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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".