Evaluating the modulation of peripheral immune profile in people living with HIV and (Neuro)cysticercosis
Bibliographic record
Abstract
BACKGROUND: The parasitic infection caused by Taenia solium represents a significant public health concern in developing countries. Larval invasion of body tissues leads to cysticercosis (CC), while central nervous system (CNS) involvement results in neurocysticercosis (NCC). Both conditions exhibit diverse clinical manifestations, and the potential impact of concomitant HIV infection especially prevalent in sub-Saharan Africa on peripheral and CNS immune responses remains poorly understood. This study aimed to identify the potential impact of HIV coinfection in CC and NCC patients. METHODOLOGY: A nested study within a cross-sectional analysis in two Tanzanian regions was performed and 234 participants (110 HIV+ and 124 HIV-) were tested for cysticercosis antibodies, antigens, CD4 counts and serum Th1 and Th2 cytokines via multiplex bead-based immunoassay. 127 cysticercosis seropositive individuals underwent cranial computed tomography (CCT) and clinical symptoms were assessed. Multiple regression analyses were performed to identify factors associated with cytokine modulation due to HIV in CC and NCC patients. RESULTS: Serologically, 18.8% tested positive for cysticercosis antibodies, with no significant difference HIV+ and HIV+. A significantly higher rate of cysticercosis antigen positivity was found in HIV+ individuals (43.6%) compared to HIV- (28.2%) (p = 0.016). CCT scans revealed that overall 10.3% had active brain cysts (NCC+). Our study found no significant changes in the overall cytokine profiles between HIV+ and HIV- participants coinfected CC and NCC, except for IL-5 which was elevated in HIV+ individuals with cysticercosis. Furthermore, HIV infection in general was associated with increased levels of pro-and some anti-inflammatory cytokines e.g. TNF-α, IL-8, and IFN-γ. However, based on the interaction analyses, no cytokine changes were observed due to HIV in CC or NCC patients. CONCLUSIONS: In conclusion, while HIV infection itself significantly modulates levels of key cytokines such as TNF-α, IL-8, and IFN-γ, it does not modulate any cytokine changes due to CC or NCC. This underscores the dominant influence of HIV on the immune system and highlights the importance of effective antiretroviral therapy in managing immune responses in individuals coinfected with HIV and CC/NCC.
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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".