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Diseases and Conditions

“Revolutionizing Cancer Treatment: AI-Powered Model Predicts Cell Activity in Tissues Over Time”

A team of scientists has developed a remarkable new approach to modeling how cells behave over time—using a digital “forecast” much like predicting the weather. By combining patient genomics with a groundbreaking plain-language “hypothesis grammar,” the researchers can simulate how cells communicate and evolve within tissues. These simulations allow scientists to digitally test how cancers grow, how immune systems respond, and even how treatments might work in individual patients.

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The new software combines genomics technologies with computational modeling to predict cell changes in behavior, such as communication between cells that could cause cancer cells to flourish.

Researchers at the University of Maryland School of Medicine’s Institute for Genome Sciences (IGS) co-led the study that published online on July 25 in the journal Cell. It is the result of a multi-year, multi-lab project at the interface of software development with important collaborations between bench and clinical team science researchers.

“This research eventually could lead to computer programs that could help determine the best treatment for cancer patients by essentially creating a ‘digital twin’ of the patient,” said Jeanette Johnson, PhD, a Postdoc Fellow at the Institute for Genome Sciences (IGS) at UMSOM and co-first author of this study.

The team used a plain-language “hypothesis grammar” that uses common language as a bridge between biological systems and computational models to simulate how cells act in tissue. This grammar allows scientists to use simple English language sentences to build digital representations of multicellular biological systems and enabled the team to develop computational models for diseases like cancer.

In breast cancer, the IGS team modeled an effect where the immune system cannot curtail tumor cell growth and instead promotes invasion and cancer spread. They adapted this computational modeling framework to simulate a real-world immunotherapy clinical trial of pancreatic cancer.

Using genomics data from untreated tissue samples of pancreatic cancer, the model predicted that each virtual “patient” had a different response to the immunotherapy treatment — showcasing the importance of cellular ecosystems for precision oncology.

The team used new spatial genomics technology to further demonstrate the ways fibroblasts communicate with tumor cells. The program allowed the scientists to follow the growth and progression of pancreatic tumors to invasion from real patient tissue.

“What makes these models so exciting to me as someone who studies immunology is that they can be informed, initialized, and built upon using both laboratory and human genomics data,” said Dr. Johnson.

The new grammar is open source so that all scientists can benefit from it. “By making this tool accessible to the scientific community, we are providing a path forward to standardize such models and make them generally accepted,” said Daniel Bergman, PhD, a scientist at IGS and Assistant Professor of Pharmacology and Physiology at UMSOM.

To demonstrate this generalizability, Genevieve Stein-O’Brien, PhD, the Terkowitz Family Rising Professor of Neuroscience and Neurology at Johns Hopkins School of Medicine (JHSOM) led researchers in using this approach in a neuroscience example in which the program simulated the creation of layers as the brain develops.

“With this work from IGS, we have a new framework for biological research since researchers can now create computerized simulations of their bench experiments and clinical trials and even start predicting the effects of therapies on patients,” said Mark T. Gladwin, MD, Vice President for Medical Affairs at the University of Maryland, Baltimore, and the John Z. and Akiko K. Bowers Distinguished Professor and UMSOM Dean.

The team of senior authors on this study include, Paul Macklin, PhD, Associate Dean for Undergraduate Education and Professor of Intelligent Systems Engineering at the Indiana School of Informatics, Computing and Engineering at Indiana University, Genevieve Stein-O’Brien, Bloomberg Assistant Professor of Neuroscience and Assistant Director Single-Cell Training and Analysis Center (STAC) at Johns Hopkins University, and Dr. Fertig are continuing efforts to disseminate this software and extend its integration with genomics data for automatic model formulation through the National Cancer Institute (NCI) Informatics Technology in Cancer Research Consortium, who funded this study. Additional benchmarking of this study and applications of the software to breast and pancreatic cancer are supported from numerous NCI grants, the Jayne Koskinas Ted Giovanis Foundation, the National Foundation for Cancer Research, the Cigarette Restitution Fund Program from the State of Maryland, and the Lustgarten Foundation.

Alzheimer's Research

Walking 7000 Steps a Day Can Be Just as Beneficial as 10,000 – Here’s Why

Walking 7000 steps a day may be just as powerful as hitting the much-hyped 10,000-step goal when it comes to reducing the risk of early death and disease. A sweeping global review of 57 studies shows that 7000 steps per day slashes the risk of dying early by nearly half—and brings major benefits across heart health, dementia, depression, and more. The bonus? Even walking from 2000 to 4000 steps per day brings measurable improvements. For millions of people, this study redefines what it means to “move enough.”

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The study, led by Professor Melody Ding from the University of Sydney, has made a groundbreaking discovery that walking 7000 steps a day can offer similar health benefits as walking 10,000 steps. This finding is based on an analysis of data from 57 studies conducted in over ten countries between 2014 and 2025.

The researchers examined the impact of different daily step counts on various health outcomes, including cardiovascular disease and cancer. They found that walking at least 7000 steps a day can significantly improve eight major health outcomes, such as reducing the risk of cardiovascular disease, dementia, and depressive symptoms.

Professor Melody Ding emphasized that aiming for 7000 steps is a realistic goal for people who struggle to meet traditional exercise guidelines. “Even small increases in step counts, like going from 2000 to 4000 steps a day, are associated with significant health gains,” she said.

