One Health Approach to Trypanosoma cruzi: Serological and Molecular Detection in Owners and Dogs Living on Oceanic Islands and Seashore Mainland of Southern Brazil
Bibliographic record
Abstract
Via a One Health approach, this study concomitantly assessed the susceptibility of humans and dogs to Trypanosoma cruzi infections on three islands and in two mainland seashore areas of southern Brazil. Human serum samples were tested using an enzyme-linked immunosorbent assay (ELISA) to detect anti-T. cruzi antibodies, while dog serum samples were tested using indirect fluorescent antibodies in an immunofluorescence assay (IFA). Seropositive human and dog individuals were also tested using quantitative polymerase chain reaction (qPCR) in corresponding blood samples. Overall, 2/304 (0.6%) human and 1/292 dog samples tested seropositive for T. cruzi by ELISA and IFA, respectively, and these cases were also molecularly positive for T. cruzi by qPCR. Although a relatively low positivity rate was observed herein, these cases were likely autochthonous, and the individuals may have been infected as a consequence of isolated events of disturbance in the natural peridomicile areas nearby. Such a disturbance could come in the form of a fire or deforestation event, which can cause stress and parasitemia in wild reservoirs and, consequently, lead to positive triatomines. In conclusion, T. cruzi monitoring should always be conducted in suspicious areas to ensure a Chagas disease-free status over time. Further studies should also consider entomological and wildlife surveillance to fully capture the transmission and spread of T. cruzi on islands and in seashore mainland areas of Brazil and other endemic countries.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".