Cytokine Profiling in Chagas Disease: Towards Understanding the Association with Infecting Trypanosoma cruzi Discrete Typing Units (A BENEFIT TRIAL Sub-Study)
Bibliographic record
Abstract
BACKGROUND: Chagas disease caused by the protozoan Trypanosoma cruzi is an important public health problem in Latin America. The immunological mechanisms involved in Chagas disease pathogenesis remain incompletely elucidated. The aim of this study was to explore cytokine profiles and their possible association to the infecting DTU and the pathogenesis of Chagas disease. METHODS: 109 sero-positive T. cruzi patients and 21 negative controls from Bolivia and Colombia, were included. Flow cytometry assays for 13 cytokines were conducted on human sera. Patients were divided into two groups: in one we compared the quantification of cytokines between patients with and without chronic cardiomyopathy; in second group we compared the levels of cytokines and the genetic variability of T. cruzi. RESULTS: Significant difference in anti-inflammatory and pro-inflammatory cytokines profiles was observed between the two groups cardiac and non-cardiac. Moreover, serum levels of IFN-γ, IL-12, IL-22 and IL-10 presented an association with the genetic variability of T.cruzi, with significant differences in TcI and mixed infections TcI/TcII. CONCLUSION: Expression of anti-inflammatory and pro-inflammatory cytokines may play a relevant role in determining the clinical presentation of chronic patients with Chagas disease and suggests the occurrence of specific immune responses, probably associated to different T. cruzi DTUs.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".