Epidemiological relevance of dogs for the prevention of Toxoplasma gondii, Neospora caninum and Leptospira spp.
Bibliographic record
Abstract
This study evaluated the seroprevalence of Toxoplasma gondii, Neospora caninum and Leptospira spp. in dogs from Foz do Iguaçu, Paraná, Brazil. Indirect immunofluorescent antibody test was used to detect antibodies anti-T.gondii and anti-N. caninum. Immunoenzymatic assay and microscopic serum agglutination were used for screening antibodies anti-T.gondii and anti-Leptospira spp., respectively. The results were: 67.02% of the samples reactive for T.gondii and 1.38% for N. caninum, both without statistically significant variables. For Leptospira spp. the results indicated seroprevalence of 23.11%. The analysis of the variables without distinction of serovar showed association for intrinsic characteristics as breed, age, nutritional status and dog category. The extrinsic variables as city region and access to the street presented association (p<0.05). The most prevalent serovars were: Canicola 59.47%; Bratislava 13.07% and Butembo 15.68%. Variables that make up the adjusted multiple analysis model using Leptospira spp. were: age, breed and nutritional status; serovar Canicola, sex, nutritional status and area (p<0.05); serovar Bratislava, lymphadenomegaly and presence of fleas (p<0.05). Given the results obtained, dogs can be used as sentinels for toxoplasmosis and leptospirosis in Foz do Iguaçu and other cities with similar outcomes. In addition, preventive measures should be taken by health authorities because they are zoonoses and humans are also at risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".