The Duration of Exposure to HIV Modulates the Breadth and the Magnitude of HIV-Specific Memory CD4+ T Cells
Bibliographic record
Abstract
The impact of exposure to Ag on the development and maintenance of human CD4(+) memory T cells in general and HIV infection in particular is partially understood. In this study, we measured HIV-specific CD4(+) T cell proliferative responses against HIV proteins and derived peptides one year after highly active antiretroviral therapy initiation in 39 HIV-infected patients who initiated therapy at different times following infection. We show that a brief exposure to HIV of <1 month does not allow the generation of significant detectable frequencies of HIV-specific CD4(+) memory T cells. Patients having prolonged cumulative exposure to high viral load due to therapy failures also demonstrated limited HIV-specific CD4(+) T cell responses. In contrast, patients exposed to significant levels of virus for periods ranging from 3 to 18 mo showed brisk and broad HIV-specific CD4(+) T cell responses 1 year following the onset of therapy intervention. We also demonstrate that the nadir CD4(+) T cell count before therapy initiation correlated positively with the breadth and magnitude of these responses. Our findings indicate that the loss of proliferative HIV-specific CD4(+) T cell responses is associated with the systemic progression of the disease and that a brief exposure to HIV does not allow the establishment of detectable frequencies of HIV-specific memory CD4(+) T cells.
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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".