One of the boldest undertakings that NIH has ever attempted, the All of Us Research Program has been hard at work in a “beta” testing phase, and is now busy gearing up for full recruitment in the spring. This historic effort will enroll 1 million or more people in the United States to share information about their health, habits, and what it’s like where they live. This information will be part of a resource that scientists can use to accelerate research and improve health. How? By taking into account individual differences in lifestyle, environment, and biology, researchers will uncover paths toward realizing the full potential of precision medicine.
Before embarking on this adventure, All of Us is reaching out to prospective researchers, community organizations, and citizen scientists—including people just like you—to get their input. Imagine that the project has already enrolled 1 million participants from all over the country and from diverse backgrounds. Imagine that they have all agreed to make available their electronic health records, to put on wearable sensors that can track body physiology and environmental exposures, and to provide blood samples for lab testing, including DNA analysis. Is there a particular research question that you think All of Us could help answer? Possible topics include risks of disease, factors that promote wellness, and research on human behavior, prevention, exercise, genetics, environmental health effects, health disparities, and more. To submit an idea, just go to this special All of Us web page.
Credit: Amy Robinson, Alex Norton, William Silversmith, Jinseop Kim, Kisuk Lee, Aleks Zlasteski, Matt Green, Matthew Balkam, Rachel Prentki, Marissa Sorek, Celia David, Devon Jones, and Doug Bland, Massachusetts Institute of Technology, Cambridge, MA; Sebastian Seung, Princeton University, Princeton, NJ
This eerie scene might bring back memories of the computer-generated alien war machines from Steven Spielberg’s War of the Worlds thriller. But what you’re seeing is a computer-generated depiction of a quite different world—the world inside the retina, the light-sensitive tissue that lines the back of the eye. The stilt-legged “creatures” are actually ganglion nerve cells, and what appears to be their long “noses” are fibers that will eventually converge to form the optic nerve that relays visual signals to the brain. The dense, multi-colored mat near the bottom of the image is a region where the ganglia and other types of retinal cells interact to convey visual information.
What I find particularly interesting about this image is that it was produced through the joint efforts of people who played EyeWire, an internet crowdsourcing game developed in the lab of computational neuroscientist Sebastian Seung, now at Princeton University in New Jersey. Seung and his colleagues created EyeWire using a series of high-resolution microscopic images of the mouse retina, which were digitized into 3D cubes containing dense skeins of branching nerve fibers. It’s at this point where the crowdsourcing came in. Online gamers—most of whom aren’t scientists— volunteered for a challenge that involved mapping the 3D structure of individual nerve cells within these 3D cubes. Players literally colored-in the interiors of the cells and progressively traced their long extensions across the image to distinguish them from their neighbors. Sounds easy, but the branches are exceedingly thin and difficult to follow.
For millions of people with epilepsy, life comes with too many restrictions. If they just had a reliable way to predict when their next seizure will come, they could have a chance at leading more independent and productive lives.
That’s why it is so encouraging to hear that researchers have developed a new algorithm that can predict the onset of a seizure correctly 82 percent of the time. Until recently, the best algorithm was hardly better than flipping a coin, leading some to speculate that seizures are random neurological events that can’t be predicted at all. But the latest leap forward shows that seizures certainly can be predicted, and our research efforts are headed in the right direction to make them even more predictable. The other big news is how this new algorithm was developed: it’s the product of a crowdsourcing competition.
Developing a drug takes time and money: on the average, around 14 years and $2 billion or more. More than 95 percent of the drugs fail during development. Even those that go all the way to large and expensive clinical trials in humans frequently don’t make the cut—perhaps because they weren’t quite as effective as they were supposed to be, had undesirable side effects, or didn’t align with the developer’s business priorities. But some of these compounds may have surprising therapeutic properties that have not yet been fully exploited. It would be a wasted opportunity not to take another look at them and test them for effectiveness in other conditions.
For that reason, our National Center for Advancing Translational Sciences (NCATS), with financial support from the NIH Common Fund, launched a pilot program to discover new therapeutic uses for existing molecules. Today we are awarding $12.7 million to nine academic institutions to reexamine a collection of compounds developed by major pharmaceutical companies and test them as treatments for diseases, both common and rare: from alcoholism and Alzheimer’s disease to Duchenne muscular dystrophy and schizophrenia. Continue reading →