Falls among older adults often begin with changes too subtle to be noticed during an ordinary walk. A person may still appear steady while moving through a familiar environment, yet struggle when asked to divide attention between walking and another task. A new study by researchers Dong, Hu, Guo and colleagues examines whether machine-learning techniques can identify this hidden vulnerability in community-dwelling older adults by analyzing how gait changes during dual-task walking. Published in BMC Geriatrics in 2026, the research focuses on a potentially powerful combination of digital movement measurement and artificial intelligence: spatiotemporal gait parameters recorded while people walk and perform a second mental or motor activity.

Walking is not a single, automatic action. It requires continuous coordination between the brain, muscles, sensory systems and balance mechanisms. When a person walks while counting backward, answering questions or carrying an object, the nervous system must distribute attention between locomotion and the competing task. This is known as dual-task walking. Older adults who have limited cognitive or physical reserve may respond by slowing down, shortening their steps, widening their base of support or becoming more irregular in their timing. These adaptations can provide clues about fall susceptibility that may remain invisible during conventional clinical walking tests.

The study’s central idea is that gait should be evaluated under realistic cognitive pressure rather than only in ideal, distraction-free conditions. Spatiotemporal gait parameters describe measurable features of movement across time and space, including walking speed, stride length, step time, cadence, stance duration and the variability between consecutive steps. In a dual-task setting, researchers can also examine the degree to which these variables change compared with normal walking. This difference, often called dual-task cost, can reveal how much a person’s mobility is affected when attention is divided. A larger cost may indicate reduced ability to manage competing demands while remaining stable.

Machine learning offers a way to analyze these patterns simultaneously. Traditional clinical assessment may focus on one or two measurements, such as walking speed or the number of previous falls. A machine-learning model can process many variables at once and search for combinations associated with elevated risk. Depending on the model design, the system may learn from labeled examples, in which participants’ gait data are linked to fall-related outcomes, and then estimate risk for new individuals. Algorithms can detect nonlinear relationships that are difficult to capture with conventional statistical methods, although their usefulness depends heavily on the quality, size and representativeness of the data used for training.

The community setting is especially important. Many studies of balance and falls are conducted in hospitals or specialist laboratories, where participants may be healthier, more closely supervised or less representative of the broader older population. Community-dwelling adults live independently and encounter the complex conditions that make falls more likely: distractions, uneven surfaces, hurried movement, household obstacles and the need to perform several tasks at once. By focusing on this group, the research addresses a practical question for preventive medicine: can accessible gait testing identify people who appear independent but may require additional support before a serious fall occurs?

A potential advantage of this approach is its ability to move fall-risk assessment toward objective, repeatable measurement. Gait sensors, pressure-sensitive walkways, wearable devices or camera-based systems can convert movement into numerical data. Those data may be more sensitive than a brief visual observation, particularly when they capture step-to-step variability and changes induced by a secondary task. In the future, similar measurements could potentially be collected in clinics, rehabilitation centers or even at home. However, a prediction tool would need to be tested across different ages, health conditions, walking environments and technology platforms before it could be trusted for widespread use.

The research also highlights the technical challenges behind apparently simple predictions. Falls are influenced by numerous factors, including muscle strength, vision, medication use, reaction time, fear of falling, neurological disease and environmental hazards. Gait features alone cannot explain every incident. A machine-learning model may also perform impressively on the data used to develop it but less reliably on people from another population, a problem known as limited generalizability or overfitting. For clinical adoption, researchers must therefore evaluate calibration, sensitivity and specificity, test the model on independent datasets and make its decisions understandable to health professionals and patients.

Although the citation identifies the study’s objective and methodology, it does not provide detailed numerical findings such as sample size, predictive accuracy or the specific algorithm used. Those results will determine how close this technology is to practical deployment. Even so, the study reflects a broader shift in geriatric care: fall prevention is increasingly being treated as a problem of measurable, dynamic performance rather than a simple checklist of past incidents. By combining dual-task gait analysis with machine learning, the researchers are investigating whether the body’s response to distraction can serve as an early warning signal—one that could eventually help clinicians intervene before a dangerous fall changes an older person’s independence.

Subject of Research: Fall-risk prediction in community-dwelling older adults using machine learning and dual-task spatiotemporal gait parameters.

Article Title: Machine learning-based fall risk prediction in community-dwelling older adults using dual-task spatiotemporal gait parameters.

Article References: Dong, G., Hu, H., Guo, Y. et al. “Machine learning-based fall risk prediction in community-dwelling older adults using dual-task spatiotemporal gait parameters.” BMC Geriatrics (2026). https://doi.org/10.1186/s12877-026-07998-3

Image Credits: AI Generated

DOI: 10.1186/s12877-026-07998-3

Keywords: machine learning, fall risk prediction, older adults, community-dwelling older adults, dual-task walking, gait analysis, spatiotemporal gait parameters, geriatric medicine, balance, mobility.

As populations age, scientists are looking more closely at a hidden biological process that may help explain why some older adults remain mobile and independent while others experience a rapid decline in strength, balance, and endurance. A systematic review by A. Boshnjaku, R. Obërtinca, M. Hoxha, and colleagues examines whether five inflammation-related biomarkers—CRP, high-sensitivity CRP, interleukin-6, interleukin-10, and tumor necrosis factor-alpha—are linked to physical performance in older adults living in the community. Published in BMC Geriatrics, the review focuses on a question with major implications for healthy aging: can signals circulating in the blood reveal who is at greater risk of losing physical function?

The study centers on chronic, low-grade inflammation, a biological condition often associated with aging and commonly described as “inflammaging.” Unlike the intense inflammation triggered by an infection or injury, inflammaging can persist quietly over years. It may be influenced by immune-system changes, excess body fat, physical inactivity, chronic disease, and cellular stress. Although inflammation is essential for repairing tissue and fighting pathogens, prolonged activation can affect muscle metabolism, blood-vessel function, nervous-system signaling, and energy production. These changes could, in theory, make everyday movements such as walking, rising from a chair, or climbing stairs increasingly difficult.

C-reactive protein, or CRP, is one of the most widely used indicators of systemic inflammation. It is produced primarily by the liver in response to inflammatory signals, especially interleukin-6. Standard CRP testing is useful for detecting relatively pronounced inflammatory activity, while high-sensitivity CRP, known as hs-CRP, can measure much lower concentrations. That distinction matters in research on older adults, where subtle inflammatory differences may exist even when there is no obvious acute illness. However, neither CRP nor hs-CRP identifies a single disease or proves that inflammation itself caused a decline in physical ability.

The review also considers three signaling proteins known as cytokines. Interleukin-6, or IL-6, can promote inflammation but also participates in muscle adaptation and energy regulation, making its effects highly dependent on timing, tissue, and physiological context. Tumor necrosis factor-alpha, or TNF-α, is a potent inflammatory mediator that can influence muscle protein breakdown and interfere with insulin signaling. Interleukin-10, or IL-10, generally acts as an anti-inflammatory regulator, helping restrain excessive immune activation. Examining these markers together offers a more nuanced picture than relying on a single laboratory value.

