Cognitive Hemorrhagic Onset in Cerebral Amyloid Angiopathy: Neuroimaging Features.

Background: Intracerebral hemorrhage and cognitive decline are typical clinical presentations of cerebral amyloid angiopathy (CAA).

Objective: To determine whether magnetic resonance imaging (MRI) features differ between CAA with hemorrhagic cognitive onset.

Methods: In this retrospective study, sixty-one patients with CAA were classified by onset presentation of the disease: hemorrhage (n = 31) or cognitive decline (n = 30).

Mid-term follow-up chest CT findings in recovered COVID-19 patients with residual symptoms.

Objectives: More than a year has passed since the initial outbreak of SARS-CoV-2, which caused many hospitalizations worldwide due to COVID-19 pneumonia and its complications. However, there is still a lack of information detailing short- and long-term outcomes of previously hospitalized patients. The purpose of this study is to analyze the most frequent lung CT findings in recovered COVID-19 patients at mid-term follow-ups.

Specific hypomethylation programs underpin B cell activation in early multiple sclerosis.

Epigenetic changes have been consistently detected in different cell types in multiple sclerosis (MS). However, their contribution to MS pathogenesis remains poorly understood partly because of sample heterogeneity and limited coverage of array-based methods. To fill this gap, we conducted a comprehensive analysis of genome-wide DNA methylation patterns in four peripheral immune cell populations isolated from 29 MS patients at clinical disease onset and 24 healthy controls.

Longitudinal Quantitative MRI Evaluation of Muscle Involvement in Amyotrophic Lateral Sclerosis.

Biomarkers of disease progression and outcome measures are still lacking for patients with amyotrophic lateral sclerosis (ALS). Muscle MRI can be a promising candidate to track longitudinal changes and to predict response to the therapy in clinical trials. Our aim is to apply quantitative muscle MRI in the evaluation of disease progression, focusing on thigh and leg muscles of patients with ALS, and to explore the correlation between radiological and clinical scores.

Deep learning for automatic segmentation of thigh and leg muscles.

Objective: In this study we address the automatic segmentation of selected muscles of the thigh and leg through a supervised deep learning approach.

Material And Methods: The application of quantitative imaging in neuromuscular diseases requires the availability of regions of interest (ROI) drawn on muscles to extract quantitative parameters. Up to now, manual drawing of ROIs has been considered the gold standard in clinical studies, with no clear and universally accepted standardized procedure for segmentation.