1 Holman Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, New York University Grossman School of Medicine, New York, New York, USA.
2Keenan Centre for Biomedical Research, St. Michael’s Hospital, and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada.
3Department of Foundations of Medicine, Diabetes and Obesity Research Center, New York University Grossman Long Island School of Medicine, Mineola, New York, USA.
Google has unveiled a quantum computing breakthrough that could reshape the future of artificial intelligence, cryptography, medicine, and global technology. But does this really mean AI is becoming obsolete?
In this video, we break down Google’s Willow quantum chip, the revolutionary error-correction milestone it achieved, and why experts believe this could be one of the biggest advances in computing history. We also explain what the headlines get wrong, how quantum computing actually differs from AI, and why the future is likely to be a combination of both technologies rather than a competition.
You’ll discover:
• What makes Google’s Willow chip so significant.
• How quantum computers differ from classical AI
• Why the \.
Researchers have discovered a previously unknown mechanism that makes most pancreatic cancer cells susceptible to a form of programmed cell death. The team showed that cancer cells with mutations in the KRAS gene develop a vulnerability which can be used to eliminate tumor cells in preclinical models. The findings open up new perspectives for treating pancreatic cancer. The study was published in the journal Nature Communications.
Pancreatic cancer is one of the most aggressive forms of cancer and has so far shown only limited response to available treatments. In approximately 90 percent of cases, these tumors carry mutations in the KRAS gene that drive cancer growth. Due to the ageing population and the lack of effective therapies, physicians, clinicians, and researchers expect pancreatic carcinoma to become one of the leading causes of cancer-related deaths worldwide in the coming years. With the discovery of this newly identified vulnerability, a therapeutically promising approach has now been identified for treating this disease following future clinical trials.
The researchers discovered that KRAS-mutated tumor cells continuously activate signals from the innate immune system. This primes the cancer cells for an inflammatory form of cell death known as necroptosis. In order to survive, tumor cells rely heavily on the protein caspase-8, which usually inhibits necroptosis. If caspase-8 is blocked, the tumor cells die. “KRAS-mutated tumors have a previously unknown Achilles heel,” says the senior author of the study. “By switching off the tumor cells’ defence mechanisms, we can significantly kill these tumors.”
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An irregular heartbeat, or arrhythmia, leads to inefficient pumping of blood by the heart, which then prevents blood and oxygen from getting to the body’s other organs. When blood and oxygen flow poorly to the brain, the risk of stroke and cognitive decline increases.
A team of researchers based at Washington University in St. Louis used cardiac optogenetics to noninvasively study arrhythmia and its impact on the brain. Using highly sensitive imaging in a mouse model, they found that arrhythmia in a mouse heart alters oxygen concentration in the brain during and after arrhythmia.
Results of the research are published in Science Advances.
A gene therapy designed to replace a missing brain protein restored normal brain activity and improved behavior in a mouse model of fragile X syndrome (FXS), according to a study led by researchers at the University of California, Riverside. The findings, published in Molecular Therapy Nucleic Acids, suggest that gene therapy may one day address the underlying cause of FXS rather than simply treating its symptoms.
FXS affects approximately 2–3% of individuals diagnosed with autism and is one of the best-defined genetic causes of neurodevelopmental disability. The condition occurs when a mutation in the FMR1 gene prevents the production of fragile X messenger ribonucleoprotein (FMRP), a protein that regulates communication between brain cells.
“In a typical brain, FMRP acts like a brake or a volume control,” said Iryna Ethell, the paper’s senior author and a professor of biomedical sciences in the UCR School of Medicine. “Without it, neural circuits become overactive and less efficient, which contributes to many of the developmental and behavioral challenges associated with FXS.”
A team of scientists from the Barbara Ann Karmanos Cancer Institute, Wayne State University and institutions across the U.S. have published a new paper on the role of TGFBR1*6A, a naturally occurring genetic mutation in the TGFBR1 gene found in approximately 14% of the general population.
The study, “TGFBR1*6A and risk for colorectal cancer,” published June 9, 2026, in Cancer Communications, focuses on TGFBR1*6A and how it influences a person’s risk of developing colorectal cancer. Dr. Boris Pasche, president and CEO of the Karmanos Cancer Institute and chair of the Wayne State University Department of Oncology, was the first to discover TGFBR1*6A as a cancer risk allele.
“This mutation has often been overlooked by genome-wide association study chips, which cannot detect TGFBR1*6A, and is commonly missed by next-generation sequencing platforms due to the complexity of the region,” said Dr. Allan Johansen, a postdoctoral fellow and first author of the paper.
Post-traumatic stress disorder (PTSD) remains a common condition affecting World Trade Center (WTC) responders 25 years after the attack on the Twin Towers. While the condition is considered mainly psychological, a new study sheds light on changes in the biological processes of WTC patients with PTSD that may explain why PTSD is associated with a variety of chronic diseases that ultimately contribute to aging.
Completed by a team of researchers affiliated with the Stony Brook World Trade Center Health and Wellness Program, which monitors the health of and provides patient care to some 10,000 WTC responders, and scientists at Duke University, the study is published in Nature Communications.
The work represents more than a decade of research led by Benjamin J. Luft, MD, senior author, the Edmund D. Pellegrino Professor of Medicine in the Renaissance School of Medicine (RSOM) at Stony Brook University and director of the WTC Health and Wellness Program; and Pei-Fen Kuan, Ph.D., first author and professor in the Department of Applied Mathematics and Statistics in the College of Engineering and Applied Sciences at Stony Brook University.
A cross-university paper led by researchers at Queen Mary University of London, published in the Journal of Physiology, shows how better “digital twins” could help doctors treat people with atrial fibrillation.
One of the leading causes of stroke, atrial fibrillation (AF) is an erratic, quivering heartbeat that affects more than 1.5 million people in the U.K. The most common treatment is a procedure called ablation, in which doctors use heat or cold energy to destroy the small patches of heart tissue that trigger the chaotic rhythm. It works, but not for everyone and not always the first time.
Repeat ablation is common in persistent AF partly because the condition involves complex, distributed electrical changes that are hard to map in a single procedure.
For a child diagnosed with neuroblastoma—the most common infant cancer, occurring when early nerve cells grow out of control—the path to treatment isn’t simple. Some types of neuroblastoma resolve on their own, while others require aggressive intervention. Researchers have tried matching treatments to patients based on one-gene mutations with limited success. This is because patients’ outcomes depend on their entire molecular background, containing millions or even billions of features, such as DNA and RNA from tissues and blood.
“It’s much more than just one gene—everything that’s happening in the cells of the patient matters,” said Orly Alter, an associate professor of biomedical engineering at the University of Utah’s Scientific Computing & Imaging Institute.
Current artificial intelligence and machine learning (AI/ML) approaches require massive amounts of training data and, specifically, vastly more patient samples than genetic features.