Detection of SARS-CoV-2 Receptor-Binding Domain Antibody using a HiBiT-Based Bioreporter
Bibliographic record
Abstract
The emergence of the COVID-19 pandemic has increased the need for better serological detection methods to determine the epidemiologic impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The increasing number of SARS-CoV-2 infections raises the need for better antibody detection assays. Current antibody detection methods compromise sensitivity for speed or are sensitive but time-consuming. A large proportion of SARS-CoV-2-neutralizing antibodies target the receptor-binding domain (RBD), one of the primary immunogenic compartments of SARS-CoV-2. We have recently designed and developed a highly sensitive, bioluminescent-tagged RBD (NanoLuc HiBiT-RBD) to detect SARS-CoV-2 antibodies. The following text describes the procedure to produce the HiBiT-RBD complex and a fast assay to evaluate the presence of RBD-targeting antibodies using this tool. Due to the durability of the HiBiT-RBD protein product over a wide range of temperatures and the shorter experimental procedure that can be completed within 1 h, the protocol can be considered as a more efficient alternative to detect SARS-CoV-2 antibodies in patient serum samples.
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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".