Screening Antibodies Raised Against the Spike Glycoprotein of SARS-CoV-2 to Support the Development of Rapid Antigen Assays
Bibliographic record
Abstract
<p>The spike glycoprotein of SARS-CoV-2 is a highly conserved surface protein and as such may represent a good target for immunoassay detection. We screened a variety of antibodies that were reactive to the S glycoprotein in a highly sensitive liquid immunoassay format and also on paper-based or lateral flow assay (LFA) to assess their analytical performance. Our findings included significant variation in performance when using different sources of S antigen. We identified several antibody pairs that had an LOD of below 10 pg/mL in the liquid immunoassay format with the lowest being 3 pg/mL. The antibodies were highly specific to SARS-Cov-2 based on cross reactivity screening with other human CoVs. The LFA screening found some different optimal antibody pairs from the pool of candidate antibodies used but a several antibodies were observed to have high performance with either immunoassay format.</p><br>
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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".