Effects of Highly Active Antiretroviral Therapy and Immune Recovery on CD8+ T-Cell-Mediated Inhibition of HIV-1 Transcription
Bibliographic record
Abstract
SUMMARY: : To date, the relation between the CD8 antiviral factor (CAF) and clinical indicators of disease progression in HIV-1 infection (CD4 T-cell counts and viral load [VL]) is inconclusive. Particularly, the effect of antiretroviral therapy and immune recovery on CAF production remains unclear. Using a transient transfection assay and a reporter gene activated by the HIV-1 long terminal repeat (LTR), we analyzed CAF production in CD8 T cells of HIV-1-positive individuals divided into 3 groups: patients on protease inhibitor (PI)-based therapy, patients on nonnucleoside reverse transcriptase inhibitor (NNRTI)-based therapy, and patients receiving no therapy. We found that within the untreated group, CAF activity inversely correlated with VL and high CAF was associated with lower VLs over a period of 0.5 to 3 years. Furthermore, patients who were drug-naive demonstrated significantly higher CAF than untreated patients who had previously undergone antiretroviral therapy. CAF activity in treated patients was similar to CAF in drug-naive patients and higher than in off-treatment patients. There seemed to be a trend toward higher CAF in patients on NNRTI-based therapy compared with those on PI-based therapy. These results suggest that immune recovery after highly active antiretroviral therapy (HAART) contributes to the normalization of CAF levels in HIV-1-positive individuals. Furthermore, we have distinguished between CD8 T-cell-mediated suppression of HIV-1 replication and gene transcription.
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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".