Physical performance is a crucial measure of aging because it reflects how effectively multiple systems work together. Muscle strength alone does not determine whether a person can remain independent. Walking speed, repeated chair stands, balance, grip strength, endurance, and composite tests such as the Short Physical Performance Battery can reveal the combined effects of the muscular, cardiovascular, neurological, and skeletal systems. For people living outside hospitals and care facilities, these measures may also provide a practical snapshot of resilience in everyday life. A small difference in performance can translate into a major difference in fall risk, mobility, and the ability to manage daily activities.

By bringing together evidence from studies of community-dwelling older adults, the authors address an important gap between laboratory research and real-world aging. Older people living independently are not a uniform group: some have multiple chronic conditions, while others remain highly active; some take anti-inflammatory medications, and others do not. Diet, smoking, obesity, socioeconomic conditions, sleep, recent infections, and exercise can all alter cytokine levels and physical performance. A systematic review can help determine whether associations between biomarkers and function appear consistently across this complex population or whether they vary substantially from one study to another.

The review’s importance lies not only in asking whether inflammatory markers are associated with poorer performance, but also in highlighting the limitations of interpreting such relationships. A high biomarker level may be a consequence of frailty, reduced activity, obesity, or an undiagnosed illness rather than the original cause of decline. Conversely, a single blood sample may fail to capture fluctuations in inflammation over time. Differences in laboratory methods, cutoff values, participant health, medication use, and performance tests can make studies difficult to compare. These issues are central when translating population-level associations into decisions about an individual patient.

The findings may ultimately support a broader approach to healthy aging in which inflammation is considered alongside strength, nutrition, cardiovascular health, cognition, and social environment. The biomarkers discussed in the review are not presented as standalone tests that can predict the future with certainty. Instead, they may help researchers understand why physical function changes and identify biological pathways that could be targeted through exercise, weight management, treatment of chronic disease, or other interventions. The study also underscores the need for longitudinal research capable of separating cause from consequence and for standardized testing that can connect laboratory signals with meaningful improvements in daily life.

For now, the review places a spotlight on the immune system as a possible contributor to the physical changes associated with later life. Its focus on CRP, hs-CRP, IL-6, IL-10, and TNF-α reflects the growing recognition that aging is not simply the result of muscles becoming weaker, but of interconnected changes across the body. Understanding how these inflammatory signals relate to mobility could help scientists develop more precise strategies for preserving independence—while reminding clinicians and the public that a blood test is most informative when interpreted together with a person’s functional ability, medical history, and lived environment.

Subject of Research: The relationship between inflammatory biomarkers and physical performance in community-dwelling older adults.

Article Title: What is the impact of CRP, hs-CRP, IL-6, IL-10, and TNFα in physical performance in community-dwelling older adults: a systematic review.

Article References: Boshnjaku, A., Obërtinca, R., Hoxha, M. et al. What is the impact of CRP, hs-CRP, IL-6, IL-10, and TNFα in physical performance in community-dwelling older adults: a systematic review. BMC Geriatrics (2026). https://doi.org/10.1186/s12877-026-08080-8

Image Credits: AI Generated

DOI: 10.1186/s12877-026-08080-8

Keywords: aging, older adults, inflammation, CRP, hs-CRP, IL-6, IL-10, TNF-α, physical performance, inflammaging, systematic review

Certain gut bacteria were linked to slower or faster biological aging. The communities of bacteria living in the gut may carry clues about how rapidly the body is aging. Researchers at the University of Hawaiʻi at Mānoa found that certain microbial patterns were associated with faster or slower biological aging, adding to evidence that gut [...]

Medical imaging plays a pivotal role in today’s healthcare system. CT scans, ultrasounds, X-rays, MRIs, and other diagnostic

Every breath we exhale carries a complex chemical narrative written in thousands of volatile organic compounds (VOCs). These tiny molecules, produced by human metabolic processes and invading pathogens, hold immense diagnostic potential. Yet, translating this molecular cloud into a definitive medical diagnosis has long faced an analytical bottleneck. Now, an international team of physicists from Botswana and South Africa has harnessed machine learning to decode these complex signatures, offering a rapid, non-invasive pathway to detect tuberculosis (TB) directly from human breath.

Breaking the analytical bottleneck

Traditional gas chromatography-mass spectrometry (GC-MS) excels at separating complex molecular mixtures based on volatility and mass-to-charge ratios. However, a single breath sample generates thousands of overlapping spectral peaks. Manually sifting through this dense landscape is a painstaking, time-consuming process.

To find a faster, more systematic approach to molecule identification, researchers in the Nano –Breath taking group led by George Chimowa from Botswana International University of Science and Technology (BIUST) collected breath samples from three distinct cohorts: patients with active TB, individuals with multidrug-resistant TB (MDR-TB), and healthy control volunteers.

Standard analytical pipelines often rely on aggressive data pre-processing to smooth and simplify the GC-MS spectra. However, this filtering risks erasing subtle, low-concentration molecular features that might hold critical diagnostic clues. Bypassing this limitation, the BIUST group fed a high-dimensional dataset of 1867 spectral features (from 87 breath samples) directly into four competing supervised machine-learning algorithms: decision trees, random forest, k-nearest neighbours, and support vector machines (SVM). The researchers published their findings in Discover Artificial Intelligence.

Geometric optimization avoids the pitfalls of high-dimensional data

The team found that the linear SVM classifier led the algorithmic comparison, distinguishing among the three patient cohorts with 93% accuracy.

This performance stems from how the algorithm handles high-dimensional data spaces. While distance metric models often suffer from overfitting errors, where experimental noise is misidentified as a meaningful diagnostic signal, SVM relies on geometric boundary optimization to separate classes.

Much like clearing a wide highway down the centre of two opposing crowds, it identifies a hyperplane that maximizes the margin between groups. Instead of tracking every individual in the crowd, the algorithm relies solely on a sparse subset of border points known as support vectors. By focusing strictly on these boundary “guards” and ignoring the background movement, this approach completely isolates the classifier from the noise inherent to the surrounding high-dimensional feature space.

Beyond raw classification accuracy, the model yielded a crucial insight for future clinical applications. It pinpointed a specific window within the gas chromatogram, a retention time between 10 and 30 min, where the most diagnostic, high-variance VOCs elute. This 20-min window corresponds to medium- and large-molecule VOCs, including specific chemical biomarkers associated with TB, such as tridecane, decane and O-cymene.

By demonstrating that these vital diagnostic features are concentrated within this single slice of data, the physicists have shown that future diagnostic hardware can be specialized, eliminating the need to analyse the entire spectrum. Narrowing the focus to this specific elution window can drastically shrink data-processing times and maximize instrument throughput in clinical settings.

AI and the future of medical diagnostics

TB remains one of the world’s deadliest infectious diseases. Traditional diagnostics rely heavily on slow sputum cultures and backlogged laboratory queues, delaying critical treatment. A rapid, non-invasive breath test powered by an optimized physical classifier offers an ideal alternative for resource-limited settings where laboratory infrastructure may be scarce.

