Background: Multiple sclerosis (MS) constitutes a chronic progressive demyelinating disease which negatively affects the central nervous system. MS symptoms detrimentally affect the quality of life, as well as the life expectancy of MS patients. In this aspect, the present study aims to critically summarize and evaluate the currently available clinical studies focusing on the potential beneficial effects of dietary supplements on controlling MS symptomatology and relapse. Methods: PubMed database was comprehensively searched, using relative keywords to identify clinical trials that investigated the beneficial effects of dietary supplementation against MS symptomatology and progression. 40 clinical trials were found, which were divided into categories. Results: Nutritional status of MS patients, as well as supplementation have been suggested as potential factors affecting progression. Several substantial studies have documented a systematically high prevalence of vitamin A, B12 and D3 deficiency amongst MS patients. At present, clinical data have suggested that most of the dietary supplements under study may exert antioxidant and anti-inflammatory properties, improving depression symptomatology and quality of life overall. However, malnutrition risk in MS patients has not been adequately explored in order for more precise conclusions to be drawn. The supplements that may have a positive effect on MS are vitamins, fatty acids, antioxidants, phytochemicals and melatonin. Conclusions: Several dietary supplements may decrease inflammation and fatigue, also increasing also autoimmunity tolerance in MS patients, and thus improving quality of life and life expectancy. Currently, there is no effective clinical indication for applying dietary supplementation as complementary treatment against MS symptomatology.

In the last decades, there is a vivid interest of many researchers about algorithms with natural procedure similarities. Artificial Neural Networks (ANNs) are wide known algorithms, which have been developed in order to solve complex combinational problems and can be proven as a powerful tool in medical research. This paper implements computational algorithms, which were based on artificial neural networks for the dementia's types distinguish. Diversity of ANN architectures are implemented and assessed for correct prediction of types of dementia, based on available cases. The obtained results prove that ANNs have the ability to be incorporated in the field of Neurology.