Improving immunoassay detection accuracy of anti-SARS-CoV-2 antibodies through dual modality validation
Bibliographic record
Abstract
A novel test strategy is proposed with dual-modality detection techniques for COVID-19 antibody detection. The full-length S protein of SARS-CoV-2 was chemically immobilized on a glass surface to capture anti-SARS-CoV-2 IgG in patient serum and was detected through either Electrochemical Impedance Spectroscopy (EIS) or fluorescence imaging with labeled secondary antibodies. Gold nanoparticles conjugated with protein G were used as the probe and the bound GNP-G was detected through EIS measurements. Anti-human-IgG conjugated with the fluorescent tag Alexa Fluor 488 was used as the probe for fluorescence imaging. Clinical SARS-CoV-2 IgG positive serum and negative controls were used to validate both modalities. For fluorescence-based detection, a high sensitivity was noticed with a quantification range of 0.01-0.1 A.U.C. and a LOD of 0.004 A.U.C. This study demonstrates the possibility of utilizing different measurement techniques in conjunction for improved COVID-19 serology testing.
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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".