Longitudinal Assessment of Changes in HIV-Specific Effector Activity in HIV-Infected Patients Starting Highly Active Antiretroviral Therapy in Primary Infection
Bibliographic record
Abstract
Both the magnitude and breadth of HIV-specific immunity were evaluated longitudinally on samples collected from six subjects starting highly active antiretroviral therapy (HAART) preseroconversion (group 1), 11 recently infected subjects starting HAART postseroconversion (group 2), five subjects starting HAART in the second half of the first year of infection (group 3), and six persons starting treatment in the chronic phase of infection (group 4). HIV-specific immunity was measured by IFN-gamma ELISPOT, detecting the frequency of cells responding to a panel of HLA-restricted HIV-1 peptides. Intracellular cytokine staining was used to detect the frequency of HIV-1 Gag p55-specific CD4(+) and CD8(+) T cells in a subset of participants. The magnitude and breadth of HIV-specific responses persisted in all group 1 subjects and in 5 of 11 (45%) group 2 subjects. Both of these parameters declined in 6 of 11 (55%) group 2 and in all group 3 and 4 individuals. All persons who maintained detectable numbers of HIV-1 Gag p55-specific CD4(+) and CD8(+) T cells after starting HAART preserved the intensity and breadth of their HIV-specific effector response. Our results show that HIV-specific immunity can be preserved even if HAART is initiated beyond the acute phase of infection.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".