Cytokine profiles and CD4 counts in HIV-positive individuals with cysticercosis: implications for sex-specific immune responses in co-endemic regions of Tanzania
Bibliographic record
Abstract
Introduction: T-cell counts, and cysticercosis infection status in HIV-positive individuals in Tanzania's southern highlands. Methods: We conducted a cross-sectional study involving 110 HIV-positive individuals. Cysticercosis was diagnosed using antibody and antigen tests, with neurocysticercosis confirmed by CT imaging. CD4 counts and serum cytokine levels (pro- and anti-inflammatory) were analyzed using multivariate regression and MANOVA, including sex-stratified analyses. Results: Among participants, 20.9% tested positive for cysticercosis antibodies, 43.6% for antigens and 20.2% presented with brain cysts, with 10.6% showing active neurocysticercosis. Cysticercosis-positive individuals showed positive correlations between CD4 counts and pro-inflammatory cytokines (e.g., TNF-α, IL-1β), contrasting with negative correlations in cysticercosis-negative individuals. Sex-stratified analysis showed stronger regulatory cytokine responses in males compared to females, particularly involving higher levels of IL-10 and IL-4 indicating sex-specific immune modulation in co-infected individuals. However, overall cytokine profiles were not significantly influenced by CD4 categories or cysticercosis status. Conclusion: These results contribute to our understanding of immunological interactions in HIV-cysticercosis co-infection and underscore the need for further research with larger sample sizes to elucidate the clinical implications of these findings. Such studies could inform the development of more effective, sex-personalized treatment strategies for HIV patients in cysticercosis-endemic regions.
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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".