Humoral Immune Response to Influenza A(H1N1)pdm2009 in Patients with Natural Infection and in Vaccine Recipients in the 2009 Pandemic
Bibliographic record
Abstract
The 2009 pandemic H1N1 mainly affected adolescents and children, and most of the elderly in Japan escaped clinical illness. To clarify the role of humoral immunity in the infection, the time kinetics of hemagglutination inhibition (HI), neutralization (NT), and IgG subclass antibody response directed against influenza A(H1N1)pdm2009 were analyzed in three consecutive specimens obtained from 51 young adults and children (group 1) who contracted pandemic influenza and from 74 pediatric clinic employees (group 2) inoculated with pandemic monovalent vaccine. In group 1 patients, 6 and 30 patients had lower HI and NT antibody in the acute phase respectively. Thereafter, HI and NT antibody titers increased fourfold or more in 50 patients with peak response in the third specimens obtained four weeks after the onset. IgG1 in 45 patients, IgG3 in 18 patients, and IgG4 in 29 patients showed elevated responses. Forty (54%) and 70 (95%) subjects in group 2 had positive HI and NT antibodies in the prevaccination samples, with increased antibody responses in the follow-up peaking in the second specimens. Forty of those vaccinated had increased IgG1 responses peaking in the third specimens, whereas elevated IgG3 was observed in 22 recipients with the highest level in the second samples. IgG4 did not show any increase in subjects in group 2. A few participants showed an IgG2 response in both groups. An immunologically naive population contracted influenza with apparent clinical symptoms. However, already primed subjects through subclinical infection elicited the unique pattern of IgG subclass responses by vaccination, which differed from those of naive populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".