Human Immunodeficiency Virus (HIV)-Specific Gamma Interferon Secretion Directed against All Expressed HIV Genes: Relationship to Rate of CD4 Decline
Bibliographic record
Abstract
Immune responses to human immunodeficiency virus (HIV) are detected at all stages of infection and are believed to be responsible for controlling viremia. This study seeks to determine whether gamma interferon (IFN-gamma)-secreting HIV-specific T-cell responses influence disease progression as defined by the rate of CD4 decline. The study population consisted of 31 subjects naive to antiretroviral therapy. All were monitored clinically for a median of 24 months after the time they were tested for HIV-specific responses. The rate of CD4+-T-cell loss was calculated for all participants from monthly CD4 counts. Within this population, 17 subjects were classified as typical progressors, 6 subjects were classified as fast progressors, and 8 subjects were classified as slow progressors. Peripheral blood mononuclear cells were screened for HIV-specific IFN-gamma responses to all expressed HIV genes. Among the detected immune responses, 48% of the recognized peptides were encoded by Gag and 19% were encoded by Nef gene products. Neither the breadth nor the magnitude of HIV-specific responses correlated with the viral load or rate of CD4 decline. The breadth and magnitude of HIV-specific responses did not differ significantly among typical, fast, and slow progressors. These results support the conclusion that although diverse HIV-specific IFN-gamma-secreting responses are mounted during the asymptomatic phase, these responses do not seem to modulate disease progression rates.
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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 | 0.001 |
| 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".