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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".