While the researchers emphasize that larger validation trials are required, the work signals a profound shift in medical diagnostics. It mirrors advanced data-modelling techniques used in engineering, such as those predicting structural degradation in self-healing aerospace composites.

By fusing analytical physics with artificial intelligence, this research points to a future where immediate, automated bedside disease detection could be rapid, accessible and driven entirely by the mathematical profiling of a patient’s breath.

The post AI sniffs out tuberculosis in breath’s chemical signature appeared first on Physics World.

A large British study spanning more than 70 years has found that people who experience chronic money struggles over their working lives show measurable declines in memory and processing speed by their 50s, along with signs of accelerated brain shrinkage decades later.

The post Decades of Financial Hardship Linked to Faster Brain Aging appeared first on Sci.News: Breaking Science News.

Thousands of years ago, a striking blue pigment began appearing in the art that adorned the walls of ancient Egyptian tombs, and eventually on the crown of the famous bust of Nefertiti, wife of the 18th dynasty pharaoh Akhenaten and half-sister to one of Ancient Egypt’s most famous kings: Tutankhamun.

However, the ancient artists who first used this remarkable pigment, known today as “Egyptian blue,” had likely never guessed that thousands of years into the future, this unique substance would have applications in modern biomedical research.

That is precisely what one team of researchers discovered several years ago, in a study that revealed how this striking pigment is more than just a modern medical marvel with origins in the ancient world—it also provides evidence of ancient nanotechnology in early Egypt.

The Enigma of Egyptian Blue

“Egyptian blue” is the common term for a pigment first developed in ancient Egypt around 2500 B.C., made from calcium copper silicate. Originally, it was created to provide an artificial alternative to a more costly form of blue pigment from the era, which was produced using lapis lazuli.

However, ancient Egyptian scientists discovered that by heating limestone, sand, and copper, they could produce a far less expensive form of blue pigment, and soon its use spread throughout the ancient Mediterranean world, making its way into the craftsmanship in parts of the ancient world that ranged from Mesopotamia to Greece and Rome.

An Ancient Technology Finds Modern Biomedical Applications

Although Egyptian blue’s obvious practical uses in art were derived from its unique and stunning appearance, time would eventually reveal that it had other uses. Specifically, in a 2020 study published in Nature Communications, a team of researchers reported on their discovery that Egyptian blue in its powdered form can be separated into nanoscale sheets roughly 100,000 times thinner than a human hair.

This is significant because these tiny particles of the substance exhibit significant fluorescence under near-infrared light, which can be used by researchers to track them within biological samples.

Chemist Sebastian Kruss of Georg-August University Göttingen, the lead author of the 2020 study, remarked at the time that he was certain such capabilities “will be of growing interest for material scientists as well as biomedical research” in the years ahead.

Ancient Nanotech’s Survival Through the Centuries

One reason the pigment is so useful is also the likely reason behind its discovery, and that is Egyptian blue’s durability. For many millennia, samples of this unique material have managed to survive, and the material’s nanoscale form remains stable even over such long periods.

In the past, earlier studies had demonstrated that the material fluoresces in near-infrared wavelengths. Additionally, it was known to scientists that the pigment could be broken down into flakes after being stirred in hot water for periods of several days.

Building on this knowledge, the research detailed in the 2020 paper sought to go even further by examining whether those particles could function as fluorescent markers in living systems.

Near-Infrared Applications

One thing that scientists find useful about near-infrared imaging is that it provides an effective way to examine biological material by leveraging wavelengths that fall just beyond the ranges of the visible spectrum.

Although some molecules can already be used as fluorescent markers, Egyptian blue nanoparticles generate significantly brighter signals than several common alternatives. Additionally, unlike other fluorescent compounds, the pigment can resist photobleaching, which is important because this allows its glow to persist even during prolonged observation.

A pair of experiments was used by the team to demonstrate the material’s potential. The first involved using Egyptian blue particles injected within a fruit fly embryo, which were tracked as it developed. In another, the particles were added to plant leaves, allowing comparisons with the performance of a conventional near-infrared imaging molecule.

In this experiment, the team found that the Egyptian blue produced a bright signal that remained visible even against the leaves’ natural fluorescence—the comparison molecule, by contrast, was heavily obscured.

According to Kruss, the tiny Egyptian Blue particles prove to be “very useful glowing labels in biomedical research,” calling the pigment a “very powerful fluorophore.”

An Ancient Discovery Breaking New Ground in the 21st Century

According to Kruss and his colleagues, such particles could eventually help scientists observe processes including embryonic development and cell division, while potentially supporting applications such as image-guided surgery and the labeling of cancerous tissue.

Altogether, this enigmatic form of ancient nanotechnology is still playing a role in helping develop the cutting-edge biomedical technologies of tomorrow, even more than 5,000 years after its initial discovery.

Kruss and his team’s original paper from 2020, “Exfoliated near infrared fluorescent silicate nanosheets for (bio)photonics,” was published in Nature.

Micah Hanks is the Editor-in-Chief and Co-Founder of The Debrief. A longtime reporter on science, defense, and technology with a focus on space and astronomy, he can be reached at micah@thedebrief.org. Follow him on X @MicahHanks, and at micahhanks.com.

Age-related remodeling of the immune system leaves older adults increasingly susceptible to sudden infections and cancer, while also promoting chronic, misdirected inflammation and impaired wound repair. These effects help drive a wide spectrum of long-term diseases. Yet despite rapid progress in immunology and gerontology, key mechanistic links—how specific immune-cell programs degrade over time and how that degradation shapes early response decisions—remain incompletely understood.

A major obstacle has been the limited availability of immune-aging models that are both predictive and experimentally tractable. Conventional in vitro systems often fail to capture the tissue context, cellular crosstalk, and microenvironmental cues that govern when and how immune responses ignite. As a result, “aging phenotypes” can be difficult to reproduce across laboratories and difficult to connect to causal pathways.

In a 2026 Nature Aging discussion, Nikolich and colleagues argue that the field needs a shift from descriptive comparisons toward engineered, mechanistically grounded platforms. They propose multidisciplinary strategies spanning immunology, gerontology, and engineering to interrogate immune aging at the point of initiation—when innate sensors, tissue barriers, and early signaling networks collectively determine the trajectory of downstream immunity.

The authors highlight drug delivery technologies as a route to more precise interventions. By tuning where, when, and at what dose immunomodulatory agents act, researchers could better test how age-altered signaling thresholds change the balance between protective responses and harmful inflammation.

They also emphasize extracellular matrix (ECM) and biomaterials, noting that aging reshapes the physical and biochemical scaffold surrounding immune cells. Biomaterials that recapitulate age-associated stiffness, ligand density, and degradation dynamics could reveal how microenvironmental cues reprogram immune recruitment, activation, and effector function.

Microphysiological systems and organoid-like models are presented as complementary engines for causality. These platforms can integrate multiple cell types, enable controlled perturbations, and better mimic tissue architecture than traditional culture formats.

