No abstract

The aims of this study were to carry out a serological survey of canine leishmaniasis and identify the phlebotomine fauna in the urban area of Bonito, Mato Grosso do Sul. The serological survey was conducted on a sample of 303 dogs, by means of the indirect immunofluorescence test. Phlebotomines were captured using automated light traps. The serological survey found that 30% of the dogs were seropositive, both from the center and from all districts of the town. A total of 2,772 specimens of phlebotomines were caught and the species most found was Lutzomyia longipalpis (90.4%), which corroborated its role as the vector of for canine visceral leishmaniasis in the region. Phlebotomines of the species Bichromomyia flaviscutellata (the main vector for Leishmania (Leishmania) amazonensis) and Nyssomyia whitmani (the vector for Leishmania (Viannia) brasiliensis) were also caught. The findings indicate the need for continuous epidemiological surveillance, with attention towards diminishing the vector breeding sites and the transmission of these diseases in that region.

BackgroundMato Grosso do Sul has been undergoing a process of urbanization which results in loss of native vegetation. This withdrawal makes vectors of man and domestic animals closer, causing changes in the epidemiology of diseases such as American Visceral Leishmaniasis. The aim of the study was to evaluate the phlebotomine fauna and environmental issues related to the transmission of AVL in Ponta Porã, Mato Grosso do Sul, between 2009 and 2010.MethodsVegetation of the urban area was evaluated by Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Soil Adjusted Vegetation Index (SAVI).ResultsThe results showed that the phlebotomine fauna of the city consists of five species, especially Lutzomyia longipalpis (Lutz and Neiva, 1912), the vector of Leishmania (Leishmania) infantum. Predominance of males was observed. The insects were captured in greater quantity in the intradomicile. Lu. longipalpis was the most frequent and abundant species, present throughout the year, with a peak population after the rainy season. Vectors can be found in high amounts in forest and disturbed environments.ConclusionsThe finding of Lu. longipalpis in regions with little vegetation and humidity suggests that the species is adapted to different sorts of environmental conditions, demonstrating its close association with man and the environment it inhabits. The tourist feature of Ponta Porã reinforces its epidemiological importance as a vulnerable city. The geographical location, bordering Paraguay through dry border, makes possible the existence of a corridor of vectors and infected dogs between the two countries.

Background and aims Capsule endoscopy is a central element in the management of patients with suspected or known Crohn’s disease. In 2017, PillCam™ Crohn’s Capsule was introduced and demonstrated greater accuracy in the evaluation of extension of disease in these patients. Artificial Intelligence is expected to enhance the diagnostic accuracy of capsule endoscopy. This study aims to develop an AI algorithm for the automatic detection of ulcers and erosions of the small intestine and colon in PillCam™ Crohn’s Capsule images. Methods A total of 8085 PillCam™ Crohn’s Capsule images were extracted between 2017-2020, constituted by 2855 images of ulcers and 1975 erosions; the remaining images showed normal enteric and colonic mucosa. This pool of images was subsequently split into training and validation datasets. The performance of the network was subsequently assessed in an independent test set. Results The model had an overall sensitivity and specificity of 90.0% and 96.0%, respectively. The precision and accuracy of this model were 97.1% and 92.4%, respectively. Particularly, the algorithm detected ulcers with a sensitivity of 83% and specificity of 98%, and erosions with sensitivity and specificity of 91% and 93%, respectively. Conclusion A deep learning model capable of automatically detecting ulcers and erosions in PillCam™ Crohn’s Capsule images was developed for the first time. These findings pave the way for the development of automatic systems for detection of clinically significant lesions, optimizing diagnostic performance and efficiency of monitoring Crohn’s disease activity.