A new study investigating the effect of thirdhand smoke (THS) in a mouse model system specially designed to mimic the genetic diversity of human populations has shed new light on how genetic predispositions contribute to an individual’s cancer risk.
Machine Learning Helps Link Chemical Exposure and Obesity
Scientists at Berkeley Lab and their collaborators developed a machine learning technique to discover obesity-related mixed chemical exposure patterns associated with environmental health risk in the general U.S. population. To assess this, they used indicators like body mass index and waist circumference.
Researchers at Berkeley Lab Advance Cancer Research Using Artificial Intelligence
BSE Researchers recently published two studies that will help oncologists more precisely understand the state of their patients’ disease or their risk for cancer relapse. As with many diseases, cancer can be challenging to predict and in some cases, impossible to treat. This work, however, is pushing the boundaries of how science and artificial intelligence (AI) can be used to better understand the risks and outcomes of cancer in human health.
A Laser-powered Upgrade to Cancer Treatment
Researchers in the Biological Systems and Engineering (BSE) Division are collaborating with colleagues at the Berkeley Lab Laser Accelerator (BELLA) Center to adapt the nascent technology of laser-driven ion accelerators to make a more effective type of radiation more readily available to patients. The mutually beneficial partnership gives BELLA scientists a real-world application around which to refine their experimental laser platform, and gives the biologists a chance to test how living tissue responds to laser-driven proton beams at FLASH dose rates.
Transferring Discoveries from Mouse Model to Humans
Scientists at Berkeley Lab are working to expand our understanding of breast cancer diagnosis and treatment. Recently, a group in the BSE Division developed a framework that enables the transfer of discoveries derived from mouse models to humans. This success will allow breast cancer researchers to better predict how likely a tumor in humans is to metastasize based on how the corresponding cells in mice behaved.
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