Together, these approaches aim to deliver validated in vitro tools for immune-aging research—tools designed not only to measure differences, but to reproduce them reliably and connect them to specific upstream drivers.

The ultimate ambition is precision diagnostics and therapies that can “reprogram and rejuvenate” immune function in older adults, extending both lifespan and healthspan. By targeting immune-response initiation with engineered models, the field may finally bridge the gap between observational aging biology and interventions that work.

Subject of Research: Aging immune system; immune response initiation models and modulation

Article Title: Bioengineering strategies to interrogate and modulate the aging immune system

Article References: Nikolich, J.Ž., Wolf, M.T., Pompano, R.R. et al. Bioengineering strategies to interrogate and modulate the aging immune system. Nat Aging (2026). https://doi.org/10.1038/s43587-026-01187-y

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s43587-026-01187-y

Scientists may have found a way to rejuvenate key cells that help rebuild muscle in the body and thus preserve muscle into old age, they report in a new mouse study.

It is "really exciting stuff," Matthew Krause, a muscle physiologist at the University of Windsor in Ontario, said of the results. It could have clinical applications down the road if shown to work in humans, as well, added Krause, who was not involved in the research.

The body loses muscle with age and with chronic conditions that compromise people's mobility. To find a potential treatment for this muscle atrophy, the scientists behind the new study looked to "satellite cells." These stem cells are located around skeletal muscle fibers and play an important role in muscle regeneration.

Muscles incur microscopic damage with use, and this damage calls satellite cells into action. They proliferate to rebuild muscle fibers, by either merging into the torn muscle cells or forming new muscle cells. With age, satellite cells activate less efficiently and muscles become weak. But the new study, published July 24 in the journal Scientific Reports, suggests that it may be possible to rescue satellite cells from dormancy.

When muscles tear during exercise, a protein called hepatocyte growth factor (HGF) gets released. This is the signal that binds to receptors on satellite cells and activates them. Previously, studies from the same team showed that HGF works less well in old lab mice because its structure is chemically altered by a compound called peroxynitrite, and this makes it harder for HGF to plug into its receptor on satellite cells. This chemical alteration is known as nitration.

Peroxynitrite is produced through the reaction of unstable nitrogen and oxygen molecules, called radicals, said study co-author Ryuichi Tatsumi, a muscle physiologist at Kyushu University in Japan. These radicals are produced during normal metabolism, but their levels increase with age, he explained.

As a result, satellite cells cannot be activated efficiently to trigger muscle growth, even if the amount of HGF itself does not decline. Tatsumi and colleagues showed this in a previous study in rats.

In the new study, the scientists tested two compounds to see if they could counter this effect in lab mice. They used an experimental setup in which mice couldn't use their muscles as normal, so they weakened; such muscle disuse is known to increase HGF nitration around muscle cells.

Both of the tested compounds are known to protect proteins from damage caused by highly reactive chemicals by intercepting the reaction before it can take place. In experiments, the team treated HGF with each compound before exposing it to peroxynitrite. They found that, compared with untreated HGF, HGF treated with either compound showed substantially lower levels of nitration. This preserved its ability to activate satellite cells grown in lab dishes.

At high concentrations, one of the compounds — lipoic acid trisulfide (LASSS) — not only prevented nitration but also markedly increased HGF's ability to plug into its receptor. That suggests LASSS may chemically modify HGF in a way that could boost its effects.

To see if these observations carried over to living animals, the researchers gave lab mice LASSS in their drinking water. Although these were young mice, they were prevented from bearing weight on their hind limbs for five days, so they experienced short-term muscle disuse. Compared with untreated animals, the mice given LASSS had substantially lower HGF nitration around their muscle cells.

It's "pretty remarkable" that providing LASSS by mouth was sufficient to produce this effect, Krause said. He noted, however, that the researchers did not measure muscle atrophy or try administering LASSS to old mice to see the effects on the muscle. Longer experiments would be needed to observe those changes, he told Live Science.

The compound could also be tested in other forms of muscle wasting, Krause suggested. For example, severe chronic diseases like cancer can cause cachexia, a wasting syndrome marked by dramatic weight and muscle loss.

Related stories

• Human aging accelerates dramatically at age 44 and 60
• Rise in cancer in younger adults may be explained by faster 'biological aging,' early study hints
• 'Aging clocks' tell you how much 'older' you are than your chronological age. How do they work?

In general, protein nitration has been linked to several age-related disorders, including Alzheimer's disease, in which nitrated proteins accumulate in the brain. Understanding how to protect proteins from this kind of chemical damage could have implications beyond muscle biology, Tatsumi said.

However, further studies are still needed to assess the effectiveness and safety of LASSS in older animals. Experiments involving aging animals are expensive and can take a lot of time, Tatsumi said, but he is optimistic that this understanding will help address a range of age-related disorders with this same underlying biology.

"That is our big wish," he told Live Science. "I think, in the near future, we can do that."

 

Join us for an in‑depth conversation with Simon Cherry, former editor‑in‑chief of Physics in Medicine & Biology (PMB), as he shares his expert perspective on the evolving landscape of medical physics research, molecular imaging, and non-invasive imaging technologies. Drawing on decades of leadership in biomedical imaging and academic publishing, Simon will reflect on major advances shaping the field, define the characteristics of high‑impact imaging research, and highlight how PMB continues to support rigorous, influential scientific work through its editorial standards and peer‑review practices.

This session will additionally spotlight the research of Xinchen (Alison) Deng, a finalist of the PMB 2024 Early Career Award. Attendees will gain insights into her contributions to the advanced medical imaging community, such as photon-counting CT for clinical applications and how her work supports broader advances in medical physics and X-ray/CT imaging research.

Simon will also discuss what makes early‑career research stand out and share strategies for researchers aiming to increase the visibility, reach, and long‑term impact of their publications within competitive scientific journals.

Moderator

Carol Clark, Publisher, Physics in Medicine & Biology

Speakers

Simon R Cherry, PhD received his BSc (Hons) in physics with astronomy from University College London in 1986 and a PhD in medical physics from the Institute of Cancer Research, University of London in 1989. Simon is currently Distinguished Research Professor in the Departments of Biomedical Engineering and Radiology at UC Davis and Professor of Total-Body Imaging at King’s College London. Simon’s research interests centre around biomedical imaging. His major contributions have been in developing systems for positron emission tomography (PET), including designing the microPET scanner for small-animal imaging, the earliest examples of simultaneous PET and MRI studies and co-leading the EXPLORER consortium that developed the world’s first total-body PET scanner. Simon is a Fellow of the Royal Society and Member of the U.S. National Academy of Engineering and National Academy of Inventors. He has received several international awards including the Society of Nuclear Medicine and Molecular Imaging Cassen Prize in 2022. He served as editor-in-chief of the journal Physics in Medicine & Biology from 2011–2020. Simon is the author of more than 350 peer-reviewed journal articles, review articles and book chapters in the field of biomedical imaging. He is also lead author of the widely-used textbook Physics in Nuclear Medicine.

