Dietary Patterns and Metabolic Disorders in Polish Adults with Multiple Sclerosis.

Diet plays a major role in the aetiopathogenesis of many neurological diseases and may exacerbate their symptoms by inducing the occurrence of metabolic disorders. The results of research on the role of diet in the course of multiple sclerosis (MS) are ambiguous, and there is still no consensus concerning dietary recommendations for patients with MS. The aim of this study was to analyse the dietary patterns (DPs) of patients with MS and to assess the relationships between these DPs and the metabolic disorders. The study participants were comprised of 330 patients aged 41.9 ± 10.8 years. A survey questionnaire was used to collect data related to diet, lifestyle and health. The DPs were identified using a principal component analysis (PCA). Three DPs were identified: Traditional Polish, Prudent and Fast Food & Convenience Food. An analysis of the odds ratios adjusted for age, gender, smoking and education showed that a patient's adherence to the Traditional Polish and the Fast Food & Convenience Food DPs increased the likelihood of abdominal obesity and low HDL-cholesterol concentration. Conversely, adherence to the Prudent DP was not significantly associated with any metabolic disorder. The results of this study confirmed that an unhealthy diet in patients with MS is connected with the presence of some metabolic risk factors. There is also an urgent need to educate patients with MS on healthy eating, because the appropriate modifications to their diet may improve their metabolic profile and clinical outcomes.

Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age.

Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting that, in most circumstances, the proposed technique outperforms both existing SVM and AlexNet. The overall accuracy obtained is 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%), with best accuracy of 100%, 92%, 92%, and 81%, respectively.