Immune response to influenza A(H1N1)v in HIV-infected patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it