Xinchen (Alison) Deng, PhD, is a diagnostic imaging physics resident at The Ohio State University. Her research focuses on advancing photon-counting CT for clinical applications, including multi-contrast imaging, early disease detection, detector optimization, and AI-based improvements in image quality. Before beginning the residency, Xinchen (Alison) was a postdoctoral fellow in medical physics at the University of Victoria, where she worked with Magdalena Bazalova-Carter and collaborated with Redlen Technologies on the optimization of photon-counting detectors and the clinical applications of spectral CT. She received her PhD in medical physics from the University of British Columbia, where she applied Raman spectroscopy, machine learning, and explainable AI to study radiation treatment response in cancer cells and tissues. Her broader research background spans photon-counting CT, spectral X-ray imaging, Raman spectroscopy, radiotherapy response assessment, and AI-driven image and data analysis. Her photon-counting CT research has been featured in the Physics in Medicine & Biology Early Career Researcher Focus Collection. She is a finalist for the PMB Early Career Researcher Award.

About this journal

Physics in Medicine & Biology. The international journal of biomedical physics and engineering, published by IOP Publishing on behalf of the Institute of Physics and Engineering in Medicine (IPEM)

Editor-in-chief: Katia Parodi, Ludwig-Maximilians University, Munich, Germany

 

 

The post Shaping the field of medical imaging: a fireside chat with Simon R Cherry and Xinchen Deng appeared first on Physics World.

A new study has revealed that sensitive health information about individual patients can be extracted from medical artificial intelligence (AI) models more effectively than previous research had indicated.

Diagnostic AI models are used to determine whether some given health data belong to a specific class – for example if an X-ray image shows evidence of pneumonia – and offer huge promise for improving patient outcomes. Earlier studies showed that membership inference attacks (MIAs), which determine whether a particular person’s data was used to train an AI model, yielded results no better than random chance. But these previous studies only evaluated the average risk across all patients in a training dataset.

By contrast, in this latest work doctoral student Moritz Knolle and his supervisor Daniel Rückert at the Technical University of Munich (TUM), together with collaborators at Imperial College London and Georg Kaissis from the Hasso Plattner Institute (HPI), examined the privacy risk for an individual patient. “Privacy that only works on average has little meaning. So I asked myself: would I contribute my data to train a medical AI model?” says Knolle.

As detailed in their recent Nature paper, the researchers attacked AI models based on seven different types of real-life medical datasets, including one comprising 21,799 electrocardiograms and another of 377,110 chest radiographs.

“If a model sees a specific data point during training, it usually becomes more confident at predicting it. So in these MIA attacks I compare the predictive probability number for this data point outputted by the medical AI model with the predictive probability number obtained from my model which has not seen that data point,” explains Knolle, adding that the attack is essentially a statistical test to distinguish between the two models.

Their results revealed that the privacy risk for an individual was far greater than the average risk. The team also found that privacy risk is not equally distributed among subgroups.

“We found that patients from minority groups that are under-represented in the training dataset tend to be over-represented among the 99 percentile of extreme [privacy] risk,” Knolle tells Physics World, warning that as medical AI models become larger and more sophisticated the privacy risk is amplified because their deep analysis creates the small changes in predictive probability that MIAs are good at finding.

While an MIA that reveals someone is a UK citizen poses little security risk, discovering that an individual has received a specific type of cancer treatment reveals personal information that could, for instance, adversely affect their medical insurance premiums. “The more specific the training cohort is, the larger the privacy risk,” states Knolle, crediting his medical doctor parents with inspiring his desire to improve healthcare AI.

Countermeasures include differential privacy (DP), in which a small amount of Gaussian random white noise is added in alongside the data during training, thereby reducing the effectiveness of MIAs while maintaining the model’s diagnostic accuracy. “This gives a mathematically provable upper bound for how much privacy risk any individual in this dataset could face, by limiting the information content that flows to the AI model,” explains Knolle.

He notes that, unfortunately, such DP tools have not been widely adopted by hospitals or clinics. This, he fears, could undermine patients’ confidence in sharing their data, and thereby not only reduce AI’s ability to help combat rare diseases, but also compound AI discrimination that occurs when there is too little data from minority groups included in training datasets.

“So we are hoping to inspire the medical AI community to do risk assessments before deploying AI models, and use verifiable strategies such as DP or limiting access to the AI model, so that as an individual you have a statistical guarantee that no harm would come to you by being part of a dataset,” concludes Knolle, who now plans to investigate the privacy risks posed by generative AI models for healthcare.

The post Privacy risks from medical AI are greater than previously thought appeared first on Physics World.

A collaboration of researchers in North America has reported their findings from the first diagnostic X-ray imaging performed in space. X-ray images recorded during an orbital spaceflight exhibited similar overall quality to those taken preflight on Earth, demonstrating the feasibility of adding radiography as a diagnostic tool for crew health.

With humans venturing further into space and spending more time in this harsh environment, the risk of adverse medical events increases. Accurate and timely diagnosis and treatment is a must-have. Currently, the only reliable medical imaging modality available to astronauts in spaceflight is ultrasonography – which is operator dependent, requires substantial training and relies on limited acoustic windows.

X-ray imaging, on the other hand, benefits from low operator dependence, rapid acquisition and typically higher spatial resolution than ultrasound. Digital radiography could also offer superior diagnostic capabilities for many medical conditions of concern during spaceflight, including dental disease, musculoskeletal trauma, inhalational injury, collapsed lung and arthritis.

Traditional X-ray scanning systems are bulky, produce a lot of radiation and are sensitive to motion – making them less than ideal for sending into space. But a new generation of portable X-ray scanners shows potential to survive the spaceflight and be operated by crew members with minimal training.

“It’s been a dream for aerospace medicine to have more than one imaging modality for diagnosing illnesses and injuries in space,” says lead researcher Sheyna Gifford, an assistant professor of aerospace medicine at the Mayo Clinic in Rochester, MN, in a press statement.

Scanning in space

In a study led by co-author David Lerner, the researchers first demonstrated the feasibility of X-ray imaging in simulated microgravity in 2022. During a parabolic flight, they used a commercial off-the-shelf digital radiography system to successfully capture radiographs of a human subject and a phantom during lunar gravity (1/6g), Martian gravity (1/3g) and microgravity.

In this latest project, reported in in Radiology, Gifford and her team investigated the performance of a similar portable digital radiography system aboard the Dragon spacecraft, during the Fram2 spaceflight mission, a 3.5-day polar orbital flight launched on 31 March 2025 on a SpaceX Falcon 9 rocket. The imaging system comprised a commercial off-the-shelf, FDA-cleared, portable, digital X-ray generator (Impact Wireless; MinXray) along with an FDA-cleared flat-panel detector.

Prior to the mission, three crew members received 4 hr of training on the portable X-ray system and acquired preflight X-ray images of the hand, forearm, abdomen, pelvis, chest and a quality control phantom. They then recorded the same anatomic and phantom images whilst in space, and also imaged a smartwatch to investigate the potential for non-destructive testing.

