Quantitative Physiologic MRI Combined with Feature Engineering for Developing Machine Learning-Based Prediction Models to Distinguish Glioblastomas from Single Brain Metastases.

: The accurate and early distinction of glioblastomas (GBMs) from single brain metastases (BMs) provides a window of opportunity for reframing treatment strategies enabling optimal and timely therapeutic interventions. We sought to leverage physiologically sensitive parameters derived from diffusion tensor imaging (DTI) and dynamic susceptibility contrast (DSC)-perfusion-weighted imaging (PWI) along with machine learning-based methods to distinguish GBMs from single BMs. : Patients with histopathology-confirmed GBMs ( = 62) and BMs ( = 26) and exhibiting contrast-enhancing regions (CERs) underwent 3T anatomical imaging, DTI and DSC-PWI prior to treatment.

Unequal Effects of the Lockdown on Mental Health in Shanghai: The Moderating and Mediating Role of Neighborhood Environment and Online Social Connections.

The COVID-19 pandemic profoundly impacted population mental health worldwide. Few studies examined how the neighborhood environment and online social connections might influence the social gradient in mental health during the pandemic lockdown. We aim to examine the moderating and mediating role of neighborhood environment and online social connections in the association between socioeconomic status (SES) and mental health outcomes.

Integration of pharmacogenetic data in epic genomic module drives clinical decision support alerts.

Introduction: The Precision Medicine Program (PMP) at the University of Florida (UF) focuses on advancing pharmacogenomics (PGx) to improve patient care.

Methods: The UF PMP, in collaboration with the UF Health Pathology Laboratory (UFHPL), utilized Health Level Seven (HL7) standards to integrate PGx data into Epic's Genomic Module to enhance the management and utilization of PGx data in clinical practice.

Results: A key feature of the Genomic Module is the introduction of genomic indicators-innovative tools that flag actionable genetic information directly within the electronic health record (EHR).

Real-world evidence for passive video-based cardiac monitoring from smartphones used by patients with a history of AF.

Passive cardiac monitoring has become synonymous with wearable technologies, necessitating patients to incorporate new devices into their daily routines. While this requirement may not be a burden for many, it is a constraint for individuals with chronic diseases who already have their daily routine. In this study, we introduce an innovative technology that harnesses the front-facing camera of smartphones to capture pulsatile signals discreetly when users engage in other activities on their device.