Universal type/subtype-specific antibodies for quantitative analyses of neuraminidase in trivalent influenza vaccines
Bibliographic record
Abstract
Both influenza viral hemagglutinin and neuraminidase can induce protective immune responses in humans. Although the viral hemagglutinin antigens have been quantified in influenza vaccines, the amounts of neuraminidase remain undetermined. Using comprehensive bioinformatics analyses of all neuraminidase sequences, we identified highly conserved and subtype-specific peptide epitopes within each of N1, N2 and type B neuraminidase groups. Mono-specific antibodies generated against these peptides bound to their respective subtype/type only while demonstrating remarkable specificity against the viral neuraminidase sequences without any cross-reactivity with allantoic and cellular proteins. Moreover, the subtype/type-specific antibodies were found not to interfere with one another when a mixture of vaccine samples was analysed. Importantly, immunoassay based on these antibodies can quantitatively determine neuraminidase in commercial trivalent vaccine samples. Analyses of vaccines from eight manufacturers using the same vaccine seeds revealed significant differences in neuraminidase levels. Specifically, while the ratio between neuraminidase and hemagglutinin in some products are found to be close 1/5, other products have a ratio of approximately 1/100, a level which is far below the theoretical ratio between neuraminidase and hemagglutinin in a virus. The antibody-based assays reported here could be of great value for better quality control of both monovalent and trivalent vaccines.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".