The researchers compared the health outcomes of people walking at different step increments, starting at 2000 steps per day. They found that:

* When compared to 2000 steps a day, walking 7000 steps a day was associated with a 47% lower risk of death from cardiovascular disease and cancer.
* For those who cannot yet achieve 7000 steps a day, even small increases in step counts can lead to significant health improvements.

Experts are calling for future studies to explore how step goals should vary based on age, health status, and region. They also suggest including diverse populations and longer-term data to strengthen the evidence. Professor Ding emphasizes that this kind of detail is rare and will be useful for health practitioners when tailoring advice for patients.

Overall, the study suggests that walking 7000 steps a day can be a more achievable and beneficial goal than previously thought, and even small increases in daily movement can lead to meaningful health improvements.

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Children's Health

A Genetic Breakthrough: Predicting Adulthood Obesity in Early Childhood

What if we could predict obesity before it ever takes hold? A global team has created a genetic test that forecasts a child’s risk of adult obesity before age five—years before other factors kick in. By analyzing data from over five million people, their polygenic risk score doubles the predictive power of previous tools. While genetics isn’t destiny, those with higher genetic risk respond better to weight loss interventions but may regain weight quickly. The tool isn’t perfect, it performs far better in people of European ancestry, but it’s a game-changer in early prevention.

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The World Health Organization predicts that over half of the global population will develop overweight or obesity by 2035. Despite treatment strategies like lifestyle changes, surgery, and medications, these methods are not universally available or effective. An international team of researchers has made a groundbreaking discovery – a genetic test that can predict adulthood obesity in early childhood.

By leveraging genetic data from over five million people, the researchers created a polygenic risk score (PGS) that identifies children at higher genetic risk of developing obesity. This finding could lead to targeted preventative strategies, such as lifestyle interventions, at a younger age.

“What makes this score so powerful is its ability to predict, before the age of five, whether a child is likely to develop obesity in adulthood,” says Assistant Professor Roelof Smit from the NNF Center for Basic Metabolic Research (CBMR) at the University of Copenhagen and lead author of the research published in Nature Medicine.

The study draws on data from the Genetic Investigation of Anthropometric Traits (GIANT) Consortium, an international collaboration of human genetics researchers. The research involved a partnership with 23andMe, inc., and contributions from over 600 scientists from 500 institutions globally.

Twice as effective at predicting obesity as the next best test, the new PGS combines the effects of thousands of genetic variants that increase our risk of obesity. These variants act in the brain and influence our appetite, making them a crucial factor in the development of adulthood obesity.

“This new polygenic score is a dramatic improvement in predictive power and a leap forward in the genetic prediction of obesity risk,” says Professor Ruth Loos from CBMR at the University of Copenhagen.

While genetics is not destiny, the researchers also investigated the relationship between a person’s genetic risk of obesity and the impact of lifestyle weight loss interventions. They found that people with a higher genetic risk of obesity were more responsive to interventions but also regained weight more quickly when the interventions ended.

However, the new PGS has its limitations – it was far better at predicting obesity in people with European-like ancestry than in people with African ancestry. Further research is needed to address these disparities and make this groundbreaking test universally useful.

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Animals

“From Millipede Secretions to Human Pain Relief — A New Path for Drug Discovery”

Millipedes, often dismissed as creepy crawlies, may hold the secret to future painkillers and neurological drugs. Researchers at Virginia Tech discovered unique alkaloid compounds in the defensive secretions of a native millipede species. These complex molecules, which cause disorientation in ants, interact with human neuroreceptors linked to pain and cognition. By decoding these natural chemical defenses, scientists could open a new path toward innovative drug therapies, though challenges remain in producing the compounds at scale.

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The discovery of new compounds in millipede secretions has opened up exciting possibilities for drug development and the treatment of neurological diseases. A team led by chemist Emily Mevers has found complex structures in these secretions that can modulate specific neuroreceptors in ant brains, leading to disorientation in the ants.

These newly discovered structures, called alkaloids, fall into a class of naturally occurring compounds that have been studied for their potential pharmacological applications. The Mevers team named them andrognathanols and andrognathines after the millipede species, Andrognathus corticarius, found on Virginia Tech’s Blacksburg campus.

Mevers’ research focuses on leveraging the chemistry of underexplored ecological niches, such as the millipede, for drug discovery. Her team collected millipedes from Stadium Woods and used various analytical tools to identify the compounds contained in their defensive glands. These secretions are released by the millipedes to ward off predators while also sharing their location with their kin.

The broader implications of this research are significant, as much about millipedes remains mysterious, including their specific habitats, numbers, diets, behaviors, and chemistry. Mevers is collaborating with millipede expert Paul Marek in the entomology department to fill in these gaps and explore potential applications for future medications.

In a previous study, Mevers and Marek examined a millipede native to the Pacific Northwest and discovered that related alkaloids interacted potently and selectively with the Sigma-1 neuroreceptor. This interaction suggested that this family of compounds may have useful pharmacological potential for treating pain and other neurological disorders.

The new alkaloids discovered in this study are actively secreted from the Hokie millipede when it is physically disturbed, causing disorientation in ants, a presumed natural predator. A subset of these compounds possesses similar interactions with the Sigma-1 neuroreceptor, further supporting their potential for drug development.

With these complex compounds in hand, the next step is to synthesize them in larger quantities and evaluate their biomedical applications. According to Mevers, “These compounds are quite complex, so they’re going to take some time to synthesize in the lab.” Once larger quantities are available, Mevers will be able to better study their properties and potential in drug development, potentially leading to new treatments for human pain relief.

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