Catholic University of the Sacred Heart

About: Catholic University of the Sacred Heart is a education organization based out in Milan, Lombardia, Italy. It is known for research contribution in the topics: Population & Health care. The organization has 13592 authors who have published 31048 publications receiving 853961 citations.

Assessment of muscle function and physical performance in daily clinical practice: a position paper endorsed by the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO).

Abstract: It is well recognized that poor muscle function and poor physical performance are strong predictors of clinically relevant adverse events in older people. Given the large number of approaches to measure muscle function and physical performance, clinicians often struggle to choose a tool that is appropriate and validated for the population of older people they deal with. In this paper, an overview of different methods available and applicable in clinical settings is proposed. This paper is based on literature reviews performed by members of the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) working group on frailty and sarcopenia. Face-to-face meetings were organized afterwards where the whole group could amend and discuss the recommendations further. Several characteristics should be considered when choosing a tool: (1) purpose of the assessment (intervention, screening, diagnosis); (2) patient characteristics (population, settings, functional ability, etc.); (3) psychometric properties of the tool (test–retest reliability, inter-rater reliability, responsiveness, floor and ceiling effects, etc.); (4) applicability of the tool in clinical settings (overall cost, time required for the examination, level of training, equipment, patient acceptance, etc.); (5) prognostic reliability for relevant clinical outcomes. Based on these criteria and the available evidence, the expert group advises the use of grip strength to measure muscle strength and the use of 4-m gait speed or the Short Physical Performance Battery test to measure physical performance in daily practice. The tools proposed are relevant for the assessment of muscle weakness and physical performance. Subjects with low values should receive additional diagnostic workups to achieve a full diagnosis of the underlying condition responsible (sarcopenia, frailty or other).

Prevalence of Fabry Disease in Female Patients With Late-Onset Hypertrophic Cardiomyopathy

Abstract: Background— Fabry disease (FD) has been recognized as the cause of left ventricular hypertrophy in 6% of men with late-onset hypertrophic cardiomyopathy (HCM). Although FD is considered a recessive X-linked disorder, affected women are increasingly reported. The aim of our study was to determine the prevalence of FD in female patients with HCM. Methods and Results— Thirty-four consecutive women (mean age, 50±13.6 years) who received an ECG and echocardiographic diagnosis of HCM were submitted to an invasive cardiac study that included a biventricular endomyocardial biopsy. Tissue samples were analyzed for histology and electron microscopy. Peripheral blood activity of α-galactosidase (α-Gal) A was assessed in all patients. None of them had a family history of FD. Histology and electron microscopy showed in 4 patients (12%; mean age, 51.5±3.9 years) the presence of cell vacuoles characterized by the accumulation of glycolipid material organized in concentric lamellar structures, diagnostic for FD. In the r...

Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines

Abstract: This paper addresses the prediction of epileptic seizures from the online analysis of EEG data. This problem is of paramount importance for the realization of monitoring/control units to be implanted on drug-resistant epileptic patients. The proposed solution relies in a novel way on autoregressive modeling of the EEG time series and combines a least-squares parameter estimator for EEG feature extraction along with a support vector machine (SVM) for binary classification between preictal/ictal and interictal states. This choice is characterized by low computational requirements compatible with a real-time implementation of the overall system. Moreover, experimental results on the Freiburg dataset exhibited correct prediction of all seizures (100 % sensitivity) and, due to a novel regularization of the SVM classifier based on the Kalman filter, also a low false alarm rate.