SARS coronavirus 2-reactive antibodies in bovine colostrum.
Bibliographic record
Abstract
Objective: To determine if bovine colostrum and sera have antibodies that react with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Animals: Dairy and beef cattle from North America and Europe, sampled before and after the SARS-CoV-2 pandemic. Procedures: Indirect ELISAs using whole bovine coronavirus (BCoV) and SARS-CoV-2; whole SARS-CoV-2 Spike 1, Spike 2, and nucleocapsid proteins; and SARS-CoV-2-specific nucleocapsid peptide as antigens. Virus neutralization assay for BCoV. Surrogate virus neutralization assay for SARS-CoV-2. Results: Antibodies reactive to BCoV were highly prevalent in samples collected from cattle before and after the SARS-CoV-2 pandemic. Antibodies reactive with SARS-CoV-2 were present in the same samples, and apparently increased in prevalence after the SARS-CoV-2 pandemic. These antibodies had variable reactivity with the spike and nucleocapsid proteins of SARS-CoV-2 but were apparently not specific for SARS-CoV-2. Conclusions: Bovine coronavirus continues to be endemic in cattle populations, as indicated by the high prevalence of antibodies to the virus in colostrum and serum samples. Also, the prevalent antibodies to SARS-CoV-2 in bovine samples, before and after the pandemic, are likely the result of responses to epitopes on the spike and nucleocapsid proteins that are shared between the 2 betacoronaviruses. Cross-reactive antibodies in bovine colostrum could be examined for prophylactic or therapeutic effects on SARS-CoV-2 infections in humans.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".