The key to that is a sharper focus on patient outcomes – measuring what works, and doesn't work, in treating patients from one clinic, region and member-state to another. For that, we need more experts – in industry, policy and academia – who help this effort grow across the EU, from one capital to another. Stakeholder groups need mobilising, and interconnecting, across Europe. The old silos in healthcare across Europe need to be dismantled.
An anonymous reader quotes a report from Science Business: Chinese researchers have developed an artificial intelligence system which can diagnose cancerous prostate samples as accurately as any pathologist, holding out the possibility of streamlining and eliminating variation in the process of cancer diagnosis. The system may also help overcome shortages of trained pathologists and in the longer term lead to automated or partially-automated prostate diagnosis. Confirmation of a prostate cancer diagnosis normally requires a biopsy sample to be examined by a pathologist. Now the Chinese AI system has shown similar levels of accuracy to pathologists and can also accurately classify the level of malignancy of the cancer, eliminating the variability which can creep into human diagnoses. The pathology images were subdivided into 40,000 smaller samples of which 30,000 were used to train the software while the remaining 10,000 were used to test accuracy.
Procept BioRobotics, a Silicon Valley-based surgical robotics company, recently raised nearly $120 million in private equity to commercialize a treatment for a prevalent prostate condition known as benign prostatic hyperplasia (BPH). BPH, also known as enlarged prostate, affects around half of men age 60 or older and 90 percent of men age 85 or older. Founded in 1999, Procept has pioneered the first commercially available autonomous tissue removal robot to treat BPH. The company's AQUABEAM system uses autonomous robotics and advanced imaging to deliver a heat-free waterjet that removes enlarged prostate tissue. The company has presented clinical research suggesting its method carries less risk of side effects than the current surgical gold-standard, known as TURP.
This PhD studentship is funded for three years by the Big C Charity. Funding comprises Home/EU fees, an annual stipend (£14,553 for 2017 entry – this increases each year in line with the GDP deflator) and £1000 per annum to support research training. Analysis of the genetic phylogeny of multifocal prostate cancer identifies multiple independent clonal expansions in neoplastic and morphologically normal prostate tissue.
"Let's say the urologist ordered a prostate specific antigen (PSA) test and advised the internist to have the patient follow up in three months," he said. "Maybe we called the patient twice; he missed an appointment; he doesn't return our messages. Or maybe I thought the urologist was following up, and he thought I was doing it, and it turns out cancer is developing in the patient and he doesn't know it."
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. "For this kind of test you want the sensitivity to be as high as possible because you don't want to miss a single cancer that should be treated."
An online pharmacy is planning to use drones to deliver the morning-after pill and Viagra following successful UK trials. MedExpress is in talks with the independent regulator for pharmacy services to dispatch medicines and the contraceptive nationwide. The company says the service will be particularly useful for people living in remote areas. They have offered assurances that the products will be delivered discreetly with purchases details blacked out on sales records and bank accounts. One challenge MedExpress faces is delivering medication safely and at temperatures that do not interfere with the drugs' effectiveness.
Andrew Hopkins' route into the world of science has a familiar start – an inspirational chemistry teacher at school. Less predictably, he made a career in pharmaceuticals after getting his first taste of industrial chemistry in a steelworks. Hopkins grew up in Neath, South Wales, and his first summer job was in the labs at the British Steel plant in nearby Port Talbot. 'By then I'd already got the chemistry bug,' he says. 'What I loved about chemistry is that it is the "liberal science" that allows you to go in many different directions, and it seemed like a natural choice to follow my passion.'
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).