During anatomic imaging, the devices were handheld by the crew members, while the phantom and smartwatch were secured to the detector for imaging. The crew reported that they found it easy to use the X-ray system and follow the imaging protocols.

Back on Earth

To assess the quality of the seven X-ray images acquired by the crew, three independent radiologists evaluated and compared the preflight and in-flight anatomic radiographs, finding no differences in overall image quality, contrast resolution or spatial resolution. For central-body radiographs (chest, abdomen and pelvis), image positioning was worse in-flight than preflight. Despite this, all X-rays achieved good or near-excellent diagnostic quality (rated on a  5-point Likert scale).

Phantom radiographs recorded in space exhibited good spatial and contrast resolution, imaging low-contrast targets down to the smallest 2-mm target and easily visualizing high-contrast meshes ranging from 20 to 60 lines per inch, with 80 lines per inch remaining visible. The smartwatch radiograph clearly showed the watch’s internal components at the submillimetre scale.

The research team found that the X-ray generator had sustained minor re-entry damage, but its internal components and X-ray output were not affected. Similarly, the detector passed visual inspection and quality control tests. The researchers also recorded a set of postflight X-rays (on non-crewmembers) following the same in-flight protocols. These showed no qualitative differences from the preflight or in-flight radiographs.

“By acquiring the first human and equipment X-rays in space, our study demonstrates the feasibility of in-orbit radiography and expanded diagnostic capabilities for crew health and hardware evaluation,” says Gifford. “Acquiring diagnostically useful X-rays in space is something that anyone can do. Three very talented nonmedical people with four hours of training in one of the harshest environments did it right and did it well.”

Gifford points out that a spaceflight-ready radiography system could also prove indispensable for mission-critical non-medical tasks. “For sustained human presence in space, X-rays are critical not just for crew members but also for other mission components like electronics and spacesuits. The only way to look inside these objects without taking them apart is to X-ray them,” she explains.

The post First medical X-ray images taken in space achieve diagnostic quality appeared first on Physics World.

A new glass screen containing nanoclusters of copper iodide could create higher-resolution X-ray images using a much lower dose of radiation. The scintillating screen, which also works underwater, can be moulded into curved shapes, overcoming key limitations of the conventional rigid, flat-panel detectors used for many medical imaging applications. These include mammography, say the researchers at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, and the University of Hamburg, Germany, who developed it.

X-ray scintillators capture X-rays passing through an object and convert them into flashes of visible light to form a digital image, explains physicist Mehmet Bayindir of KAUST and the University of Hamburg. The more efficient the scintillator is at this conversion, the clearer the final image and the lower dose of radiation required to generate it.

He and his colleagues’ detailed analyses of a scintillator containing nanoclusters of copper, iodine and an organic ligand revealed a fascinating phenomenon: its photoluminescence and radioluminescence pathways appear to have distinct physical origins. This implies that the scintillating light yield – that is, how much visible light a material emits when struck by high-energy X-rays – can be separated from its standard photoluminescence quantum yield (how efficiently the material converts absorbed ultraviolet or visible light into a different wavelength of light).

Until now, Bayindir explains, it was assumed that a material must have high photoluminescence efficiency to perform well when excited by X-rays. “Breaking this assumption directly challenges conventional wisdom and opens up a promising new design space for making high-performance scintillators – provided that materials highly responsive to radiation can be engineered of course.”

Enter amorphous quantum systems

In recent years, researchers have been looking into cubane-type [Cu₄I₄L₄, L: organic ligand] nanocluster glasses (NCGs) for use in this context. These belong to a family of hybrid transparent luminescent amorphous materials that show much promise for X-ray scintillation applications.

In their latest work, detailed in ACS Energy Letters, Bayindir and colleagues studied several stable alkyl-phosphine ligand-based NCGs. In these amorphous zero-dimensional quantum systems, ionizing radiation is mostly absorbed by the heavy iodine and copper elements at the cluster cores. The researchers used these NCGs to make large-area, free-standing scintillating screens – both planar and conformal – with ultrasmooth surfaces, using a fabrication methodology that they developed in their lab.

The screens are robust and can image samples – both electronic and biological – with sub-3 µm spatial resolution. They also work underwater, something that is difficult for conventional screens. The researchers demonstrated this by capturing a very clear scan of a fish’s tail in water that was indistinguishable from an image taken in air.

Such a glass scintillator represents a significant milestone, says team leader Osman Bakr. “Indeed, the level of precision we obtained allows us to capture highly detailed, high-contrast X-ray images of complex internal microstructures, such as the delicate anatomy of a microscopic insect and an electronic memory card, to cite but two examples,” he says.

X-ray imaging systems that curve to fit a person’s body

“Combined with the fact that this new nanocluster glass becomes almost rubbery when heated to 42 °C, we can mould it into any three-dimensional shape at low temperatures,” study lead author Bashir Hasanov adds. “This could allow for X-ray imaging systems that curve to fit a person’s body and overcomes the limitations of conventional rigid, flat-panel detectors.”

One possible application is in mammography, he notes. Today, a major drawback of current mammography machines is that breast tissue must be firmly compressed between flat panels so that it can be imaged. This is very uncomfortable – and even painful – and deters many women from going for a scan.

“One in 20 women around the world will be diagnosed with breast cancer in their lifetime, with annual cases projected to reach 3.2 million and related deaths climbing to 1.1 million by 2050,” says Bayindir. “Many of these fatalities stem from late-stage diagnosis.

Nearly one-third of women miss their initial mammogram appointments, something that significantly increases their risk of succumbing to the disease. “While the reasons for this vary, the medical literature frequently cites the physical discomfort of tissue compression,” says Bakr. “What is more, because current guidelines recommend starting routine mammography screenings at age 40, cumulative lifetime exposure to X-ray radiation remains a clinical concern for many. Our long-term objective is to completely redefine this clinical experience.”

Indeed, the researchers say they would now like to bring this quantum glass out of the lab and into real-world applications. Bakr tells Physics World that his team’s immediate next step is to scale up the geometric dimensions of its mouldable screens while maintaining perfect thickness and clarity. “This would move us closer to full-scale clinical prototyping for 3D conformal medical imaging.”

“We are also designing a novel detector architecture capable of high-resolution imaging on curved surfaces using an array of specialized optical sensors,” he reveals.

The post Mouldable glass screen could make mammograms clearer and much more comfortable appeared first on Physics World.

Epithelial cancers account for 80% to 90% of all cancer cases in the world, comprising a wide variety of malignancies that begin in the epithelial tissues that form the skin and line the body’s internal passageways and organs. Early diagnosis is critical to improving patient outcomes, but is limited by a heavy reliance on invasive biopsies.

Simultaneous assessment of changes in both the cell nuclei and the microvasculature could potentially improve the accuracy of early cancer and precancer detection, and help guide decisions regarding biopsy and pathology testing. In vivo microscopy (IVM) has shown promise for aiding early diagnosis of epithelial cancers, but has challenges relating to image quality, spatial resolution and field-of-view (FOV).

