HIV infection deregulates innate immunity to malaria despite combination antiretroviral therapy
Bibliographic record
Abstract
OBJECTIVE: Malaria and HIV-1 adversely interact, with HIV-positive individuals suffering higher parasite burdens and worse clinical outcomes. However, the mechanisms underlying these disease interactions are unclear. We hypothesized that HIV coinfection impairs the innate immune response to malaria, and that combination antiretroviral therapy (cART) may restore this response. Our aim was to examine the innate inflammatory response of natural killer (NK), natural killer T (NKT), and γδ T-cells isolated from the peripheral blood of HIV-infected therapy-naive donors to malaria parasites, and determine the effect of cART on these responses. METHODS: Freshly isolated peripheral blood mononuclear cells from 25 HIV-infected individuals pre-cART (month 0) and post-cART (months 3 and 6), and HIV-negative individuals at matched time-points, were cultured in the presence of Plasmodium falciparum parasitized erythrocytes. Supernatants and cells were collected to assess cytokine production and phenotypic changes. RESULTS: Compared to HIV-negative participants, NKT, NK, and γδ T-cell subsets from participants with chronic HIV infection showed marked differences, including decreased production of interferon γ (IFNγ) and tumor necrosis factor (TNF) in response to malaria parasites. IFNγ production was linked to interleukin-18 receptor (IL-18R) expression in all three cell types studied. Six months of cART provided partial cellular reconstitution but had no effect on IL-18R expression, or IFNγ and TNF production. CONCLUSION: These data suggest that HIV infection impairs the inflammatory response of innate effector cells to malaria, and that the response is not fully restored within 6 months of cART. This may contribute to higher parasite burdens and ineffective immune responses, and have implications for vaccination initiatives in coinfected individuals.
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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 | 0.000 | 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".