Immune response to influenza A(H1N1)v in HIV-infected patients
Bibliographic record
Abstract
INTRODUCTION: HIV infection is considered a risk factor for severe outcomes of influenza A(H1N1)v infection. However, data on immune response against influenza A(H1N1)v virus in HIV-infected patients are lacking. METHODOLOGY: Data from seven HIV-positive and 14 HIV-negative patients infected with A(H1N1)v and from 23 HIV-positive and six HIV-negative asymptomatic controls were analyzed to evaluate the clinical picture, A(H1N1)v viral shedding, and the immune response against the virus. RESULTS: Patients displayed mainly upper respiratory tract diseases (57.1%), while pneumonia was diagnosed only in HIV-negative patients (23.8% of subjects, of which 4.8% required intensive care unit admission). At day seven, 29% of HIV-infected patients were still positive for A(H1N1)v by RT-PCR on nasopharyngeal swabs. Interestingly, a persistence of CXCL10 secretion at high level and lower IL-6 levels was observed in HIV-positive subjects. The geometric mean haemagglutination inhibition titer (HI-GMT) and anti-influenza IgM levels were lower in HIV-positive individuals while anti-influenza IgG levels remained similar in the two groups. CONCLUSIONS: The immune impairment due to HIV infection could affect A(H1N1)v clearance and could lead to a lower antibody response and a persistent secretion of CXCL10 at high levels. However, the lower IL-6 secretion and treatment with highly active antiretroviral therapy (HAART) could result in a milder clinical picture.
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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 | 0.000 | 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) | 0.001 | 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".