrates a versatile biomimetic receptor based on molecularly imprinted polymers in a core-shell structure with nano gold electrodes, a multiplexed fluidic-impedimetric readout, built-in saliva collection/preparation, and smartphone-enabled data acquisition and interpretation. NFluidEX is validated with Influenza A H1N1 and SARS-CoV-2 (original strain and variants of concern), and achieves low detection limit in saliva and blood for the viral proteins and the anti-receptor binding domain (RBD) Immunoglobulin G (IgG) and Immunoglobulin M (IgM), respectively. It is demonstrated that nanoprotrusions of gold electrodes are essential for the fine templating of antibodies and spike proteins during molecular imprinting, and differentiation of IgG and IgM in whole blood. In the clinical setting, NFluidEX achieves 100% sensitivity and 100% specificity by testing 44 COVID-positive and 25 COVID-negative saliva and blood samples on par with the real-time quantitative polymerase chain reaction (p < 0.001, 95% confidence) and the enzyme-linked immunosorbent assay.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it