CoVSense: Ultrasensitive Nucleocapsid Antigen Immunosensor for Rapid Clinical Detection of Wildtype and Variant SARS‐CoV‐2
Bibliographic record
Abstract
The widespread accessibility of commercial/clinically-viable electrochemical diagnostic systems for rapid quantification of viral proteins demands translational/preclinical investigations. Here, Covid-Sense (CoVSense) antigen testing platform; an all-in-one electrochemical nano-immunosensor for sample-to-result, self-validated, and accurate quantification of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N)-proteins in clinical examinations is developed. The platform's sensing strips benefit from a highly-sensitive, nanostructured surface, created through the incorporation of carboxyl-functionalized graphene nanosheets, and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) conductive polymers, enhancing the overall conductivity of the system. The nanoengineered surface chemistry allows for compatible direct assembly of bioreceptor molecules. CoVSense offers an inexpensive (<$2 kit) and fast/digital response (<10 min), measured using a customized hand-held reader (<$25), enabling data-driven outbreak management. The sensor shows 95% clinical sensitivity and 100% specificity (Ct<25), and overall sensitivity of 91% for combined symptomatic/asymptomatic cohort with wildtype SARS-CoV-2 or B.1.1.7 variant (N = 105, nasal/throat samples). The sensor correlates the N-protein levels to viral load, detecting high Ct values of ≈35, with no sample preparation steps, while outperforming the commercial rapid antigen tests. The current translational technology fills the gap in the workflow of rapid, point-of-care, and accurate diagnosis of COVID-19.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 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".