Diagnostics of severe acute respiratory syndrome-associated coronavirus (SARS-CoV) nucleocapsid antigen using chicken immunoglobulin Y
Bibliographic record
Abstract
The goal of this study was to develop a quantitative detection system for severe acute respiratory syndrome-associated coronavirus (SARS-CoV), targeting the nucleocapsid protein (NP), to determine the presence and degree of infection in suspected individuals. Because the NP is the viral protein shed during infection and its template mRNA is the most abundant subgenomic RNA, it is a suitable candidate for developing antibodies for diagnostic applications. In this study, we have prepared full-length SARS-CoV NP expressed in Escherichia coli and purified. Full-length NP was used for the preparation of mouse monoclonal antibody and chicken polyclonal IgY antibodies for the development of heterosandwich ELISA for early diagnostics of SARS-suspected individuals. The sensitivity of the developed heterosandwich ELISA can detect the viral antigen at 18.5 pg/mL of recombinant NP. This study describes ultrasensitive ELISA using 19B6 monoclonal antibody as the capture antibody and IgY as the detecting antibody against the most abundant SARS-CoV NP antigens. One of the most important findings was the use of inexpensive polyclonal IgY antibody to increase the sensitivity of the detection system for SARS-CoV at the picogram level. Furthermore, the immunoassay of SARS-CoV NP antigen developed could be an effective and sensitive method of diagnosing SARS-suspected individuals during a future SARS-CoV outbreak.
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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) | 0.000 | 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 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".