Longitudinal changes in HIV-specific IFN-γ secretion in subjects who received Remune™ vaccination prior to treatment interruption
Bibliographic record
Abstract
BACKGROUND: Despite the benefits of highly active antiretroviral therapy (HAART) for suppressing viral replication in HIV infection, virus persists and rebounds during treatment interruption (TI). This study explored whether HAART intensification with Remune vaccination before TI can boost HIV-1-specific immunity, leading to improved control of viremia off HAART. METHODS: Ten chronically HIV-infected adults were enrolled in this proof of concept study. After a 6-month HAART intensification phase with didanosine, hydroxyurea, granulocyte-macrophage colony-stimulating factor, (GM-CSF), and a first dose of Remune (HIV-1 Immunogen), HAART was discontinued. Patients continued to receive Remune every 3 months until the end of study. HAART was restarted if viral load did not fall below 50,000 copies/ml of plasma within 3 months or if CD4+ counts decreased to <200 cells/mm3. HIV-specific immunity was monitored with the interferon-gamma (IFN-gamma) ELISPOT assay. RESULTS: All subjects experienced viral rebound during TIs. Although the magnitude and breadth of HIV-specific responses to HLA-restricted optimal peptide panels and Gag p55 peptide pools increased and viral load decreased by 0.44 log10 units from TI#1 to TI#2, no significant correlations between these parameters were observed. The patients spent 50.4% of their 36 months follow up off HAART. CONCLUSION: Stopping HAART in this vaccinated population induced immune responses that persisted after therapy was restarted. Induction of HIV-specific immunity beyond IFN-gamma secretion may be contributing to better control of viremia during subsequent TIs allowing for long periods off HAART.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".