University of Alberta scientists developed a deep learning-based prostate cancer diagnostic platform that only uses a single drop of blood which will allow men to bypass the current painful biopsy methods. Using a GTX 1060 GPU, CUDA and the MathWorks Neural Network Toolbox, the scientists' trained their model on information from millions of cancer cell nanoparticles in the blood to recognize the unique fingerprint of aggressive prostate cancer. To test their method, they evaluated a group of 377 men who were referred to their urologist with suspected prostate cancer and found that their system called Extracellular Vesicle Fingerprint Predictive Score (EV-FPS) correctly identified men with aggressive prostate cancer 40 percent more accurately than the most common test in wide use today. "Higher sensitivity means that our test will miss fewer aggressive cancers," said John Lewis, the Alberta Cancer Foundation's Frank and Carla Sojonky Chair of Prostate Cancer Research at the University of Alberta.
An online pharmacy is planning to use drones to deliver the morning-after pill and Viagra following successful UK trials. But in the trial, the emergency contraception pill was carried at below 25C by a drone and successfully delivered in Broadstairs, Kent. We're considering making drone delivery part of our future service and are in talks to work out how we can do this. A high street retailer has become the first to launch a generic emergency hormonal contraceptive pill (EHC) at half the price of branded versions – prompting concerns it could result in a rise in ectopic pregnancies and STDs.
'The idea of understanding a disease from an evolutionary viewpoint to inform drug design still resonates today in how Exscientia is approaching the design of anticancer agents. 'I spent a season at the GlaxoWellcome labs in Stevenage making the compounds I'd designed, and vividly remember the excitement of discovering the first molecule we'd made was active.' These included topics such as the druggable genome, ligand efficiency and network pharmacology – all of which are familiar topics to drug discovery chemists today. An early success involved feeding historical data for the project that discovered erectile dysfunction drug tadalafil (Cialis) into the evolutionary drug design model.
Among the chance discoveries that have been honored with the prestigious prize are X-rays (physics, 1901), penicillin (medicine, 1945), fullerenes that paved the way for nanotechnology (chemistry, 1996), conductive polymers (chemistry, 2000), and the bacteria responsible for ulcers (medicine, 2005). He was rewarded with the first Nobel physics prize awarded in 1901. Positive serendipity (Roentgen finds something he is not looking for, and confirms it through further study). Negative serendipity (Columbus finds something he is not looking for .
Most biological network inference methods focus on the definition of gene regulatory networks, in which edges represent direct regulatory interactions between genes [2–4]. Two approaches to functional network inference: one based on the expression profile similarity and the other based on the extraction of knowledge from machine learning models. The specific focus of this paper is the network inference from rule-based machine learning models, these have been successfully applied before to extract knowledge from genetic data  and identify disease risk factors in a bladder cancer study . To address these questions, we propose in this article a new network inference protocol, called FuNeL (Functional Network Learning).