Global, regional, and national burdens of intracerebral hemorrhage and its risk factors from 1990 to 2021.

Background And Purpose: The aim of this study was to assess the intracerebral hemorrhage (ICH) burden in 204 countries and territories worldwide from 1990 to 2021, disaggregated by sex, age, and sociodemographic index (SDI) at the global, regional, and country levels.

Methods: Data from the 2021 Global Burden of Disease Study (GBD) were used to calculate age-standardized prevalence (ASPR), incidence (ASIR), death (ASDR), and disability-adjusted life year (DALY) rates for ICH. The estimated annual percentage change (EAPC) was used to assess time patterns.

Plasma water T detects age-stratified differences in cardiometabolic health among familial CCM patients with Hispanic CCM1 mutation.

Cerebral cavernous malformations (CCMs) are abnormal clusters of capillaries in the nervous system. This pilot study analyzed the cardiometabolic health status of individuals with familial CCMs caused by a rare mutation in the CCM1 gene (fCCM1). The aim was to compare plasma water T values from individuals with fCCM1 with values from metabolically unhealthy and healthy individuals with no known CCM mutations.

Plasma water T2 detects age-stratified differences in cardiometabolic health among familial CCM patients with Hispanic CCM1 mutation.

Introduction: Cerebral cavernous malformations (CCMs) are abnormal clusters of capillaries in the nervous system. This pilot study analyzed the cardiometabolic health status of individuals with familial CCMs caused by a rare mutation in the gene (fCCM1). The aim was to compare plasma water T values from individuals with fCCM1 with values from metabolically unhealthy and healthy individuals with no known CCM mutations.

Plasma water T detects age-stratified differences in cardiometabolic health among familial CCM patients with Hispanic CCM1 mutation.

Introduction: Cerebral cavernous malformations (CCMs) are abnormal clusters of capillaries in the nervous system. This pilot study analyzed the cardiometabolic health status of individuals with familial CCMs caused by a rare mutation in the gene (fCCM1). The aim was to compare plasma water T values from individuals with fCCM1 with values from metabolically unhealthy and healthy individuals with no known CCM mutations.

Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications.

Diabetes is a major public health challenge affecting more than 451 million people. Physiological and experimental factors influence the accuracy of non-invasive glucose monitoring, and these need to be overcome before replacing the finger prick method. Also, the suitable employment of machine learning techniques can significantly improve the accuracy of glucose predictions.