Medical News Observer

Verkko: automated genome assembly tool enables complete genome sequencing for multiple species

National Institutes of Health (NIH) researchers have developed a software tool called Verkko, which can assemble complete genome sequences from various species. Verkko uses long, accurate reads and haplotype-specific markers to assemble complete, diploid genomes. The software automates the process of assembling genome sequences, making it more affordable and accessible. The researchers tested Verkko with…
Electronic patient-reported outcome (ePRO) systems reduce outpatient waiting lists

A review by the Centre for Patient-Reported Outcomes Research and the Institute of Applied Health Research at the University of Birmingham found that digital systems for patients to report symptoms remotely may reduce outpatient waiting lists. Electronic Patient-Reported Outcome (ePRO) systems allow patients to fill out questionnaires and have the results sent back to clinicians…
iTrain – video game app to teach post-stroke care

Researchers from the Kaunas University of Technology in Lithuania have developed an interactive training app called “iTrain” to support post-stroke care. The app is a course that combines a brochure, a Massive Open Online Course, visual material, and a video game to help caretakers learn how to take care of people who have had a…
Chemistry42 – AI-based drug discovery platform

Researchers from Insilico Medicine have published a paper in the Journal of Chemical Information and Modeling on the use of its AI platform, Chemistry42, for designing novel drugs for diseases. Launched in 2020, the platform includes 42 generative algorithms, customizable reward functions, and 3D physics-based modules to dynamically assess and optimize the structures. The company…
Spleen-on-a-chip to model acute splenic sequestration in sickle cell disease

A “spleen-on-a-chip” has been created by researchers from MIT, Nanyang Technological University in Singapore, the Pasteur Institute in Paris, and other institutions. The chip models how the spleen filters out old or damaged red blood cells and helps understand the phenomenon of acute splenic sequestration in sickle cell disease patients, which is made difficult by…
Low-cost humidity sensor to create smart diaper

Researchers at Penn State have developed a low-cost and easy-to-make, and highly reliable humidity sensor using hand-drawn interdigital electrodes from pencil-on-paper treated with NaCl solution. The resulting sensor is highly sensitive and can work over a wide range of relative humidity levels. The applications of the sensor include monitoring respiratory rate, characterizing human skin types,…
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…