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… Continue reading Chemistry42 – AI-based drug discovery platform

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… Continue reading Spleen-on-a-chip to model acute splenic sequestration in sickle cell disease

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,… Continue reading Low-cost humidity sensor to create smart diaper

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Categorized as Biosensors

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… Continue reading NeuralTree: closed-loop neuromodulation system-on-chip

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… Continue reading Machine learning model to predict heart injury post non-cardiac surgery

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… Continue reading Low-cost biosensor to detect Lead and Cadmium in sweat

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… Continue reading Deep learning model improves radiologist diagnostic performance in colon cancer screening

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… Continue reading Sybil, a machine-learning model for lung cancer risk assessment

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… Continue reading A new computational model to predict patient-specific growth of glioblastoma multiforme

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… Continue reading New Computational Equation To Better Predict Drug-Drug Interactions