Oran Knowlson, a British teenager with a severe type of epilepsy called Lennox-Gastaut syndrome, became the first person in the world to trial a new brain implant last October, with phenomenal results – his daytime seizures were reduced by 80%. "It's had a huge impact on his life and has prevented him from having the falls and injuring himself that he was having before," says Martin Tisdall, a consultant paediatric neurosurgeon at Great Ormond Street Hospital (Gosh) in London, who implanted the device. "His mother was talking about how he's had such a improvement in his quality of life, but also in his cognition: he's more alert and more engaged." Oran's neurostimulator sits under the skull and sends constant electrical signals deep into his brain with the aim of blocking abnormal impulses that trigger seizures. The implant, called a Picostim and about the size of a mobile phone battery, is recharged via headphones and operates differently between day and night. "The device has the ability to record from the brain, to measure brain activity, and that allows us to think about ways in which we could use that information to improve the efficacy of the stimulation that the kids are getting," says Tisdall. "What we really want to do is to deliver this treatment on the NHS."

Scientists have developed the world's first mini brains in a breakthrough they hope could revolutionize neuroscience. The 3D organs - which are about the size of a grain of rice - were grown in a lab using human fetal brain tissue from healthy abortion material. Scientists at the Princess Máxima Center for Pediatric Oncology in the Netherlands aimed to grow a a brain in early-mid development and so used the brain tissue from aborted fetus' that were in the gestational period of weeks 12 through 15. The team was surprised to find that the fetal brain tissue was vital to growing a mini brain. Until now, when growing other mini organs, scientists would break down original tissues into single cells by using embryonic or pluripotent stem cells to grow and replicate specific areas of the brain.

Summary: Many brain cancers are very difficult to treat, but research now provides greater insight into how they grow. Researchers at the University of Waterloo have created a computational model to predict the growth of deadly brain tumours more accurately. Glioblastoma multiforme (GBM) is a brain cancer with an average survival rate of only one year. It is difficult to treat due to its extremely dense core, rapid growth, and location in the brain. Estimating these tumours' diffusivity and proliferation rate is useful for clinicians, but that information is hard to predict for an individual patient quickly and accurately.

Scientists have used artificial intelligence (AI) to create a drug regime for children with a type of deadly brain cancer, where survival rates have not improved for 50 years. Diffuse intrinsic pontine glioma (DIPG) is a rare and fast-growing type of brain tumour in children. These types of tumours are difficult to remove surgically because they are diffuse, which means they do not have well-defined borders suitable for operations. A quarter of children with DIPG have a mutation in a gene known as ACVR1, but there are currently no treatments approved to target this mutation. In a new study, scientists at the Institute of Cancer Research, London (ICR), and the Royal Marsden NHS Foundation Trust were able to use AI to discover that combining the drug everolimus with another called vandetanib could enhance vandetanib's capacity to pass through the blood-brain barrier in order to treat the cancer.

Scientists have successfully used artificial intelligence to create a new drug regime for children with a deadly form of brain cancer that has not seen survival rates improve for more than half a century. The breakthrough, revealed in the journal Cancer Discovery, is set to usher in an "exciting" new era where AI can be harnessed to invent and develop new treatments for all types of cancer, experts say. "The use of AI promises to have a transformative effect on drug discovery," said Prof Kristian Helin, chief executive of The Institute of Cancer Research (ICR), London, where a team of scientists, doctors and data analysts made the discovery. "In this study, use of AI has identified a drug combination which appears to have promise as a future treatment for some children with incurable brain cancer. It's exciting to think that it could become one of the first examples of a treatment proposed by AI going on to benefit patients."

Digital molecule designer Insilico Medicine has launched a preclinical research program focused on finding new treatments for brain cancer, and has brought on the former global program head of GlaxoSmithKline's computer-aided drug discovery unit to help run it. George Okafo will join Insilico as an entrepreneur-in-residence, as the company looks to wield its artificial intelligence networks and generative engines to uncover novel targets and molecules for glioblastoma multiforme, and potentially spin out the findings into a new business. "Brain cancers are the worst diseases anyone can ever get and the most rare cancers are often overlooked because the efforts are expensive, the market is small, and the probability of failure is high," Insilico co-founder and CEO Alex Zhavoronkov said in a statement. "We will try to change this with our AI-powered drug discovery pipeline." "We needed to have a seasoned big pharma veteran to drive this process to the point where it can be quickly developed and externalized," Zhavoronkov added, saying the company plans to use its AI to generate and evaluate several leads over the coming months, before hopefully setting the whole project off on its own by the end of August.

The AI analyses high-resolution images of tumours produced using a method called stimulated Raman histology (SRH). Todd Hollon at the University of Michigan and his colleagues generated more than 2 million SRH images of brain tumours from 415 people with known diagnoses. Each image showed a small region of an excised tumour and was labelled with which type of brain tumour it was out of the 10 most common types. The team fed them all to the AI so it could learn from the images to identify tissue features linked to these specific types of cancer. The images had either come from biopsies that remove a small sample of a suspected tumour for analysis or from surgeries to remove tumours.

Imagine being able to know the probability of whether a persistent headache that you are experiencing is a symptom of something much worse through a simple blood test. Researchers affiliated with ClinSpec Diagnostics Limited, a spin-off from the University of Strathclyde in Glasgow, Scotland, and their colleagues developed patented technology that can detect brain cancer from blood samples. Using an innovative combination of artificial intelligence (AI) and spectroscopy, the U.K. researchers developed a method to detect brain cancer from a blood biopsy, and published their study on October 8, 2019 in Nature Communications. Headaches are one of the most common symptoms of brain tumors, according to the American Brain Tumor Association. But while headaches are very common, brain cancer is not.

Imagine being able to know the probability of whether a persistent headache that you are experiencing is a symptom of something much worse through a simple blood test. Researchers affiliated with ClinSpec Diagnostics Limited, a spin-off from the University of Strathclyde in Glasgow, Scotland, and their colleagues developed patented technology that can detect brain cancer from a blood samples. Using an innovative combination of artificial intelligence (AI) and spectroscopy, the U.K. researchers developed a method to detect brain cancer from a blood biopsy, and published their study on October 8, 2019 in Nature Communications. Headaches are one of the most common symptoms of brain tumors, according to the American Brain Tumor Association. But while headaches are very common, brain cancer is not.

AlphaGo became the first household AI name by teaching itself to play the ancient Chinese game Go and then beating the world's best human player. Self-driving cars use AI systems to learn to park or merge into traffic by practicing the maneuvers over and over until they get it right. It's clear that AI programs are good at training themselves to win, maximize, or perfect. But what if success means striking a balance? In cancer treatments, doctors endeavor to dose patients with enough drugs to kill as many tumor cells as possible but as few patient cells as possible.