Sumit Rana, head of research and development, discusses how the EHR giant’s system uses AI to generate progress notes, create draft responses to patient questions and assist with medical coding. And how AI sometimes can be more empathetic than a person.
This protocol for the spatiotemporal control of RNA activity uses LicV, a synthetic, photoswitchable RNA-binding protein (RBP) that can bind to a specific RNA sequence in response to blue light irradiation, and provides an efficient and generalizable strategy for engineering photoswitchable RBPs.
After five years, more than 350,000 hours of genome sequencing, and over £200 million of investment, UK Biobank is releasing the world’s largest-by-far single set of human sequencing data—completing the most ambitious project of its kind ever undertaken. The new data, whole genome sequences of its half a million participants, will certainly drive the discovery of new diagnostics, treatments, and cures. Uniquely, the data are available to approved researchers worldwide, via a protected database containing only de-identified data.
This advance lies not only in the abundance of genomic data, but its use in combination with the existing data UK Biobank has collected over the past 15 years on lifestyle, whole body imaging scans, health information, and proteins found in the blood. The Pharma Proteomics Project was published last month in Nature, in the paper, “Plasma proteomic associations with genetics and health in the UK Biobank.”
Looking forward, these data could be used to further advance efforts such as more targeted drug discovery and development, discovering thousands of disease-causing noncoding genetic variants, accelerating precision medicine, and understanding the biological underpinnings of disease.
Researchers at Tufts University and Harvard University’s Wyss Institute have created tiny biological robots that they call Anthrobots from human tracheal cells that can move across a surface and have been found to encourage the growth of neurons across a region of damage in a lab dish.
The multicellular robots, ranging in size from the width of a human hair to the point of a sharpened pencil, were made to self-assemble and shown to have a remarkable healing effect on other cells. The discovery is a starting point for the researchers’ vision to use patient-derived biobots as new therapeutic tools for regeneration, healing, and treatment of disease.
The work follows from earlier research in the laboratories of Michael Levin, Vannevar Bush Professor of Biology at Tufts University School of Arts & Sciences, and Josh Bongard at the University of Vermont in which they created multicellular biological robots from frog embryo cells called Xenobots, capable of navigating passageways, collecting material, recording information, healing themselves from injury, and even replicating for a few cycles on their own.
Regenerative medicine might just have had a new tool added to its arsenal: Scientists have created tiny biological robots out of living human cells. Though they may be small, the self-assembling bots are mighty, with a study demonstrating their potential for healing and treating disease.
The team had already proven their biological robotics chops back in 2020 with the creation of Xenobots, made from frog embryonic cells. They even managed to design Xenobots so that they could reproduce in a way that no living animal or plant does, something that had never been seen before.
The researchers weren’t sure whether the incredible capabilities of the Xenobots were in some way down to their amphibious origins, so they wanted to find out if biobots could also be created from the cells of other organisms. And why not begin with humans?
Ray Kurzweil and a host of other ambitious scientists are trying to take major next steps with AI — the revival of the dead. Within three decades, he hopes to create a ‘dad bot’ in the flesh.
Vertex Pharmaceuticals plans to sell a gene-editing treatment for sickle-cell disease. A patent on CRISPR could stand in the way.
Additionally, GAI helps radiologists cross-reference comorbidities in a way that was not possible before. For instance, people with certain types of autoimmune arthritis have an increased risk of cardiovascular disease (atherosclerosis, hypertension, and type 2 diabetes). These conditions might seem unrelated, but if a CT scan reveals calcifications in the coronary arteries, GAI can facilitate informing the radiologist and treating physician of this important biomarker. These types of added value are not just consumer conveniences. As potentiators of clinical research and effectuators of episodes of care, they can save the lives of patients.
Leaning into the whole.
It should be clear to most in the industry that AI is knocking at the door, and those who do not adopt new technology will be left behind. What seems less clear is how that design and implementation should move forward. Laying AI functions on top of already outdated systems or relying on separate solutions that do not play into the unified stack system–especially given the volume of data, delicate privacy issues and the need for constant updates–does not optimally contribute to advancement. Instead, we should embrace the vision as a whole and build for unification and GAI, rather than jury-rig a square peg in a round hole.
