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Leukemia stem cells cause treatments to fail, but findings open new avenues to overcome resistance

Scientists from the German Cancer Research Center (DKFZ) and the HI-STEM Stem Cell Institute have deciphered a key mechanism that contributes to treatment failure in acute myeloid leukemia (AML). They show that there are not just one, but four different subtypes of leukemia stem cells. This diversity could explain why one of the most important AML drugs does not work sufficiently in some patients or loses its effectiveness over time—resulting in the return of leukemia.

This discovery lays an important foundation for more precise and long-term successful treatment strategies that could specifically overcome resistance mechanisms. The findings are published in the journal Cell Stem Cell.

Acute myeloid leukemia (AML) is an aggressive form of blood cancer that primarily affects older people and often has a poor prognosis despite improved therapies. In recent years, the targeted drug venetoclax has significantly improved treatment. In combination with other drugs, venetoclax often shows good therapeutic success in AML and will, at least in part, replace highly aggressive chemotherapy in the future. However, AML returns in nearly all patients—usually because individual cancer stem cells become resistant to the drug.

AI agents may be skilled researchers—but not always honest ones

Artificial intelligence tools designed to execute end-to-end projects, from coming up with hypotheses to running and writing up experiments, are increasingly popular with researchers—and increasingly skilled.

But a new study shows these tools can stealthily violate norms of research integrity.

VANCOUVER, CANADA— Artificial intelligence (AI) tools designed to execute end-to-end projects, from coming up with hypotheses to running and writing up experiments, are increasingly popular with researchers—and increasingly skilled. But a new study shows these tools can stealthily violate norms of research integrity.

Computer scientist Nihar Shah of Carnegie Mellon University and colleagues looked at two high-profile tools— Agent Laboratory and the AI Scientist v2 —both developed recently to help computer scientists perform experiments within the field of machine learning. The AI Scientist made headlines earlier this year by being the first AI system to have an original research paper accepted by peer review.

But in a presentation at the World Conferences on Research Integrity here today, Shah reported that both systems engaged in acts that aren’t acceptable in research, including making up data and “p-hacking”: running an experiment multiple times but only reporting the best outcome. (The team’s results were previously posted as a preprint on arXiv.) The misbehaviors weren’t obvious and required a lot of sleuthing to track down, suggesting AI-assisted studies might fall victim to such problems without their authors’ knowledge.

Digital therapy outperforms referrals to campus clinics among college students

College students with anxiety, depression and eating disorders may be more likely to start and to respond more positively to therapy offered via a digital app compared to referrals to in-person campus clinics, according to a study led by Penn State researchers and published in the journal Nature Human Behaviour.

Globally, an estimated 40% to 60% of college students experience a mental health disorder at some point, and the need for campus counseling services has increased faster than institutions’ capacity to provide these services, according to the researchers.

The research team wanted to see if a proactive intervention using a digital therapy app could effectively treat anxiety disorders, depression and eating disorders, as well as address the increased need for psychological services.

Unlocking lithium’s hidden effects on Alzheimer’s disease at the cellular level

A recent study using advanced cell mapping shows that lithium chloride changes the activity of multiple enzymes linked to Alzheimer’s disease. These findings could help researchers design safer, more effective treatments for cognitive decline and dementia.

Magnetic checkerboard separates microparticles by size and sends them along different paths

A team of researchers from the Universities of Tübingen, Bayreuth, and Kassel, and the Polish Academy of Sciences has developed a method for precisely controlling the movement of magnetic microparticles based on their size. These suspended particles, known as colloidal particles, range in size from a few tens of nanometers to several micrometers. Controlling them is important for applications such as drug delivery, medical laboratory tests, and the synthesis of new materials. The team’s study has now been published in Physical Review Letters.

The new method involves positioning microparticles above a magnetic layer that is patterned like a chessboard. In previous studies, magnetic transportation of the colloidal particles was limited to a specific height. At this distance, although the magnetic forces appear to balance each other out, the particles move regardless of their size. Therefore, it was not possible to control the particles specifically based on their size.

Mobile qubits on a chip move us a step closer to everyday quantum computers

For years, quantum computers have lived under a huge bubble of hype, promising to revolutionize numerous fields, from medicine and battery design to materials science and cybersecurity. But realizing their potential on any serious practical level will only be possible if large numbers of qubits (the basic units of information) can interact with each other with high precision and flexibility.

One of the main things holding that back is that traditional qubits are fixed in place, meaning they can only talk to their immediate neighbors. But in a new paper published in Nature, scientists describe how they overcame this limitation by using mobile qubits that can be moved around a chip. Lars R. Schreiber at the JARA-FIT Institute for Quantum Information in Germany has also published a News & Views piece in the same journal.

Testing quantum collapse theory with the XENONnT dark matter detector

Theories of quantum mechanics predict that some particles can exist in superpositions, which essentially means that they can be in more than one state at once. When a particle’s state is measured, however, this superposition appears to “collapse” into a single outcome; a phenomenon often referred to as the “measurement problem.”

In recent years, various theoretical physicists have tried to explain why and how this collapse happens. This led to the introduction of various models, such as the Continuous Spontaneous Localization (CSL) and Diósi–Penrose models.

Both these models predict that spontaneous quantum collapse would also lead to the emission of faint X-ray radiation. The experimental detection of this radiation would thus provide evidence of these theories’ validity.

AI tool unifies fragmented cell maps into spatial atlases across tissues

A new computational method could dramatically accelerate efforts to map the body’s cells in space, according to a study published in Nature Genetics. Spatial multi-omics technologies—often described as ultra-high-resolution maps of tissues—allow scientists to see not only which genes or proteins are active in a cell, but exactly where that activity occurs. That spatial context is critical for understanding complex organs such as the brain, immune tissues and developing embryos.

Unfortunately, capturing multiple molecular layers at once remains expensive and technically challenging, said David Gate, Ph.D., assistant professor in the Ken and Ruth Davee Department of Neurology’s Division of Behavioral Neurology, who was a co-author of the study.

“In practice, investigators end up with ‘mosaic’ datasets: different slices or batches that each capture only some of the layers, often from different technologies or labs, with batch effects and missing pieces,” said Gate, who also leads the Abrams Research Center on Neurogenomics.

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