Impact of extended-release niacin on immune activation in HIV-infected immunological non-responders on effective antiretroviral therapy
Bibliographic record
Abstract
BACKGROUND: Tryptophan (Trp) catabolism into immunosuppressive kynurenine (Kyn) is involved in immune dysregulation during HIV infection. Niacin (vitamin B3) could control the excess of tryptophan depletion and represents a potential strategy to improve immune functions and CD4 count recovery in immunological non-responder HIV-infected individuals on antiretroviral therapy (ART). METHODS: In the CTN PT006 phase 2 pilot randomized trial, 20 adults on ART with CD4 ≤ 350 cells/µl, despite an undetectable viral load (VL) for at least 3 months, received 2000 mg of extended-release (ER)-niacin orally once daily for 24 weeks. Side effects, VL, CD4/CD8 counts, lipid profile, T-cell activation and senescence, Tregs and Th17 cell frequencies, Kyn/Trp ratio, and levels of IL-6, IP-10, sST2, I-FABP, and LBP were assessed following ER-niacin treatment. RESULTS: Thirteen participants completed the study. Treatment was interrupted in 4 patients due to loss of follow-up or personal reasons and 3 patients were discontinued due to comorbidity risks. All participants maintained a VL < 40 copies/ml, while ER-niacin did not affect CD4 and CD8 cell counts. Plasma levels of triglycerides, total, and LDL cholesterol significantly decreased, following ER-niacin treatment. ER-niacin also diminished Kyn plasma levels and slightly decreased CD4 T-cell activation. However, no improvement in CD8 subsets, Kyn/Trp ratio, Th17/Treg balance, and plasma inflammatory markers was observed. CONCLUSIONS: Although ER-niacin combined with ART was well-tolerated among immune non-responders and decreased plasma lipids, it did not improve systemic inflammation, Kyn/Trp ratio, and CD4 cell recovery. Overall, ER-niacin was not effective to overcome chronic inflammation in PLWH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".