Perfusion estimation from dynamic non-contrast computed tomography using self-supervised learning and a physics-inspired U-net transformer architecture.

Purpose: Pulmonary perfusion imaging is a key lung health indicator with clinical utility as a diagnostic and treatment planning tool. However, current nuclear medicine modalities face challenges like low spatial resolution and long acquisition times which limit clinical utility to non-emergency settings and often placing extra financial burden on the patient. This study introduces a novel deep learning approach to predict perfusion imaging from non-contrast inhale and exhale computed tomography scans (IE-CT).

High-Field-Blinded Assessment of Portable Ultra-Low-Field Brain MRI for Multiple Sclerosis.

Background And Purpose: MRI is crucial for multiple sclerosis (MS), but the relative value of portable ultra-low field MRI (pULF-MRI), a technology that holds promise for extending access to MRI, is unknown. We assessed white matter lesion (WML) detection on pULF-MRI compared to high-field MRI (HF-MRI), focusing on blinded assessments, assessor self-training, and multiplanar acquisitions.

Methods: Fifty-five adults with MS underwent pULF-MRI following their HF-MRI.

Eco-friendly cellulose paper composites: A sustainable solution for EMI shielding and green engineering applications.

Cellulose paper-based composites represent a promising and sustainable alternative for electromagnetic interference (EMI) shielding applications. Derived from renewable and biodegradable cellulose fibers, these composites are enhanced with conductive fillers namely carbon nanotubes, graphene, or metallic nanoparticles, achieving efficient EMI shielding while maintaining environmental friendliness. Their lightweight, flexible nature, and mechanical robustness make them ideal for diverse applications, including wearable electronics, flexible circuits, and green electronics.

Investigating Emotional Intelligence: A Cross-Sectional Study of Pune's Postgraduate Medical Scholars.

Introduction Emotional intelligence (EI), which encompasses the ability to perceive, understand, and manage emotions, is crucial for effective human interaction. In healthcare, especially in medicine, compassion and empathy are prioritized qualities associated with enhanced patient outcomes, increased patient compliance, and overall improved healthcare experiences. This study focused on postgraduate medical students to assess their EI levels and identify influencing factors.

The Systematic Development of a Mobile Phone Delivered Text-messaging Tobacco Cessation Intervention in India.

Introduction: Tobacco consumption is a leading cause of mortality globally. Eighty percent of these deaths occur in low- and middle-income countries (LMICs). Despite this, there is a large treatment gap due to both demand and supply-side barriers.