PrecisionView, an artificial intelligence (AI)-powered, dual-modality endomicroscope in development at Rice University’s Rice360 Institute for Global Health Technologies, addresses these IVM issues. The compact, handheld, easy-to use system enables real-time, wide-area visualization of both subcellular structures and underlying blood vessels, offering higher-quality imaging, a fivefold increase in FOV, and an eightfold increase in spatial resolution over endomicroscopic systems in current clinical use.

The proof-of-concept version of PrecisionView, described in Proceedings of the National Academy of Sciences, is intended for superficial epithelial imaging where scattering-induced distortions remain moderate. And it is specifically designed to be low cost: providing an affordable diagnostic tool for clinics worldwide.

PrecisionView uses simple, off-the-shelf miniaturized optics integrated with a deep learning-based optimization framework. Its AI-supported phase mask design and reconstruction algorithm incorporate dual-modality fluorescence and reflectance imaging to achieve a 5.2 x 3.9 FOV and a 500 µm depth-of-field (DOF) while maintaining cellular-level resolution (4 µm). Images are acquired at approximately 15 frames/s, in 2 s intervals, with image tiles stitched together to form a large-scale, co-registered map of cell nuclei and microvasculature.

Performance assessment

To validate the system, principal investigators Rebecca Richards-Kortum and Ashok Veeraraghavan, and collaborators at the University of Texas MD Anderson Cancer Center, analysed images of ex vivo porcine tongue, human breast and fresh cervical tissue specimens with precancerous lesions.

High-resolution maps of cervical specimens containing squamous intraepithelial lesions (precancer) clearly delineated key anatomic structures, including columnar and squamous epithelium, and the junction between the two epithelial cell types. PrecisionView clearly distinguished abnormal regions from surrounding benign tissue – suggesting that the system may be able to support physicians’ decisions regarding whether or not to conduct a biopsy.

The researchers also performed real-time, in vivo imaging of the oral cavities of healthy volunteers. They report that the system’s large DOF enabled acquisition of high-resolution images of cell nuclei and microvasculature across a large tissue area without requiring refocusing. When the mucosal surface was scanned, the large FOV of each frame, combined with the high video acquisition rate, provided substantial frame-to-frame overlap. This allowed seamless stitching of the dual-modality images to create large-scale, co-registered maps of the oral mucosa, which showed distinct cellular and vascular features.

Meeting a global need

The Rice 360 Institute focuses on designing, implementing and scaling highly effective, low-cost medical technologies for underserved and low-resource communities. The idea for PrecisionView grew out of recognizing a global limitation in access to high-quality diagnostic information.

“In many care settings, especially where conventional pathology resources are limited, clinicians may not have timely access to the cellular- and tissue-level information needed to guide diagnosis and treatment decisions,” says first author Huayu Hou, who worked with co-first author Jimin Wu, to develop the PrecisionView instrument.

Hou explains that although existing clinical endomicroscopy systems can provide valuable microscopic information, their broader use is often limited by cost, complexity, a limited imaging volume and the ability to capture only a narrow range of diagnostic features. Such constraints reflect fundamental design trade-offs in compact clinical optical imaging systems and create significant barriers to practical point-of-care use.

“PrecisionView was motivated by the opportunity to use computational imaging to overcome limitations in existing clinical endomicroscope designs that could not be fully addressed with traditional optical approaches alone, with the goal of bringing high-quality diagnostic information closer to the point-of-care,” he says.

Hou advises that it took about a year to develop the first working prototype, and that several projects are now underway to further enhance both performance and usability for point-of-care applications. “We are working to improve image quality and resolution, so that the system can provide finer cellular details from living systems to real time,” he adds. “We are working to further refine the hardware and workflow to make the device easier to operate in clinical settings.”

The team is developing AI-based classification algorithms that can integrate both cellular and vascular information and provide real-time diagnostic guidance. Its long term goal is to create a system that not only visualizes tissue at the cellular level, but also helps clinicians interpret the information efficiently at the point-of-care. “This capability could be especially valuable in low-resource settings, where access to pathology expertise may be limited, and tools that support real-time decision-making could significantly improve clinical usability and impact,” notes Wu.

Clinical validation studies to evaluate PrecisionView’s diagnostic performance across different clinical settings and cancer types are also planned. For instance, a study focusing on head-and-neck cancer detection has already started in collaboration with MD Anderson Cancer Center. Others are being developed for cervical cancer imaging in Mozambique, to evaluate the system’s performance in low-resource settings.

The post Affordable AI-powered endomicroscope lines up for early cancer detection appeared first on Physics World.

Ronan Smith, a postdoctoral research fellow at Adelaide University, has been chosen as the winner of this year’s Physics in Medicine & Biology (PMB) Early Career Researcher Award. The award is presented to the author of the “best paper” in PMB’s 2025 Early Career Researcher Focus Collection, as selected by the journal’s editorial board.

Smith’s research involves implementing X-ray velocimetry (XV), a novel imaging method that uses X-rays to track lung motion during breathing and create 3D maps of local ventilation. In his award-winning paper, Visualising ventilation changes following endobronchial valve placement with x-ray velocimetry functional lung imaging, Smith investigates the potential of XV imaging to detect changes in lung function after insertion of an endobronchial valve (EBV).

EBVs are one-way valves that are placed into the lung to help treat emphysema – a condition that damages air sacs in the lungs, causing air to get trapped inside and making breathing difficult. The EBV, which in some cases can be used in place of surgery, prevents airflow into damaged lung areas so that the rest of the lung can function more effectively.

Successful valve placement causes the targeted area of lung to collapse, which can be imaged using CT. Smith proposed that use of XV functional lung imaging to non-invasively measure regional and local changes in airflow could more accurately assess the clinical impact of EBV placement.

“The lungs are a dynamic organ, their job is to be constantly moving,” he explains. “Because X-ray velocimetry looks at lung motion, it lets us see exactly where the air is or isn’t flowing, so you can instantly see that airflow has changed within the lungs. CT only measures structural changes, which may not necessarily be correlated to changes in lung function.”

In vivo demonstration

To investigate this potential advantage, Smith and colleagues carried out a pilot study on healthy sheep, which have a similar lung size to humans. They performed XV imaging on two anaesthetized and ventilated animals, before and after placing EBVs in their lungs.

The XV scanning process involves recording fluoroscopic videos of individual breaths at various angles around the lung, with anatomic positioning provided by an accompanying breath-hold CT scan. To analyse the data, the researchers used XV LVAS software from 4DMedical, the MedTech company that developed and commercialized the XV technology.

The software correlates motion in the XV videos with CT data to measure the lung’s expansion and contraction during a breath cycle. It creates a 3D map of specific ventilation (the change in voxel volume during a breath, divided by its starting volume) in small voxels throughout the lungs. This map can then be used to calculate mean specific ventilation and ventilation heterogeneity across a given lung region.

As soon as the EBVs were inserted into the animals’ lungs, XV imaging could visualize and quantify a reduction in airflow to areas downstream of the valves. This effect was seen both in regions where collapse was visible in CT scans, as well as those where collapse could not be detected by CT. Ventilation changes were also clearly observed in the remainder of the lungs.

