Antibody-independent surface plasmon resonance assays for influenza vaccine quality control
Bibliographic record
Abstract
Surface plasmon resonance (SPR)-based biosensors have emerged as a powerful platform for bioprocess monitoring due to their ability to detect biointeractions in real time, without the need for labeling. Paramount for the development of a robust detection platform is the immobilization of a ligand with high specificity and affinity for the in-solution species of interest. Following the 2009 H1N1 pandemic, much effort has been made toward the development of quality control platforms for influenza A vaccine productions, many of which have employed SPR for detection. Due to the rapid antigenic drift of influenza's principal surface protein, hemagglutinin, antibodies used for immunoassays need to be produced seasonally. The production of these antibodies represents a 6-8-week delay in immunoassay and, thus, vaccine availability. This review focuses on SPR-based assays that do not rely on anti-HA antibodies for the detection, characterization, and quantification of influenza A in bioproductions and biological samples. KEY POINTS: • The single radial immunodiffusion assay (SRID) has been the gold standard for the quantification of influenza vaccines since 1979. Due to antigenic drift of influenza's hemagglutinin protein, new antibody reagents for the SRID assay must be produced each year, requiring 6-8 weeks. The resulting delay in immunoassay availability is a major bottleneck in the influenza vaccine pipeline. This review highlights ligand options for the detection and quantification of influenza viruses using surface plasmon resonance biosensors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".