Dynamics of Anti-influenza Mucosal IgA Over a Season in a Cohort of Individuals Living or Working in a Long-term Care Facility
Bibliographic record
Abstract
BACKGROUND: Serological surveys are used to ascertain influenza infection and immunity, but evidence for the utility of mucosal immunoglobulin A (IgA) as a correlate of infection or protection is limited. METHODS: We performed influenza-like illness (ILI) surveillance on 220 individuals living or working in a retirement community in Gainesville, Florida from January to May 2018, and took pre- and postseason nasal samples of 11 individuals with polymerase chain reaction (PCR)-confirmed influenza infection and 60 randomly selected controls. Mucosal IgA against 10 strains of influenza was measured from nasal samples. RESULTS: Overall, 28.2% and 11.3% of individuals experienced a 2-fold and 4-fold rise, respectively, in mucosal IgA to at least 1 influenza strain. Individuals with PCR-confirmed influenza A had significantly lower levels of preseason IgA to influenza A. Influenza-associated respiratory illness was associated with a higher rise in mucosal IgA to influenza strains of the same subtype, and H3N2-associated respiratory illness was associated with a higher rise in mucosal IgA to other influenza A strains. CONCLUSIONS: By comparing individuals with and without influenza illness, we demonstrated that mucosal IgA is a correlate of influenza infection. There was evidence for cross-reactivity in mucosal IgA across influenza A subtypes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".