Impact of Select Immunologic and Virologic Biomarkers on CD4 Cell Count Decrease in Patients with Chronic HIV‐1 Subtype C Infection: Results from Sinikithemba Cohort, Durban, South Africa
Bibliographic record
Abstract
BACKGROUND: The extent to which immunologic and clinical biomarkers influence human immunodeficiency virus type 1 (HIV-1) infection outcomes remains incompletely characterized, particularly for non-B subtypes. On the basis of data supporting in vitro HIV-1 protein-specific CD8 T lymphocyte responses as correlates of immune control in cross-sectional studies, we assessed the relationship of these responses, along with established HIV-1 biomarkers, with rates of CD4 cell count decrease in individuals infected with HIV-1 subtype C. METHODS: Bivariate and multivariate mixed-effects models were used to assess the relationship of baseline CD4 cell count, plasma viral load, human leukocyte antigen (HLA) class I alleles, and HIV-1 protein-specific CD8 T cell responses with the rate of CD4 cell count decrease in a longitudinal population-based cohort of 300 therapy-naive, chronically infected adults with baseline CD4 cell counts >200 cells/mm(3) and plasma viral loads >500 copies/mL over a median of 25 months of follow-up. RESULTS: In bivariate analyses, baseline CD4 cell count, plasma viral load, and possession of a protective HLA allele correlated significantly with the rate of CD4 cell count decrease. No relationship was observed between HIV-1 protein-specific CD8 T cell responses and CD4 cell count decrease. Results from multivariate models incorporating baseline CD4 cell counts (201-350 vs >350 cells/mm(3)), plasma viral load (< or =100,000 vs >100,000 copies/mL), and HLA (protective vs not protective) yielded the ability to discriminate CD4 cell count decreases over a 10-fold range. The fastest decrease was observed among individuals with CD4 cell counts >350 cells/mm(3) and plasma viral loads >100,000 copies/mL with no protective HLA alleles (-59 cells/mm(3) per year), whereas the slowest decrease was observed among individuals with CD4 cell counts 201-350 cells/mm(3), plasma viral loads < or =100,000 copies/mL, and a protective HLA allele (-6 cells/mm(3) per year). CONCLUSIONS: The combination of plasma viral load and HLA class I type, but not in vitro HIV-1 protein-specific CD8 T cell responses, differentiates rates of CD4 cell count decrease in patients with chronic subtype-C infection better than either marker alone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".