Medical News Observer

NeuralTree: closed-loop neuromodulation system-on-chip

Researchers from École Polytechnique Fédérale de Lausanne have developed NeuralTree, a closed-loop neuromodulation system-on-chip that detects and alleviates disease symptoms. It uses a 256-channel high-resolution sensing array and an energy-efficient machine-learning processor to extract and classify biomarkers for accuracy in symptom prediction. It uses data from real patients’ EEG and iEEG epilepsy datasets, as well…
Machine learning model to predict heart injury post non-cardiac surgery

A prediction model for Myocardial injury after non-cardiac surgery (MINS) was developed by researchers from South Korea using machine learning techniques with the extreme gradient boosting algorithm. The study analyzed 6811 patients who underwent non-cardiac surgery between January 2010 and June 2019. The top 12 variables affecting MINS were preoperative cardiac troponin level, inotropic drug…
Low-cost biosensor to detect Lead and Cadmium in sweat

Researchers at the University of São Paulo in Brazil have developed a portable, flexible copper sensor to detect heavy metals such as lead and cadmium in sweat. High levels of heavy metals in the body can lead to serious health problems. The sensor is made of simple, cost-effective materials, including flexible conductive copper tape, an…
Deep learning model improves radiologist diagnostic performance in colon cancer screening

A study by the Technical University of Munich researchers evaluated the use of a deep learning algorithm to differentiate between colon cancer and acute diverticulitis on CT images and its impact on radiologists’ performance. The 3-D convolutional neural network reached a sensitivity of 83.3% and specificity of 86.6% compared to the average reader sensitivity of…
Sybil, a machine-learning model for lung cancer risk assessment

Researchers at MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health, Mass General Cancer Center, and Chang Gung Memorial Hospital have developed an artificial intelligence tool named Sybil for lung cancer risk assessment. Sybil analyzes low-dose computed tomography (LDCT) image data without using clinical or demographic data to predict a patient’s risk of developing…
A new computational model to predict patient-specific growth of glioblastoma multiforme

Researchers at the University of Waterloo and the University of Toronto have developed a new computational model to predict the growth of glioblastoma multiforme (GBM) more accurately. The proliferation-invasion (PI) model is a mathematical model commonly used to describe the growth of glioblastoma multiforme (GBM). It relies on known values of two key parameters, the…
New Computational Equation To Better Predict Drug-Drug Interactions

A joint research team of mathematicians and pharmacological scientists has identified the major causes of inaccuracies in the Food and Drug Administration’s (FDA) equation for predicting drug-drug interactions and presented solutions. They found that the FDA’s equation, based on the 110-year-old Michaelis-Menten (MM) model, was only accurate 38% of the time. The MM model is…
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…
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…
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…