Evaluation of Influenza‐Specific Humoral Response by Microbead Array Analysis
Bibliographic record
Abstract
RATIONALE: Quantitation and determination of antigen specificity of systemic and mucosal immune responses to influenza vaccination is beneficial for future vaccine development. Previous methods to acquire this information were costly, time consuming and sample exhaustive. The benefits of suspension microbead array (MBA) analysis are numerous. The multiplex capabilities of the system conserve time, money and sample, while generating statistically powerful data. OBJECTIVE: To demonstrate the use of the assay by comparing the humoral influenza-specific responses of two cohorts from two countries that differed in circulating influenza strains and rates of influenza vaccination. METHODS: Influenza hemagglutinin (HA) from different strains were coated on microbeads and incubated with serum samples to capture immunoglobulin (Ig) A(1) and IgG(1) host antibodies. RESULTS: Statistically significant differences in IgA(1) and IgG(1) exist between the serum samples from Winnipeg (Manitoba) donors and those from Kenyan (Africa) donors. Data were compared using Mann-Whitney nonparametric tests. The Winnipeg donors had higher mean fluorescence intensity values, with significant P values for anti-HA IgA(1) to A/Wyoming/3/2003 (P=0.044), A/Vietnam/1203/2004 (P=0.0179), A/New Caledonia/20/99 (P<0.0001) and B/Tokyo/53/99 (P=0.0002). No differences were seen between the groups in their response to B/Jilin/20/2003. The Winnipeg donors had higher mean fluorescence intensity values, with significant P values for anti-HA IgG(1) to A/Wyoming/3/2003 (P=0.0135), B/Tokyo/53/99 (P=0.006) and B/Jilin20/2003 (P=0.026). CONCLUSION: Influenza-specific IgA(1) and IgG(1) antibodies were successfully detected using MBA technology. A significant difference in antibody response was observed between Winnipeg and Kenyan donor serums. MBA analysis is a relatively quick and cost-effective method for serum antibody analysis. The potential to simultaneously assay small sample volumes for a multitude of antigens makes this method invaluable for future vaccine response monitoring.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".