Development and characterization of monoclonal antibodies recognizing nucleocapsid protein of multiple SARS-CoV-2 variants
Bibliographic record
Abstract
Rapid antigen test (RAT) is widely used for SARS-CoV-2 infection diagnostics. However, test sensitivity has decreased recently due to the emergence of the Omicron variant and its sublineages. Here we developed a panel of SARS-CoV-2 nucleocapsid protein (NP) specific mouse monoclonal antibodies (mAbs) and assessed their sensitivity and specificity to important SARS-CoV-2 variants. We identified seven mAbs that exhibited strong reactivity to SARS-CoV-2 variants and recombinant NP (rNP) by Western immunoblot or ELISA. Their specificity to SARS-CoV-2 was confirmed by negative or low reactivity to rNPs from SARS-CoV-1, MERS, and common human coronaviruses (HCoV-HKU1, HCoV-CO43, HCoV-NL63, and HCoV-229E). These seven mAbs were further tested by immunoplaque assay against selected variants of concern (VOCs), including two Omicron sublineages, and five mAbs (F461G13, F461G7, F459G7, F457G3, and F461G6), showed strong reactions, warranting further suitability testing for the development of diagnostic assay.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 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".