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Dogs, cats, parasites, and humans in Brazil: opening the black box.

ABSTRACT: Dogs and cats in Brazil serve as primary hosts for a considerable number of parasites, which may affect their health and wellbeing. These may include endoparasites (e.g., protozoa, cestodes, trematodes, and nematodes) and ectoparasites (i.e., fleas, lice, mites, and ticks). While some dog and cat parasites are highly host-specific (e.g., Aelurostrongylus abstrusus and Felicola subrostratus for cats, and Angiostrongylus vasorum and Trichodectes canis for dogs), others may easily switch to other hosts, including humans. In fact, several dog and cat parasites (e.g., Toxoplasma gondii, Dipylidium caninum, Ancylostoma caninum, Strongyloides stercoralis, and Toxocara canis) are important not only from a veterinary perspective but also from a medical standpoint. In addition, some of them (e.g., Lynxacarus radovskyi on cats and Rangelia vitalii in dogs) are little known to most veterinary practitioners working in Brazil. This article is a compendium on dog and cat parasites in Brazil and a call for a One Health approach towards a better management of some of these parasites, which may potentially affect humans. Practical aspects related to the diagnosis, treatment, and control of parasitic diseases of dogs and cats in Brazil are discussed.

SUBMITTER: Dantas-Torres F

PROVIDER: S-EPMC3914713 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Dogs, cats, parasites, and humans in Brazil: opening the black box.

Dantas-Torres Filipe F Otranto Domenico D

Parasites & vectors 20140114

Dogs and cats in Brazil serve as primary hosts for a considerable number of parasites, which may affect their health and wellbeing. These may include endoparasites (e.g., protozoa, cestodes, trematodes, and nematodes) and ectoparasites (i.e., fleas, lice, mites, and ticks). While some dog and cat parasites are highly host-specific (e.g., Aelurostrongylus abstrusus and Felicola subrostratus for cats, and Angiostrongylus vasorum and Trichodectes canis for dogs), others may easily switch to other h ...[more]

PMID: 24423244

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Project description:ObjectiveThe internal workings ofmachine learning algorithms are complex and considered as low-interpretation "black box" models, making it difficult for domain experts to understand and trust these complex models. The study uses metabolic syndrome (MetS) as the entry point to analyze and evaluate the application value of model interpretability methods in dealing with difficult interpretation of predictive models.MethodsThe study collects data from a chain of health examination institution in Urumqi from 2017 ~ 2019, and performs 39,134 remaining data after preprocessing such as deletion and filling. RFE is used for feature selection to reduce redundancy; MetS risk prediction models (logistic, random forest, XGBoost) are built based on a feature subset, and accuracy, sensitivity, specificity, Youden index, and AUROC value are used to evaluate the model classification performance; post-hoc model-agnostic interpretation methods (variable importance, LIME) are used to interpret the results of the predictive model.ResultsEighteen physical examination indicators are screened out by RFE, which can effectively solve the problem of physical examination data redundancy. Random forest and XGBoost models have higher accuracy, sensitivity, specificity, Youden index, and AUROC values compared with logistic regression. XGBoost models have higher sensitivity, Youden index, and AUROC values compared with random forest. The study uses variable importance, LIME and PDP for global and local interpretation of the optimal MetS risk prediction model (XGBoost), and different interpretation methods have different insights into the interpretation of model results, which are more flexible in model selection and can visualize the process and reasons for the model to make decisions. The interpretable risk prediction model in this study can help to identify risk factors associated with MetS, and the results showed that in addition to the traditional risk factors such as overweight and obesity, hyperglycemia, hypertension, and dyslipidemia, MetS was also associated with other factors, including age, creatinine, uric acid, and alkaline phosphatase.ConclusionThe model interpretability methods are applied to the black box model, which can not only realize the flexibility of model application, but also make up for the uninterpretable defects of the model. Model interpretability methods can be used as a novel means of identifying variables that are more likely to be good predictors.

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Dogs, cats, parasites, and humans in Brazil: opening the black box.

Publications

Dogs, cats, parasites, and humans in Brazil: opening the black box.

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