The authors show that dipolar condensates are prevalent in bosonic systems due to a self-proximity effect. Furthermore, they propose a new type of Josephson effect called dipolar Josephson effect, where a supercurrent of dipoles happens in the absence of particle flow.
How often should patients be screened for cannabis use? This is what a recent study published in JAMA Network Open hopes to address as a team of researchers from the University of California, Los Angeles (UCLA) investigated how primary care patients who recognize themselves as recreational cannabis users could potentially be at risk for—or suffering from—cannabis use disorder. This study holds the potential to raise awareness about healthcare providers conducting cannabis screening that could help identify early signs of cannabis use disorder in primary care patients.
For the study, the researchers analyzed surveys completed by 175,734 patients prior to a primary care appointment to assess their cannabis use and was conducted between January 2021 and March 2023. In the end, the researchers found that 17 percent indicated cannabis use in their surveys, of which 34.7 percent of those individuals demonstrated potentially high risk for cannabis use disorder based on their survey results. Additionally, 76.1 percent of patients indicated they used cannabis for medical reasons while not identifying as medical cannabis users. The researchers note these results indicate steps should be taken to conduct routine cannabis screenings of primary care patents by healthcare professionals.
“Patients may not tell their primary care providers about their cannabis use, and their doctors may not ask about it,” said Dr. Lillian Gelberg, MD, who is Professor in the Department of Family Medicine at the UCLA David Geffen School of Medicine and lead author of the study. “Not asking patients about their cannabis use results in a missed opportunity for opening up doctor-patient communication regarding use of cannabis generally and for management of their symptoms. ”
Discover an innovative ultra-thin battery for smart contact lenses, powered by tears and inspired by ‘Mission Impossible.’
Neuralink is onboarding patients in the UK in preparation for potential clinical trials amid a Brain-Computer Interface (BCI) boom.
In this Astrobite, the authors search for primordial black holes, a dark matter candidate, by calculating the effect their gravity would have on objects in our Solar System!
Imagine a portable 3D printer you could hold in the palm of your hand. The tiny device could enable a user to rapidly create customized, low-cost objects on the go, like a fastener to repair a wobbly bicycle wheel or a component for a critical medical operation.
Join us on Patreon! https://www.patreon.com/MichaelLustgartenPhDDiscount Links: Epigenetic Testing: https://trudiagnostic.com/?irclickid=U-s3Ii2r7xyIU-LSYLyQ…
SpaceX’s Starship and its massive reusable booster both successfully made their first controlled water landing during a fourth flight test on Thursday.
Why it matters: It’s a significant achievement for the vehicle, which is key to NASA’s Artemis program.
Currently, computing technologies are rapidly evolving and reshaping how we imagine the future. Quantum computing is taking its first toddling steps toward delivering practical results that promise unprecedented abilities. Meanwhile, artificial intelligence remains in public conversation as it’s used for everything from writing business emails to generating bespoke images or songs from text prompts to producing deep fakes.
Some physicists are exploring the opportunities that arise when the power of machine learning — a widely used approach in AI research—is brought to bear on quantum physics. Machine learning may accelerate quantum research and provide insights into quantum technologies, and quantum phenomena present formidable challenges that researchers can use to test the bounds of machine learning.
When studying quantum physics or its applications (including the development of quantum computers), researchers often rely on a detailed description of many interacting quantum particles. But the very features that make quantum computing potentially powerful also make quantum systems difficult to describe using current computers. In some instances, machine learning has produced descriptions that capture the most significant features of quantum systems while ignoring less relevant details—efficiently providing useful approximations.
