The Effect of Latent<i>Toxoplasma gondii</i>Infection on the Immune Response in HIV-Infected Patients
Bibliographic record
Abstract
A relationship between latent toxoplasmosis and the immune system during HIV disease is poorly understood. Therefore, the aim of this follow-up study was to characterize immunological parameters in HIV-infected patients with latent toxoplasmosis and noninfected individuals. A total of 101 HIV-infected patients were enrolled in the study. The patients were classified into two groups based on anti-Toxoplasma gondii antibodies: a group of 55 toxoplasma-positive persons (TP) and a group of 46 toxoplasma-negative persons (TN). Absolute counts of several lymphocyte subsets decreased in the TP group, namely, T cells (p = 0.007), B cells (p = 0.002), NK cells (p = 0.009), CD4 T cells (p = 0.028), and CD8 T cells (p = 0.004). On the other hand, the percentage of CD8 T cells expressing CD38 and HLA-DR significantly increased during the follow-up in the TP group (p = 0.003, p = 0.042, resp.) as well as the intensity of CD38 and HLA-DR expression (MFI) on CD8 T cells (p = 0.001, p = 0.057, resp.). In the TN group, analysis of the kinetics of immunological parameters revealed no significant changes over time. In conclusion, the results suggest that latent T. gondii infection modulates the immune response during HIV infection.
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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".