Parasite and bacterial co-infections with Leishmania spp. in dogs
Bibliographic record
Abstract
Canine visceral leishmaniasis (CVL) is a major disease affecting dogs and is often associated with other illnesses. In this study, we investigated the distribution of helminths, ectoparasites and bacteria in dogs of an endemic urban area of CVL. A total of 71 dogs, uninfected or naturally infected with Leishmania spp. were studied. Splenic samples were cultured for Leishmania identification, and anti-Leishmania antibodies were measured in the serum. Helminths were diagnosed in the feces using flotation or spontaneous sedimentation techniques. Serum antibodies against six ectoparasite-transmitted pathogens were detected. Microbial growth from eyes, skin, urine, and blood samples were evaluated. To our knowledge, this is the first time that co-infections with Leishmania spp., parasites and bacteria together has been reported. Co-infections with Leishmania were observed in 89% of the animals with helminths and 95% with ectoparasites. Most of the dogs were positive for Ehrlichia spp. and Anaplasma spp. Coagulase-negative Staphylococcus was the most frequently isolated organism. It is found that Leishmania positivity dogs from endemic area in Brazil have a higher rate of co-infections with helminths, ectoparasites and bacteria. Therefore, effective treatment and public measures are needed to contain the spread of canine leishmaniasis and other infections.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".