“The main finding of our study was that X-ray velocimetry imaging can detect airflow changes from endobronchial valve placement in the lungs,” says Smith. “Our research could be really important for people [with emphysema], as tools to help with better placement and verification will lead to improved treatment options.”

Future prospects

Since the publication of this paper, Smith has focused on further applications of pre-clinical and clinical XV imaging. “I’ve been working as part of a great interdisciplinary team looking at how lung function changes in a range of diseases, to both understand the diseases, and as an outcome measure when we test treatments,” he says.

This work includes the world’s first paediatric clinical trial of XV imaging, which is examining the feasibility of using the technology in children with cystic fibrosis. The researchers have imaged around 30 children to date and aim to publish their findings later this year. They are currently planning future studies to see how XV imaging could enhance clinical decision making and improve outcomes for these children, as well as looking at other childhood diseases where it could be of relevance.

“As an early-career researcher, I’m also focussing on developing my own research, looking at another novel X-ray imaging method called dark-field X-ray imaging,” Smith adds.

The perfect award

Smith tells Physics World that he was excited to receive the PMB Early Career Researcher Award, acknowledging the efforts of everyone involved in this “hugely collaborative project”, including clinicians, scientists, 4DMedical and the staff in the preclinical imaging facility where the study was performed.

“As a physicist working in medicine/biology, it feels like the perfect award to get,” he says. “It’s great to see interest in the work we are doing, and fantastic evidence we can use to convince the funding bodies it’s worth continuing this work.”

• The PMB Early Career Researcher Focus Collection 2025 publishes papers from early-career researchers (defined for this collection as a postgraduate student or someone who completed their PhD in 2019 or later) in biomedical physics, with the aim of highlighting research excellence from emerging leaders. The Early Career Researcher Award was introduced to further recognise an outstanding contribution from one of the early-career authors, selected according to the quality of scientific content, number of citations and downloads, and peer review ratings.

The post X-ray velocimetry study earns Ronan Smith the PMB Early Career Researcher Award appeared first on Physics World.

A new study strengthens the case for exercise as a critical way to protect the brain from age-related decline, including dementia and Alzheimer’s disease. During sleep, the brain activates a cleanup network that helps remove accumulated waste. A review led by Victoria University (VU) researcher Dr. James Broatch examined whether exercise could strengthen this glymphatic [...]

The European Union has begun delaying the release of satellite images showing shipping lanes near the Strait of Hormuz by 24 hours. The move comes in response to an earlier US government request and coincides with the Trump administration ramping up the war with Iran once again.

The unusual change in the free and open data policy for Europe’s Copernicus Earth observation program was revealed by Space News, which reviewed a copy of the decision made by the Council of the EU on July 13. The ordered delay applies to images taken by Sentinel-1 and Sentinel-2 satellites and covers the Gulf of Oman region that includes shipping lanes leading to and from the contested Strait of Hormuz.

The US government first requested that the European Union restrict Copernicus satellite imagery on May 26, according to the decision document seen by Space News. That request occurred during a shaky ceasefire period for the war that began on April 8 and ended on July 8, when the US military resumed a broader campaign of military strikes following an announcement by President Donald Trump.

Read full article

Comments

© European Union | Copernicus Sentinel-2

The European Union has begun delaying the release of satellite images showing shipping lanes near the Strait of Hormuz by 24 hours. The move comes in response to an earlier US government request and coincides with the Trump administration ramping up the war with Iran once again.

The unusual change in the free and open data policy for Europe’s Copernicus Earth observation program was revealed by Space News, which reviewed a copy of the decision made by the Council of the EU on July 13. The ordered delay applies to images taken by Sentinel-1 and Sentinel-2 satellites and covers the Gulf of Oman region that includes shipping lanes leading to and from the contested Strait of Hormuz.

The US government first requested that the European Union restrict Copernicus satellite imagery on May 26, according to the decision document seen by Space News. That request occurred during a shaky ceasefire period for the war that began on April 8 and ended on July 8, when the US military resumed a broader campaign of military strikes following an announcement by President Donald Trump.

Read full article

Comments

© European Union | Copernicus Sentinel-2

A new study reported in BMC Geriatrics spotlights cognitive reserve—the brain’s lifelong ability to adapt—as a potential resilience factor for how older adults experience frailty. The research frames frailty not as a fixed state, but as a dynamic process that can worsen or improve over time, with cognition playing a key role in that trajectory.

Frailty affects mobility, independence, and health outcomes, and clinicians increasingly treat it as something that evolves. Yet, why some people regain strength after health setbacks while others decline remains an open question. Zhao, Liau, Ma and colleagues investigate whether individuals who build stronger cognitive reserve across their lifespan may be better protected.

The team examines frailty progression and recovery in later life using longitudinal data, allowing participants to move between clinical frailty states rather than being categorized once and followed as static cases. This design is crucial for capturing real-world resilience, where improvement can occur after hospitalization, illness, or functional decline.

Cognitive reserve is often linked to differences in brain structure and function, as well as to lifelong experiences such as education, complex work, and engagement in mentally demanding activities. In the study’s framing, higher cognitive reserve may support more efficient neural processing and compensatory mechanisms, even when aging-related changes begin to accumulate.

Technically, the analysis explores associations between cognitive reserve measures and the odds of frailty worsening or recovering across follow-up. By considering longitudinal changes, the authors aim to isolate whether cognitive reserve is correlated with healthier trajectories, not merely with baseline health.

If validated, these findings could reshape how risk is assessed in geriatrics. Instead of focusing solely on current impairments, clinicians may consider cognitive reserve as part of a broader resilience model that includes cognitive health alongside physical function.

The implications extend beyond individual prediction. Cognitive reserve is influenced by modifiable factors, suggesting that public health strategies promoting lifelong learning and cognitively stimulating environments might contribute to better aging outcomes.

With its 2026 publication in BMC Geriatrics, the study adds to a growing evidence base connecting cognitive aging with systemic vulnerability. The authors argue that resilience is not only physical—brain adaptability may also buffer older adults against the downward cycle of frailty.

Finally, the study offers a viral-science-friendly takeaway: your brain’s “practice” over decades may help determine whether frailty becomes a chronic decline or a condition with room for recovery.

Subject of Research: Frailty progression and recovery in later life; cognitive reserve as a resilience factor.

Article Title: Lifelong cognitive reserve as a resilience factor for frailty progression and recovery in later life.

Article References: Zhao, M., Liau, S.J., Ma, X. et al. Lifelong cognitive reserve as a resilience factor for frailty progression and recovery in later life. BMC Geriatrics (2026). https://doi.org/10.1186/s12877-026-07919-4

Image Credits: AI Generated

DOI: 10.1186/s12877-026-07919-4

Even moderate amounts of walking may help protect brain function and slow changes linked to Alzheimer’s disease. New research reveals a modest amount of daily movement may help protect the brain from Alzheimer’s disease, but the apparent benefit does not involve the biological target researchers expected. Using pedometers, researchers followed nearly 300 older adults with [...]