Assessment of chloroquine as a modulator of immune activation to improve <scp>CD4</scp> recovery in immune nonresponding <scp>HIV</scp>‐infected patients receiving antiretroviral therapy
Bibliographic record
Abstract
OBJECTIVES: Chloroquine (CQ), an anti-inflammatory drug, inhibits Toll-like receptor (TLR) signalling in plasmacytoid dendritic cells (pDCs) and may be beneficial for HIV-infected patients in whom immune activation persists despite effective antiretroviral therapy (ART). The effect of CQ on CD4 T-cell recovery and immune activation in immune nonresponding patients receiving successful ART was therefore studied. METHODS: Nineteen adults on ART with CD4 counts ≤ 350 cells/μL and undetectable viral load (VL) orally received CQ at 250 mg/day for 24 weeks. Side effects, CD4 and CD8 T-cell counts, VL, T-cell activation, pDC proportion and plasma inflammatory markers were assessed at baseline, at 24 weeks, and at 12 weeks after CQ discontinuation (clinicaltrial.org registration #NCT02004314). RESULTS: CQ was well tolerated and all patients maintained an undetectable VL. The absolute CD4 and CD8 T-cell counts and their percentages, the pDC proportion, T-cell activation, D-dimer and C-reactive protein (CRP) plasma levels and the kynurenine/tryptophan ratio did not change with CQ treatment. Among nine cytokines/chemokines measured, only levels of interferon (IFN)-α2 were significantly increased by CQ treatment. CONCLUSIONS: CQ was well tolerated in patients with low CD4 T-cell counts despite long-term effective ART; however, 24 weeks of CQ treatment did not improved CD4 T-cell recovery, lymphoid and myeloid immune activation or inflammatory markers.
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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".