Sociodemographic and Clinical Correlates of Markers of Immune Activation, Exhaustion and Platelet Activation among HIV-Infected Patients Initiating Antiretroviral Therapy in Dar es Salaam, Tanzania
Bibliographic record
Abstract
Chronic inflammation and persistent immune activation (IA) during HIV infection are associated with non-AIDS complications. We investigated sociodemographic and clinical characteristics influencing IA and exhaustion (IE), and platelet activation (PA) in newly diagnosed people living with HIV (PLHIV) and identified modifiable factors for early interventions. We analysed baseline blood samples from 365 PLHIV participating in a trial investigating the effect of aspirin on IA, IE, and PA. We assessed levels of markers of monocyte activation (soluble CD14), platelet activation (soluble P-selectin), T-cell activation (CD4⁺ and CD8⁺ expressing CD69 and co-expressing CD38 and HLA-DR), and T-cell exhaustion (PD-1). The median (IQR) age of the participants was 37 (28, 45) years, with females comprising 64.7%. Advanced age significantly predicted IA and IE, but not PA. Markers of IA and IE, but not of PA, inversely correlated with CD4 counts, while directly with HIV viral load (HVL). We show that most Tanzanian PLHIV initiating antiretroviral therapy (ART) have low CD4 count, high HVL, with a considerable proportion aged above 50 years, characteristics associated with heightened IA and IE. Adjunctive therapy, when available, should target such population and at ART initiation to prevent morbidity and mortality associated with persistent IA and IE.
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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 | 0.001 | 0.001 |
| 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".