Wearable Electronics Created Using Screen Printing

Researchers from Washington State University have developed a way to create the serpentine structures that power wearable electronics using screen printing, the same technology used to print rock concert t-shirts. The method creates a stretchable, durable circuit pattern that can be transferred to fabric and worn directly on human skin. Current commercial manufacturing of wearable… Continue reading Wearable Electronics Created Using Screen Printing

Research Suggests COVID-19 May Trigger Multiple Sclerosis

A recent study published in Scientific Reports suggests that COVID-19 may trigger Multiple Sclerosis (MS) in susceptible individuals through a process known as “molecular mimicry.” The study conducted by scientists at the National Institute of Allergy and Infectious Diseases, part of the National Institutes of Health, analyzed the structure of SARS-CoV-2 proteins and more than… Continue reading Research Suggests COVID-19 May Trigger Multiple Sclerosis

Machine learning model to predict drug side effects

Researchers developed a machine learning model to predict drug side effects that were discovered in post-marketing surveillance after clinical trials. Diego Galeano and Alberto Paccanaro utilized a geometric self-expressive model(GSEM) machine learning framework and post-marketing drug side effect data, drug chemical structure and protein targets data, and drug indications data. GSEM algorithm to predict drug… Continue reading Machine learning model to predict drug side effects

Visible app – Activity monitoring for Illness

The Visible platform helps monitor health using manual data inputs about symptoms. It also utilizes smartphone cameras to analyze heart rate variation using a technique called photoplethysmography. In the future Visible hope to provide a monthly subscription service that includes a Polar Verity Sense heart-rate monitor (HRM), which is worn on the arm to collect… Continue reading Visible app – Activity monitoring for Illness

A new computational approach to autism screening utilizing digital biomarkers shows promise

Researchers at the University of Chicago have developed a novel computational approach that can reliably predict an eventual diagnosis of autism spectrum disorder (ASD) in young children, without the need for additional blood work or procedures, using only diagnostic codes from past doctor’s visits. The new approach reportedly reduces the number of false-positive ASD diagnoses… Continue reading A new computational approach to autism screening utilizing digital biomarkers shows promise

VarSAn – Computational tool to identify disease pathways

Researchers at the University of Illinois Urbana-Champaign have developed a new computational tool to identify pathways related to diseases, including breast and prostate cancer, using single-nucleotide polymorphisms (SNPs). The tool, called VarSAn (Variant Set Annotator, pronounced ‘version’), uses SNPs that have been identified by sequencing studies as being disease-related, to predict which pathways may be… Continue reading VarSAn – Computational tool to identify disease pathways

Virtual reality avatar to help physiotherapy at home

Researchers from the University of Warwick showed that virtual reality (VR) combined with 3D Motion capture could allow movements to be translated onto an avatar that the patient can follow. They accomplished this using consumer VR technologies currently available. Research showed that these consumer virtual reality technologies could be used for both providing guidance to… Continue reading Virtual reality avatar to help physiotherapy at home

Artificial Intelligence app to predict meningioma survival

Researchers recently published a study that shows proof of concept for how artificial intelligence can help doctors and brain tumor patients predicting survival and help make better treatment decisions. This study is published in Nature partner journal Digital Medicine. They also developed an open-source smartphone app meningioma.app to allow clinicians and other researchers to interactively… Continue reading Artificial Intelligence app to predict meningioma survival

New strategy to reduce antibiotic resistance using an extracellular polymeric substance inhibitor

Researchers from KU Leuven in Belgium have developed a new antibacterial strategy that weakens bacteria by preventing them from cooperating. The researchers showed that blocking slime (extracellular polymeric substance) production of salmonella bacteria weakens the bacterial community, making it easier to remove. They used a chemical, antibacterial substance 2-cyclopentenyl-5-(4-chlorophenyl)-2-aminoimidazole, a specific member of the class… Continue reading New strategy to reduce antibiotic resistance using an extracellular polymeric substance inhibitor

Stimulated Raman Histology and Machine learning provides an alternative approach in brain tumor diagnosis

The study published in Nature Medicine examined the diagnostic accuracy of brain tumor image classification through artificial intelligence tool, compared with the accuracy of pathologist interpretation of conventional histologic images. The results for both methods were comparable: the artificial intelligence based diagnosis was 94.6% accurate, compared with 93.9% for the traditional pathologist-based assessment and interpretation.… Continue reading Stimulated Raman Histology and Machine learning provides an alternative approach in brain tumor diagnosis