Interleukin 1-Beta (IL-1β) Production by Innate Cells Following TLR Stimulation Correlates With TB Recurrence in ART-Treated HIV-Infected Patients
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) remains a major cause of global morbidity and mortality, especially in the context of HIV coinfection because immunity is not completely restored following antiretroviral therapy (ART). The identification of immune correlates of risk for TB disease could help in the design of host-directed therapies and clinical management. This study aimed to identify innate immune correlates of TB recurrence in HIV+ ART-treated individuals with a history of previous successful TB treatment. METHODS: Twelve participants with a recurrent episode of TB (cases) were matched for age, sex, time on ART, pre-ART CD4 count with 12 participants who did not develop recurrent TB in 60 months of follow-up (controls). Cryopreserved peripheral blood mononuclear cells from time-points before TB recurrence were stimulated with ligands for Toll-like receptors (TLR) including TLR-2, TLR-4, and TLR-7/8. Multicolor flow cytometry and intracellular cytokine staining were used to detect IL-1β, TNF-α, IL-12, and IP10 responses from monocytes and myeloid dendritic cells (mDCs). RESULTS: Elevated production of IL-1β from monocytes following TLR-2, TLR-4, and TLR-7/8 stimulation was associated with reduced odds of TB recurrence. In contrast, production of IL-1β from both monocytes and mDCs following Bacillus Calmette-Guérin (BCG) stimulation was associated with increased odds of TB recurrence (risk of recurrence increased by 30% in monocytes and 42% in mDCs, respectively). CONCLUSION: Production of IL-1β by innate immune cells following TLR and BCG stimulations correlated with differential TB recurrence outcomes in ART-treated patients and highlights differences in host response to TB.
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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 | 0.000 | 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".