Researchers have found that genetically and pharmacologically restoring the normal activity of the brain circuit improved anorexia, opening the possibility of developing a treatment strategy for affected individuals in the future. Researchers at Baylor College of Medicine, Louisiana State University and collaborating institutions has discovered that abnormal activity in a particular brain circuit underlies anorexia in an animal model of the condition. Genetically and pharmacologically restoring the normal activity of the brain circuit improved the condition, opening the possibility of developing a treatment strategy for affected individuals in the future. Anorexia has no approved treatment, and the underlying causes is unclear. The study was recently published in Nature Neuroscience.

To better appreciate how a complex organ such as the brain functions, scientists strive to accurately understand both its detailed cellular architecture and the intercellular communications taking place within it.

Summary: Blood tests revealed specific epigenetic biomarkers for schizophrenia. Researchers applied machine learning to analyze the CoRSIVs region of the human genome to identify the schizophrenia biomarkers. Testing the model with an independent data set revealed the AI technology can detect schizophrenia with 80% accuracy. An innovative strategy that analyzes a region of the genome offers the possibility of early diagnosis of schizophrenia, reports a team led by researchers at Baylor College of Medicine. The strategy applied a machine learning algorithm called SPLS-DA to analyze specific regions of the human genome called CoRSIVs, hoping to reveal epigenetic markers for the condition.

CHICAGO: New applications of artificial intelligence (AI) in health care settings have shown early success in improving survival and outcomes in traffic accident victims transported by ambulance and in predicting survival after liver transplantation, according to two research studies presented at the virtual American College of Surgeons Clinical Congress 2020. Both studies evaluated how AI can crunch massive amounts of data to support decision-making by surgeons and other care providers at the point of care. In one study, researchers at the University of Minnesota applied a previously published AI approach known as natural language processing (NLP)1 to categorize treatment needs and medical interventions for 22,529 motor vehicle crash patients that emergency medical service (EMS) personnel transported to ACS-verified Level I trauma centers in Minnesota. According to a 2016 study by the National Academies of Sciences, Engineering, and Medicine, 20 percent of medical injury deaths are potentially preventable2 representing a quality gap the researchers sought to address. Reviewing the performance of EMS teams to profile potentially preventable deaths can enable quality improvement efforts to reduce these deaths.

A disease capable of decimating and killing those affected, cancer involves cells in a specific part of the body growing and reproducing uncontrollably in a process known as proliferation. In a recent breakthrough, the tumor microenvironment (TME) has been established as a key driver for cancer progression, promoting resistance to therapeutics all the while enabling the disease to evade the immune system. Specifically, myeloid-derived suppressor cells (MDSCs) have been shown to play a central role in maintaining the TME through the suppression of host immunity, the establishment of new vasculature, and the remodeling of connective tissue to support tumor growth. Therefore, it is imperative to develop cancer immunotherapies able to promote the anti-oncological activity of the immune system with the dual ability to combat the highly detrimental effects of the TME. However, while it is straightforward to assess the effect of new therapies on cancer cells, estimating the effectiveness of these novel therapies on the TME is challenging.

Here are the stories on Living Lab Radio for August 25 and 26, 2019. An American convicted of a federal crime is seven percent more likely to be sentenced to jail time if they are black than if they are white. That jail time is likely to be eight months longer if the person is black. That's a major disparity, but it's a major improvement over where we were twenty years ago. That's the conclusion of a new analysis presented at the American Sociological Association earlier this month.

Of all the fast and powerful computers in the world, our brain remains by far the most impressive. Now an interdisciplinary team of scientists, led by Baylor College of Medicine, aims to reveal the computational building blocks of our brain and use them to create smarter learning machines. To enable this ambitious project, the U.S. government's Intelligence Advanced Research Projects Activity(IARPA) has awarded a 21 million contract to an interdisciplinary team of neuroscientists, computer scientists, physicists and mathematicians, led by principal investigator Dr. Andreas Tolias, associate professor of neuroscience at Baylor. The research team includes scientists from Baylor, the California Institute of Technology, Columbia University, Cornell University, Rice University, the University of Toronto and the University of Tuebingen. The program supporting this research is known as Machine Intelligence from Cortical Networks (MICrONS) and was envisioned and organized by Jacob Vogelstein, a neuromorphic engineer and program manager with IARPA.

Neuroscientists have constructed a network map of connections between cortical neurons, traced from a 100 terabytes 3D data set. The data were created by an electron microscope in nanoscopic detail, allowing every one of the "wires" to be seen, along with their connections. Some of the neurons are color-coded according to their activity patterns in the living brain. The largest network of the connections between neurons in the cortex to date has been published by an international team of researchers from the Allen Institute for Brain Science, Harvard Medical School, and Neuro-Electronics Research Flanders (NERF). In the process of their study*, the researchers developed new tools that will be useful for "reverse engineering the brain by discovering relationships between circuit wiring and neuronal and network computations," says Wei-Chung Lee, Ph.D., Instructor in Neurobiology at Harvard Medicine School and lead author of a paper published this week in the journal Nature.