More than the eyes can see: The worrying scenario of canine leishmaniasis in the Brazilian side of the triple border
Bibliographic record
Abstract
A cross-sectional epidemiological study in the extreme-west of the state of Paraná was carried out to access the prevalence, distribution, and risk variables of canine Visceral Leishmaniases (cVL). This study was conducted in three areas, two cities of far west of Parana state: Foz do Iguaçu (FI) and Santa Terezinha de Itaipu (STI), and along two transects between these two municipalities. To sample the entire urban area, the cities (FI and STI) were divided into a grid of squares of 400 m2 (patch). Among the 526 patches, 123 in FI, 40 in the transects and 33 in STI were selected according to the 'worst scenario' criterion. In the transect areas, in each 0.86 km five dogs from houses were surveyed to leishmaniasis. In each patch, blood of five dogs from houses (and from neighborhood when necessary) in the areas that seemed to be the most appropriate for the proliferation of vector were surveyed. The infection of the dogs by cVL were assessed using two serological tests were used (cELISA and TR-DPP®), and, for those seropositive for both methods, the PCR method were used. Moreover, dogs presenting clinical signs or cutaneous lesions were sampled to PCR. The identification of Leishmania species was confirmed using PCR-RFLP followed by DNA sequencing. Micro, meso and macro scale environmental variables were also surveyed and statistically analyzed. The prevalence rate Leishmania infantum was 23.8% in FI, 4.7% in STI and 9.1% in the transects areas. Among the extrinsic variables analysed, the number of vectors and the presence of infected dogs in neighbouring were positively correlated with the occurrence of infected dogs. Dog size was positively correlated with cVL infection, while the quality of the dog's nutrition affected cVL negatively. As for cutaneous leishmaniasis (CL), the first registry of dogs infected with L. braziliensis in the region shows that there is potential for transmission in peri-urban areas, since environmental conditions allow the proliferation of vectors capable of transmitting this species of parasite. cVL is widely spread in FI, with high prevalence. This supports the hypothesis that the parasite has been present in the region for longer than previously believed, despite the fact that the presence of leishmaniais in the region has only been recognized recently. It is important to control the population of dogs infected with L. infantum (parasite and non-antibodies) to prevent the spread of the disease to other dogs and also to people in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".