Medical News Observer

Biodegradable Implantable Power System with Wireless Charging Capabilities using Zn-ion Hybrid Supercapacitors

Researchers have developed a new soft implantable power system for health devices that combines wireless energy transfer and storage. They were created by utilizing biodegradable Zn-ion hybrid supercapacitors that use molybdenum sulfide (MoS2) nanosheets as cathode, ion-crosslinked alginate gel as electrolyte, and zinc foil as anode for high-performance energy storage. This technology could revolutionize implantable…
Advanced Ingestible Device for Internal Monitoring of Vital Signs

Scientists from MIT, Celero Systems, and West Virginia University have developed an ingestible device capable of monitoring vital signs from within the human body. This device, about the size of a multivitamin, uses an accelerometer to monitor breathing and heart rate, offering a more accessible and convenient approach to patient care. Unlike implantable ones like…
USC Study Reveals Two Metabolites Enhance Prediabetes Prediction in Latino Youth Beyond Traditional Risk Factors

Researchers from the Keck School of Medicine of the University of Southern California, funded by the National Institutes of Health, conducted a study focusing on prediabetes in young Latino people. The study found that adding two specific metabolites—allylphenol sulfate and caprylic acid—to existing prediction models significantly improved their ability to predict prediabetes in children beyond…
Revolutionizing Regenerative Medicine: 3D Bioprinting of Hair Follicles in Engineered Skin Tissues

The research by scientists at Rensselaer Polytechnic Institute marks a significant breakthrough in tissue engineering, as they 3D-printed hair follicles within human skin tissue cultured in a lab. The study reports the successful use of 3D bioprinting to integrate hair follicles into engineered skin tissues. This was achieved by printing spheroids formed from dermal papilla…
Using Epigenetic Factors UCLA Cancer Researchers Develop AI Model to Predict Patient Outcomes in Multiple Cancer Types

Researchers at UCLA’s Jonsson Comprehensive Cancer Center developed an AI model that predicts patient outcomes in various cancer types using epigenetic factors (epifactors). This model categorizes cancers into distinct groups based on the gene expression patterns of these factors, which influence gene activation or deactivation. This method is more effective than traditional grading or epithelial-to-mesenchymal…
Uppsala University Develops CVD-21: A New Instrument for Enhanced Cardiovascular Disease Risk Assessment

Researchers at Uppsala University present the development of the CVD-21 tool, a decision-support instrument for cardiovascular disease (CVD) treatment. The instrument’s development involved analyzing 368 proteins in blood samples from over 10,000 patients in international studies on new CVD and atrial fibrillation treatments. The researchers identified 21 circulating proteins (the CVD-21 panel) from these analyses…
ANDI – Automated Radiological Image Processing Software

ANDI is an advanced quantitative imaging software designed to enhance the evaluation of medical images, particularly in neuroimaging. Its core function revolves around processing diffusion-weighted images to map the microstructural properties of white matter in the brain. ANDI employs a combination of local (per-voxel basis) and global (whole brain) reconstruction algorithms, including modeling, tractography, and…
KAIST Develops Temperature-Responsive P-CARE Intra Venous Needle

A research team led by Professor Jae-Woong Jeong from KAIST‘s School of Electrical Engineering, in collaboration with Professor Won-Il Jeong’s team, developed a novel intravenous (IV) needle technology called Phase-Convertible, Adapting and non-REusable (P-CARE) needle. The P-CARE needle is designed to reduce patient discomfort and safeguard medical staff. The needle is made of liquid metal…
CCT5 and ELF1: Key Biomarkers for Predicting Neoadjuvant Chemoradiotherapy Response and Prognosis in Locally Advanced Rectal Cancer

Researchers identified CCT5 and ELF1 as significant biomarkers for determining treatment response and prognosis in locally advanced rectal cancer (LARC) patients undergoing neoadjuvant chemoradiotherapy (NCRT). Their study showed that high expression levels of these genes were associated with resistance to NCRT and poorer prognosis. By analyzing gene expression and immune cell infiltration, the research developed…
AI-Powered Abdominal CT Analysis Predicts Fall Risk

Researchers evaluate the potential of automated artificial intelligence (AI)-based body composition algorithms to predict future fall risks in patients. Their study used automated abdominal CT-based measures of muscle, fat, and bone to provide a novel method for future fall risk stratification, particularly in identifying patients with osteosarcopenic obesity. The retrospective case-control study involved 9,029 patients…