A Novel Synthetic Receptor-Based Immunoassay for Influenza Vaccine Quantification
Bibliographic record
Abstract
Vaccination is the most effective prophylactic method for preventing influenza. Quantification of influenza vaccine antigens is critically important before the vaccine is used for human immunization. Currently the vaccine antigen quantification relies on hemagglutinin content quantification, the key antigenic component, by single radial immunodiffusion (SRID) assay. Due to the inherent disadvantages associated with the traditional SRID; i.e. low sensitivity, low throughput and need for annual reagents, several approaches have been proposed and investigated as alternatives. Yet, most alternative methods cannot distinguish native hemagglutinin from denatured form, making them less relevant to antigenic analyses. Here, we developed a quantitative immunoassay based on the sialic acid binding property of influenza vaccine antigens. Specifically, we chemically synthesized human and avian influenza virus receptors analogues, N-acetylneuraminic acid-2,6-lactose and N-acetylneuraminic acid-2,3-lactose derivatives with an azidopropyl aglycon, using α-2,6- and α-2,3-sialyltransferases, respectively. The azido group of the two sialyllactose-derivatives was reduced and conjugated to mouse serum albumin through a squarate linkage. We showed that the synthetic α-2,6- and α-2,3-receptors selectively bound to human and avian-derived hemagglutinins, respectively, forming the basis of a new, and robust assay for hemagglutinin quantification. Hemagglutinin treated at high temperature or low pH was measured differentially to untreated samples suggesting native conformation is dependent for optimal binding. Importantly, this receptor-based immunoassay showed excellent specificity and reproducibility, high precision, less turnaround time and significantly higher sensitivity and throughput compared with SRID in analyzing multiple influenza 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 | 0.000 | 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 | 0.001 | 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".