ABSTRACT
Dogs are the most popular pet animals worldwide, but on the other hand, they are main hosts of pathogens potentially transmissible to humans. The aim of this study was to assess the occurrence of intestinal parasites in free- roaming and owned dogs in an urban area in southeastern Brazil and to identify the hookworm species infecting them. Faecal samples (80 from free-roaming and 53 from owned dogs) were examined for intestinal parasites using concentration methods. DNA extracted from hookworm microscopy-positive samples were tested by PCR targeting the ITS1-5.8S-ITS2 region and the amplicons retrieved were sequenced. Intestinal parasites were detected in 43.60% (58/133) of the dogs and hookworm infection was found at the highest prevalence rate (38.30%), followed by Toxocara canis (10.50%), Trichuris vulpis (2.25%), Giardia spp. (0.75%) and Cystoisospora spp. (0.75%). Out of the 51 samples positive for hookworm eggs, 26 (50.90%) were successfully amplified and sequenced. Single infections with Ancylostoma caninum and Ancylostoma braziliense were recorded in 18 (69.20%) and two (7.70%) isolates, respectively, and mixed infections were found in the remaining six samples (23.10%). Both species were found infecting free-roaming and owned animals, but A. caninum was more common. These findings highlight the public health relevance of dogs as reservoirs of zoonotic parasites, with emphasis on hookworm species commonly implicated in cutaneous larva migrans (CLM) in poor and deprived areas.
ABSTRACT
Classical quantification of gastric emptying (GE) and orocaecal transit (OCT) based on half-life time T$ _{50} $, mean gastric emptying time (MGET), orocaecal transit time (OCTT) or mean caecum arrival time (MCAT) can lead to misconceptions when analyzing irregularly or noisy data. We show that this is the case for gastrointestinal transit of control and of diabetic rats. Addressing this limitation, we present an artificial neural network (ANN) as an alternative tool capable of discriminating between control and diabetic rats through GE and OCT analysis. Our data were obtained via biological experiments using the alternate current biosusceptometry (ACB) method. The GE results are quantified by T$ _{50} $ and MGET, while the OCT is quantified by OCTT and MCAT. Other than these classical metrics, we employ a supervised training to classify between control and diabetes groups, accessing sensitivity, specificity, $ f_1 $ score, and AUROC from the ANN. For GE, the ANN sensitivity is 88%, its specificity is 83%, and its $ f_1 $ score is 88%. For OCT, the ANN sensitivity is 100%, its specificity is 75%, and its $ f_1 $ score is 85%. The area under the receiver operator curve (AUROC) from both GE and OCT data is about 0.9 in both training and validation, while the AUCs for classical metrics are 0.8 or less. These results show that the supervised training and the binary classification of the ANN was successful. Classical metrics based on statistical moments and ROC curve analyses led to contradictions, but our ANN performs as a reliable tool to evaluate the complete profile of the curves, leading to a classification of similar curves that are barely distinguished using statistical moments or ROC curves. The reported ANN provides an alert that the use of classical metrics can lead to physiological misunderstandings in gastrointestinal transit processes. This ANN capability of discriminating diseases in GE and OCT processes can be further explored and tested in other applications.
