COVID-19 in patients with Myasthenia Gravis: epidemiology and disease course

COVID-19, a disease caused by SARS-CoV-2 infection, has become a global pandemic. Patients with myasthenia gravis (MG), often treated with immunosuppressants, might be at higher risk of developing COVID-19 and of demonstrating a severe disease course. We aimed to study prevalence and describe features of COVID-19 in MG patients

In person versus remote cognitive rehabilitation in patients with subjective cognitive decline or neurocognitive disorders: what factors drive patient’s preference?

BackgroundTo date, there is still a lack of consensus for identifying the ideal candidate for cognitive telerehabilitation (TR). The main goal of the present study is to identify the factors associated to the preference for either TR or in-person cognitive training (CT) programs in older adults at risk of dementia or with early cognitive impairment.MethodsA sample of 56 participants with subjective cognitive decline or neurocognitive disorders eligible for CT were enrolled at the Dementia Research Center and Neurorehabilitation Unit of IRCCS Mondino Foundation. All individuals underwent a baseline assessment to capture their complete profile, including cognitive reserve and lifestyle habits, sociodemographic characteristics, cognitive functioning, and mental health. Patients were then asked their preference for TR or in-person CT, before being randomized to either treatment as per protocol procedures. Statistical analyses included explorative descriptive approach, logistic regression, and non-parametric models to explore the overall contribution of each variable.ResultsThe two (TR and in-person) preference groups were similar for cognitive functioning and mental health status. Socio-demographic and lifestyle profiles seem to be the most important factors to influence the preference in terms of the area under the curve (AUC) of the models. The two preference groups differed in terms of socio-demographic characteristics (e.g., level of technological skills, age, and distance from the clinic). Furthermore, participants who selected the TR modality of CT had significantly higher levels of cognitive reserve and adopted more protective lifestyle habits (e.g., regular physical activity, Mediterranean diet) when compared to those who preferred in-person CT.DiscussionThese findings highlight that the preference to receive CT delivered by TR or in person is a complex issue and is influenced by a variety of factors, mostly related to lifestyle habits and sociodemographic characteristics. Availability of profiles of patients that may be more attracted to one or the other modality of TR may help promote shared decision-making to enhance patient experience and outcomes

Ketonemia variability through menstrual cycle in patients undergoing classic ketogenic diet

IntroductionKetogenic dietary therapies (KDT) are well-established, safe, non-pharmacologic treatments used for children and adults with drug-resistant epilepsy and other neurological disorders. Ketone bodies (KBs) levels are recognized as helpful to check compliance to the KDT and to attempt titration of the diet according to the individualized needs. KBs might undergo inter-individual and intra-individual variability and can be affected by several factors. Possible variations in glycemia and ketone bodies blood levels according to the menstrual cycle have not been systematically assessed yet, but this time window deserves special attention because of hormonal and metabolic related changes.MethodsThis study aims at searching for subtle changes in KBs blood level during menstrual cycle in female patients undergoing a stable ketogenic diet, by analyzing 3-months daily measurement of ketone bodies blood levels and glucose blood levels throughout the menstrual cycle.ResultsWe report the preliminary results on six female patients affected by GLUT1DS or drug resistant epilepsy, undergoing a stable classic ketogenic diet. A significant increase in glucose blood levels during menstruation was found in the entire cohort. As far as the ketone bodies blood levels, an inversely proportional trend compared to glycemia was noted.ConclusionExploring whether ketonemia variations might occur according to the menstrual cycle is relevant to determine the feasibility of transient preventive diet adjustments to assure a continuative treatment efficacy and to enhance dietary behavior support.Clinical trial registrationclinicaltrials.gov, identifier NCT05234411

Statistical and Machine Learning models for Neurosciences

This thesis addresses several problems encountered in the field of statistical and machine learning methods for data analysis in neurosciences. The thesis is divided into three parts. The first part of the thesis is related to classification tree models. In the research field of polarization measures, a new polarization measure is defined. The function is incorporated in the decision tree algorithm as a splitting function in order to tackle some weaknesses of classical impurity measures. The new algorithm is called Polarized Classification Tree model. The model is tested on simulated and real data sets and compared with decision tree models where the classical impurity measures are deployed. In the second part of the thesis a new index for assessing and selecting the best model in a classification task when the target variable is ordinal is developed. The index proposed is compared to the traditional measures on simulated data sets and it is applied in a real case study related to Attenuated Psychosis Syndrome. The third part covers the topic of smoothing methods for quaternion time series data in the context of motion data classification. Different proper methods to smoothing time series in quaternion algebra are reviewed and a new method is proposed. The new method is compared with a method proposed in the literature in terms of classification performances on a real data set and five data sets obtained introducing different degrees of noise. The results confirmed the hypothesis made on the basis of the theoretical information available from the two methods, i.e. the logarithm is smoother and generally provides better results than the existing method in terms of classification performances

A new approach in model selection for ordinal target variables

Multi-class predictive models are generally evaluated averaging binary classification indicators without a distinction between nominal and ordinal dependent variables. This paper introduces a novel approach to assess performances of predictive models characterized by an ordinal target variable and a new index for model evaluation is proposed. The new index satisfies mathematical properties and it can be applied to the evaluation of parametric and non parametric models. In order to show how our performance indicator works, empirical evidences obtained on toy examples and simulated data are provided. On the basis of the results achieved, we underline that our approach can be a more suitable criterion for model selection than the performance indexes currently suggested in the literature

Polarized classification tree models: theory and computational aspects

In this paper, a new approach in classification models, called Polarized Classification Tree model, is introduced. From a methodological perspective, a new index of polarization to measure the goodness of splits in the growth of a classification tree is proposed. The new introduced measure tackles weaknesses of the classical ones used in classification trees (Gini and Information Gain), because it does not only measure the impurity but it also reflects the distribution of each covariate in the node, i.e., employing more discriminating covariates to split the data at each node. From a computational prospective, a new algorithm is proposed and implemented employing the new proposed measure in the growth of a tree. In order to show how our proposal works, a simulation exercise has been carried out. The results obtained in the simulation framework suggest that our proposal significantly outperforms impurity measures commonly adopted in classification tree modeling. Moreover, the empirical evidence on real data shows that Polarized Classification Tree models are competitive and sometimes better with respect to classical classification tree models

Economic Segregation Under the Action of Trading Uncertainties

We study the distribution of wealth in a market economy in which the trading propensity of the agents is uncertain. Our approach is based on kinetic models for collective phenomena, which, at variance with the classical kinetic theory of rarefied gases, has to face the lack of fundamental principles, which are replaced by empirical social forces of which we have at most statistical information. The proposed kinetic description allows recovering emergent wealth distribution profiles, which are described by the steady states of a Fokker–Planck-type equation with uncertain parameters. A statistical study of the stationary profiles of the Fokker–Planck equation then shows that the wealth distribution can develop a multimodal shape in the presence of observable highly stressful economic situations

Economic Segregation Under the Action of Trading Uncertainties

We study the distribution of wealth in a market economy in which the trading propensity of the agents is uncertain. Our approach is based on kinetic models for collective phenomena, which, at variance with the classical kinetic theory of rarefied gases, has to face the lack of fundamental principles, which are replaced by empirical social forces of which we have at most statistical information. The proposed kinetic description allows recovering emergent wealth distribution profiles, which are described by the steady states of a Fokker–Planck-type equation with uncertain parameters. A statistical study of the stationary profiles of the Fokker–Planck equation then shows that the wealth distribution can develop a multimodal shape in the presence of observable highly stressful economic situations