Magnetic resonance imaging in the diagnosis of white matter signal abnormalities.

Background White matter abnormalities (WMAs) pose a diagnostic challenge when trying to establish etiologic diagnoses. During childhood and adult years, genetic disorders, metabolic disorders and acquired conditions are included in differential diagnoses. To assist clinicians and radiologists, a structured algorithm using cranial magnetic resonance imaging (MRI) has been recommended to aid in establishing working diagnoses that facilitate appropriate biochemical and genetic investigations. This retrospective pilot study investigated the validity and diagnostic utility of this algorithm when applied to white matter signal abnormalities (WMSAs) reported on imaging studies of patients seen in our clinics. Methods The MRI algorithm was applied to 31 patients selected from patients attending the neurometabolic/neurogenetic/metabolic/neurology clinics at a tertiary care hospital. These patients varied in age from 5 months to 79 years old, and were reported to have WMSAs on cranial MRI scans. Twenty-one patients had confirmed WMA diagnoses and 10 patients had non-specific WMA diagnoses (etiology unknown). Two radiologists, blinded to confirmed diagnoses, used clinical abstracts and the WMSAs present on patient MRI scans to classify possible WMA diagnoses utilizing the algorithm. Results The MRI algorithm displayed a sensitivity of 100%, a specificity of 30.0% and a positive predicted value of 74.1%. Cohen\u27s kappa statistic for inter-radiologist agreement was 0.733, suggesting good agreement between radiologists. Conclusions Although a high diagnostic utility was not observed, results suggest that this MRI algorithm has promise as a clinical tool for clinicians and radiologists. We discuss the benefits and limitations of this approach

Risk factors associated with Trypanosoma cruziexposure in domestic dogs from a rural community in Panama

Chagas disease, caused by Trypanosoma cruzi infection, is a zoonosis of humans, wild and domestic mammals,including dogs. In Panama, the main T. cruzi vector is Rhodnius pallescens, a triatomine bug whose main naturalhabitat is the royal palm, Attalea butyracea. In this paper, we present results from three T. cruzi serological tests(immunochromatographic dipstick, indirect immunofluorescence and ELISA) performed in 51 dogs from 24 housesin Trinidad de Las Minas, western Panama. We found that nine dogs were seropositive (17.6% prevalence). Dogswere 1.6 times more likely to become T. cruzi seropositive with each year of age and 11.6 times if royal palms wherepresent in the peridomiciliary area of the dog’s household or its two nearest neighbours. Mouse-baited-adhesivetraps were employed to evaluate 12 peridomestic royal palms. All palms were found infested with R. pallescens withan average of 25.50 triatomines captured per palm. Of 35 adult bugs analysed, 88.6% showed protozoa flagellates intheir intestinal contents. In addition, dogs were five times more likely to be infected by the presence of an additionaldomestic animal species in the dog’s peridomiciliary environment. Our results suggest that interventions focused onroyal palms might reduce the exposure to T. cruzi infection

eWound-PRIOR: An Ensemble Framework for Cases Prioritization After Orthopedic Surgeries

© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. Patient follow-up appointments are an imperative part of the healthcare model to ensure safe patient recovery and proper course of treatment. The use of mobile devices can help patient monitoring and predictive approaches can provide computational support to identify deteriorating cases. Aiming to aggregate the data produced by those devices with the power of predictive approaches, this paper proposes the eWound-PRIOR framework to provide a remote assessment of postoperative orthopedic wounds. Our approach uses Artificial Intelligence (AI) techniques to process patients’ data related to postoperative wound healing and makes predictions as to whether the patient requires an in-person assessment or not. The experiment results showed that the predictions are promising and adherent to the application context, even if the on-line questionnaire had impaired the training model and the performance