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

Web application to help discover new antibiotics to fight gram negative bacteria

A new web app eNTRyway speeds the discovery of drugs to kill Gram-negative bacteria by quickly evaluating potential drugs ability to accumulate in these bacterial cell. Entryway calculates physiochemical properties of molecules and compares to a training set of compounds. The tool also offers insights into discrete chemical changes that can convert drugs that kill… Continue reading Web application to help discover new antibiotics to fight gram negative bacteria

Machine learning algorithm to predict mortality in heart failure patients

Researchers from the University of California San Diego developed a machine-learning model by training a boosted decision tree algorithm on de-identified electronic health records data of 5,822 hospitalized or ambulatory patients with heart failure from the University of California San Diego. This machine learning model is based on eight readily available variables. These include, diastolic… Continue reading Machine learning algorithm to predict mortality in heart failure patients

Bacteriophage treatment for alcohol related liver disease

Researchers from King’s College London and the University of California San Diego School of Medicine conducted an animal study in mice and shown promise of bacteriophage therapy in treating alcohol-related liver disease. This research is published in journal Nature. The team discovered that patients with severe alcoholic hepatitis had high numbers of a destructive gut… Continue reading Bacteriophage treatment for alcohol related liver disease

Calculator to predict five year risk for chronic kidney disease

A new risk calculator tool to predict the risk of chronic kidney disease is developed by Chronic Kidney Disease Prognosis Consortium, a large global collaboration led by researchers at the Johns Hopkins Bloomberg School of Public Health. It utilizes a mix of variables to predict accurately whether someone is likely to develop chronic kidney disease… Continue reading Calculator to predict five year risk for chronic kidney disease