Stanford bioengineer Mark Skylar-Scott on his “science fiction” quest to 3D print human hearts and other organs on demand, using cells from a patient’s own body.
Decentralized yet coordinated networks of specialized artificial intelligence agents, multi-agent systems for healthcare (MASH), that excel in performing tasks in an assistive or autonomous manner within specific clinical and operational domains are likely to become the next paradigm in medical artificial intelligence.
Recent studies have shown that mechanical properties such as extracellular matrix stiffness, fluid flow, weight loading, compression, and stretching can affect cellular functions. Some examples of cell responses to mechanical properties could be the migration of cancer cells from rigid to soft surfaces or the differentiation of fibroblasts into myofibroblasts. Cellular responses to mechanical changes can modify the insertion of proteins in the extracellular matrix (ECM), causing an increase in tissue stiffness with functional consequences. In general, mechanical and physical factors can affect any kind of cell phenotype in culture conditions and in vivo tissues. Cells sense mechanical stimuli by applying force and restructuring their shape and functions in response to the resistance of the stimuli. Furthermore, mechanical triggers can develop a “memory” for altering cellular plasticity and adaptation. This phenomenon is called cellular mechanical memory (CMM), a singular feature of mesenchymal stem cells (MSCs). Controlled targeting of CMM may resolve the scarcity of viable cells needed for cell based therapy (CBT) and implement studies concerning cancer research, fibrosis, and senescence. This review focusses on cells from the mesodermal lineage, such as MSCs, fibroblasts and chondrocytes, and the role of CMM as a potential target for CBT.
A new model of Alzheimer’s disease has been proposed, which could speed up efforts to understand and cure the complex condition – while bringing all manifestations of the condition under one unifying theory.
Researchers from Arizona State University suggest that stress granules – protein and RNA clumps that form around cells in stressful conditions due to genetic and environmental risk factors – are the primary culprit behind the disease.
In their new study, the team reviewed data from multiple health databases and past papers – particularly a 2022 study on Alzheimer’s progression – to identify widespread changes in gene expression that come with it.
The new DGX machines are portable but powerful enough to drive complex AI modules and research, with processing capabilities previously only available in data centers.
Join aerospace engineer Mike DiVerde as he explores the fascinating world of robotic spacecraft through NASA’s remarkable Chandra X-Ray Observatory. Discover…
Fast-charging lithium-ion batteries are ubiquitous, powering everything from cellphones and laptops to electric vehicles. They’re also notorious for overheating or catching fire.
Now, with an innovative computational model, a University of Wisconsin–Madison mechanical engineer has gained new understanding of a phenomenon that causes lithium-ion batteries to fail.
Developed by Weiyu Li, an assistant professor of mechanical engineering at UW–Madison, the model explains lithium plating, in which fast charging triggers metallic lithium to build up on the surface of a battery’s anode, causing the battery to degrade faster or catch fire.
A chemical reaction that’s vital to a range of commercial and industrial goods may soon be initiated more effectively and less expensively thanks to a collaboration that included Oregon State University College of Engineering researchers.
The study, published in Nature, involves hydrogenation —adding the diatomic hydrogen molecule, H2, to other compounds.
“Hydrogenation is a critical and diverse reaction used to create food products, fuels, commodity chemicals and pharmaceuticals,” said Zhenxing Feng, associate professor of chemical engineering. “However, for the reaction to be economically viable, a catalyst such as palladium or platinum is invariably required to increase its reaction rate and thus lower cost.”
A trio of AI researchers at Google’s Google DeepMind, working with a colleague from the University of Toronto, report that the AI algorithm Dreamer can learn to self-improve by mastering Minecraft in a short amount of time. In their study published in the journal Nature, Danijar Hafner, Jurgis Pasukonis, Timothy Lillicrap and Jimmy Ba programmed the AI app to play Minecraft without being trained and to achieve an expert level in just nine days.
Over the past several years, computer scientists have learned a lot about how deep learning can be used to train AI applications to conduct seemingly intelligent activities such as answering questions. Researchers have also found that AI apps can be trained to play games and perform better than humans. That research has extended into video game playing, which may seem to be redundant, because what could you get from a computer playing another computer?
In this new study, the researchers found that it can produce advances such as helping an AI app learn to improve its abilities over a short period of time, which could give robots the tools they need to perform well in the real world.
