The startup Coherence Neuro is now testing a brain-computer interface that could one day use electrical stimulation to prevent tumors from growing. A San Francisco startup with ties to Elon Musk's Neuralink has started testing its brain implant to detect and treat cancer in humans. Coherence Neuro says it temporarily placed its coin-sized implant in the brains of three people undergoing surgery to have brain tumors removed at the Royal Melbourne Hospital in Australia. The implant was in place for roughly 30 minutes before being removed, providing an important safety check before the device can be implanted long-term in patients with brain cancer. Known as a brain-computer interface, the Coherence Neuro device is designed to sense the unique electrical signals of tumors and deliver mild electrical stimulation to prevent their growth.

Graph self-supervised learning (GSL) is essential for processing graph-structured data, reducing the need for manual labeling. Traditionally, this paradigm has extensively utilized Bayesian Personalized Ranking (BPR) as its primary loss function. Despite its widespread application, the theoretical analysis of its node relations evaluation have remained largely unexplored. This paper employs recent advancements in latent hyperbolic geometry to deepen our understanding of node relationships from a graph-theoretical perspective. We analyze BPR's limitations, particularly its reliance on local connectivity through 2-hop paths, which overlooks global connectivity and the broader topological structure.

I'd Rather Risk Cancer Than See AI Move This Fast I'd benefit if AI cured cancer. And I still want AI progress to slow down. On a fall afternoon 15 years ago, I met an idealistic researcher outside a Stanford coffee shop to discuss our shared dream: using AI to detect cancer. He had wiry hair, a penchant for talking with his hands, and a reputation for brilliance. He worked at a research lab that developed early screens for cancer; I, at 20, had just learned that I carried a mutation that conferred a very high risk of breast, ovarian, and other cancers.

Former US Attorney General Pam Bondi, who was removed from her role last month, has been diagnosed with thyroid cancer, according to multiple US outlets. Her diagnosis came shortly after President Donald Trump ousted her from the post of America's top law enforcement officer, according to Axios, which first reported the news of her illness. Bondi, 60, told CNN she is undergoing treatment and is still recovering from surgery that took place a few weeks ago, but is doing well. She is continuing to work despite the diagnosis, and will be joining the White House's new advisory council on AI, the Presidential Council of Advisors on Science and Technology. Podcast host and former White House adviser Katie Miller posted on social media that Pam has been quietly kicking cancer's ass the last few weeks, adding that Bondi has a heart of gold.

CAR-T cell therapy is already a potent treatment for certain cancers. Now, a small study is showing early promise for managing HIV. A Miracle cancer therapy that involves engineering a patient's own immune cells is being repurposed for HIV, and early results from two individuals hint at its promise for long-term control of the virus. As part of a clinical trial, scientists took people's own immune cells and reprogrammed them in a lab to recognize and attack HIV in the body. After a single infusion of the modified cells, two individuals with HIV now have undetectable levels of the virus--one for nearly two years and the other for almost a year.

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A team of scientists from Nanyang Technological University Singapore has developed a new biochip that, when paired with computer vision, can detect quickly and accurately extremely small amounts of microRNAs, which are tiny genetic markers linked to diseases such as heart disease. Published in the scientific journal, the new biosensing platform combines a specially designed nanophotonic chip with AI-automated image analysis. With a tiny drop of blood loaded into the chip, it can rapidly detect multiple microRNA biomarkers. With its integrated AI imaging function, thousands of microRNA signals can be imaged and analysed in a single snapshot. Compared with the current gold standard of detecting microRNA - PCR (polymerase chain reaction) detects tiny amounts of genetic material by copying them many times - the new device can cut detection time from hours to 20 minutes. MicroRNAs are short RNA molecules that help regulate genes that work in the body.

Be careful asking chatbots about your health. After George Mallon had his blood drawn at a routine physical, he learned that something may be gravely wrong. The preliminary results showed he might have blood cancer. Further tests would be needed. Left in suspense, he did what so many people do these days: He opened ChatGPT.

Precision medicine aims for personalized prognosis and therapeutics by utilizing recent genome-scale high-throughput profiling techniques, including next-generation sequencing (NGS). However, translating NGS data faces several challenges. First, NGS count data are often overdispersed, requiring appropriate modeling. Second, compared to the number of involved molecules and system complexity, the number of available samples for studying complex disease, such as cancer, is often limited, especially considering disease heterogeneity. The key question is whether we may integrate available data from all different sources or domains to achieve reproducible disease prognosis based on NGS count data. In this paper, we develop a Bayesian Multi-Domain Learning (BMDL) model that derives domain-dependent latent representations of overdispersed count data based on hierarchical negative binomial factorization for accurate cancer subtyping even if the number of samples for a specific cancer type is small. Experimental results from both our simulated and NGS datasets from The Cancer Genome Atlas (TCGA) demonstrate the promising potential of BMDL for effective multi-domain learning without ``negative transfer'' effects often seen in existing multi-task learning and transfer learning methods.