A Unified Approach to Health Data Exchange: A Report From the US DHHS.

Importance: Health information technology, such as electronic health records (EHRs), has been widely adopted, yet accessing and exchanging data in the fragmented US health care system remains challenging. To unlock the potential of EHR data to improve patient health, public health, and health care, it is essential to streamline the exchange of health data. As leaders across the US Department of Health and Human Services (DHHS), we describe how DHHS has implemented fundamental building blocks to achieve this vision.

Subventricular Zone Microstructure in Pediatric-Onset Multiple Sclerosis.

Objective: The aim of this study was to explore the microstructural dynamics of the subventricular zone (SVZ) with aging and their associations with clinical disability and brain structural damage in pediatric-onset multiple sclerosis (MS) patients.

Methods: One-hundred and forty-one pediatric-onset MS patients (67 pediatric and 74 adults with pediatric-onset) and 233 healthy controls (HC) underwent neurological and 3.0 T MRI assessment.

Soma and neurite density abnormalities of paramagnetic rim lesions and core-sign lesions in multiple sclerosis.

Background: In multiple sclerosis (MS), susceptibility-weighted imaging (SWI) may reveal white matter lesions (WML) with a paramagnetic rim ("paramagnetic rim lesions" [PRLs]) or diffuse hypointensity ("core-sign lesions"), reflecting different stages of WML evolution.

Objective: Using the soma and neurite density imaging (SANDI) model on diffusion-weighted magnetic resonance imaging (MRI), we characterized microstructural abnormalities of MS PRLs and core-sign lesions and their clinical relevance.

Methods: Forty MS patients and 20 healthy controls (HC) underwent a 3 T brain MRI.

Measuring water pollution effects on antimicrobial resistance through explainable artificial intelligence.

Antimicrobial resistance refers to the ability of pathogens to develop resistance to drugs designed to eliminate them, making the infections they cause more difficult to treat and increasing the likelihood of disease diffusion and mortality. As such, antimicrobial resistance is considered as one of the most significant and universal challenges to both health and society, as well as the environment. In our research, we employ the explainable artificial intelligence paradigm to identify the factors that most affect the onset of antimicrobial resistance in diversified territorial contexts, which can vary widely from each other in terms of climatic, economic and social conditions.