Tuberculosis distorts the inhibitory impact of interleukin-10 in HIV infection
Bibliographic record
Abstract
OBJECTIVES: This study aimed to assess how Mycobacterium tuberculosis (MTB) coinfection alters the impact of interleukin-10 in chronic HIV infection. DESIGN: We assessed plasma cytokine levels (interleukin-10, interferon-γ, tumor necrosis factor-α, interleukin-2, interleukin-6 and interleukin-13) in 82 individuals presenting with HIV monoinfection, HIV-LTBI (latent MTB infection) coinfection or HIV-TB (active tuberculosis) coinfection. We also assessed the influence of MTB on the functional impact of interleukin-10 receptor alpha (interleukin-10Rα) blockade on HIV and MTB-specific CD4(+) T cells. METHODS: Plasma cytokine levels were measured by high sensitivity Luminex. We used an ex-vivo interleukin-10Rα blockade assay to assess if functional enhancement of HIV and MTB-specific CD4(+) T cells was possible following a 48-h stimulation with HIV gag or pooled ESAT-6 (6 kDa early secretory antigenic target) and CFP-10 (10-kDa culture filtrate protein) peptides. Cell supernatant was collected 48 h after stimulation and the cytokine profile was measured by Luminex. RESULTS: Plasma interleukin-10 levels were elevated in HIV-TB as compared with HIV monoinfection (P < 0.05) and HIV-LTBI (P < 0.05). Plasma interleukin-10 levels correlated to HIV viral load in HIV monoinfection (P = 0.016) and HIV-LTBI (P = 0.042), but not HIV-TB. Ex-vivo blockade of interleukin-10Rα significantly enhanced MTB and HIV-specific CD4(+) T-cell function in HIV-LTBI individuals but not in HIV-TB individuals. CONCLUSION: Tuberculosis disrupts the correlation between interleukin-10 and markers of HIV disease progression. In addition, HIV-TB is associated with a more inflammatory cytokine milieu compared with HIV monoinfection. Interestingly, interleukin-10Rα blockade can enhance both HIV and MTB-specific T-cell function in HIV-LTBI, but not in HIV-TB coinfection.
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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 | 0.000 |
| 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 | 0.001 | 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".