A new study reveals how aging changes the immune system in complex ways and identifies potential targets for healthier aging. Why does immunity weaken with age, and does that decline happen the same way throughout the body? Researchers at USC approached that question by looking across the body rather than focusing on a single organ. [...]

Editor’s Note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at astrobites.org.

Title: The Exozodi Spectral Effect: Residual Habitable Zone Dust May Bias exoEarth Characterization
Authors: Miles H. Currie et al.
First Author’s Institution: NASA Goddard Space Flight Center
Status: Published in AJ

We have dust in our solar system — a huge cloud of it that permeates every corner. The dust itself is thought to be the product of many different processes including comet tails, collisions between asteroids, leftover debris from planetary formation, and gravitational collisions induced by planets. It’s small (on the order of 10–100 microns), made up of different silicates, and creates a noticeable triangle-shaped light that can be seen on the darkest nights (see Fig.1). This zodiacal dust is aptly named since the scattered light from the dust grains follows the line of the zodiac, or the ecliptic plane, across the sky. Its structure is quite complicated, with dust rings and trails and clumps near larger planetary bodies. For more information on the solar system’s zodiacal dust, you can read these bites here and here!

This zodiacal dust is not exclusive to the solar system. Other star systems, such as Tau Ceti and Vega, have been found to host similar clouds of interplanetary dust. This means planets imaged in these systems will also have emission from this dust mixed into the light that we observe from the planets. The Habitable Worlds Observatory (HWO) is a future space telescope mission under development by NASA. One of its primary science goals is to directly image 25 Earth-like planets in the habitable zones of nearby stars and look for signs of life (or biosignatures) in these planets’ atmospheres. Exozodiacal dust (aka exozodi) represents the biggest source of astrophysical noise that will need to be subtracted out from HWO observations of exoEarths. The authors of today’s article look at the potential impact of exozodi emission on the spectra of exoEarth atmospheres.

Show Me the Zodi

The authors first assume four different models of exozodi dust and how the dust’s emission changes with wavelength. The first model assumes a dust cloud that acts as a gray scatterer, meaning the particles scatter all wavelengths of light with no preference, and the second model’s dust acts just like the solar system’s does. However, it’s unknown whether the composition of exozodi will be the same as the solar system’s. Therefore, they adopt two more models of exozodi that act just like debris disks that have different compositions. One model is bluer in color because it is made up of smaller particles that preferentially scatter blue light, and the other model is redder in color because it is made up of bigger dust particles or different chemical compositions. Each of these models over HWO’s wavelength range can be seen in Figure 2. The authors then simulate a modern Earth atmosphere (both clear and 50% cloudy) as well as an Archean Earth atmosphere to understand how different exozodi models will affect observations of different exoplanet atmosphere conditions and compositions.

There’s Dust in My Spectra

In general, the effect that zodiacal dust has on the spectra of Earth-like planets can be broken down into two categories: its effect on the continuum and on the spectral features. The continuum is the object’s light emitted over all wavelengths and uninterrupted by spectral features. Spectral features are absorption and emission lines. These create either a dip or peak in the spectrum where atoms have either absorbed or emitted light at that particular wavelength. Lines at specific wavelengths tell us that a certain material is there, providing a list of possible chemicals that could be present in the planet’s atmosphere. For a brief summary of spectroscopy more generally, see here.

In the case of the continuum, exozodiacal emission raises the continuum, meaning the base flux of the planet is raised and absorption lines become more shallow. These effects can be seen in Figure 3, where the authors’ simulated spectra are plotted with and without zodiacal dust included. Not only is the continuum elevated for all models including zodiacal dust, notice how the dips become shallower at longer wavelengths. Because dust primarily radiates thermally, the red debris disk model nearly smooths out all dips beyond 1.25 microns! Molecules that absorb at these longer wavelengths, such as water or methane, may not be correctly identified with this type of exozodiacal emission.

Additionally, the slope of the spectra between 0.25 and 0.75 microns (called the Rayleigh scattering slope) becomes shallower with zodiacal dust emission, which may bias our models toward lower-molecular-weight atmospheres. The slope at these wavelengths is dependent on the size distribution of molecules in the atmosphere, meaning it can tell us how much the average molecule in an atmosphere weighs.

From Figure 3, the effects of zodiacal dust are minimized with a bluer zodiacal dust model. This means systems with “bluer” zodiacal dust may be more optimal for HWO observations, as bluer dust does not scatter as much starlight as gray, solar system–like, or red dust. From their results, the authors recommend that the residual exozodi-to-planet flux ratio (in the V-band wavelengths) be kept to less than 10^-2 in order to correctly understand the line depths (identify if a molecule is present or not) and 10^-3 in order to accurately retrieve molecular abundances (understand how much of a molecule is present in the atmosphere). Dust is pesky enough here on Earth, but to find life on another planet like ours, we’ll dust have to deal with it.

Original astrobite edited by Anavi Uppal.

About the author, Kaz Gary:

I am a fourth-year PhD candidate at The Ohio State University with a passion for planets. My current work focuses on modeling exoplanet observations for the Habitable Worlds Observatory and understanding planetary atmospheres. Outside of research, I help develop planetarium shows and love all forms of science communication. In my free time, I enjoy playing tabletop RPGs, painting, watching terrible reality TV, and hanging out with my pet hedgehog.

The post Dust Deal with It appeared first on AAS Nova.

The small trial suggests some of creatine’s muscle benefits may not require structured training.

A familiar diabetes pill could hold the power to slow the biological forces that leave the body increasingly vulnerable to disease as it ages. What if a drug already prescribed to millions of people could do more than control blood sugar? For years, researchers have wondered whether metformin, one of the world’s most widely used [...]

Billions of people still suffer from hunger and food insecurity worldwide. Meanwhile, microbial contamination causes a third of global food waste annually, resulting in major economic losses and illnesses. Although antibiotics and pesticides help control contamination, their excessive use can promote bacterial resistance or leave toxic residues. 

One alternative uses materials that generate highly reactive forms of oxygen when exposed to light, called photocatalysts, to prolong food freshness. These molecules kill bacteria by damaging their cell components in a process called photodynamic inactivation or PDI. However, current PDI systems rely on energy-intensive lasers and long exposure times because the bacteria’s protective cell membranes block reactive oxygen molecules. As a result, the systems don’t kill bacteria effectively and risk overheating food, affecting its freshness and nutritional value.

Scientists at Zhejiang University in China thought that it would be possible to achieve a PDI system using accessible, free sunlight with the right photocatalyst. Moreover, they also wanted to integrate this photocatalyst into food packaging to combat microbes during storage. 

To make the photocatalyst, the researchers mixed water with a glue-like plant fiber that provides structural support, called pectin, and a chemical from plant seeds that supplies phosphorus, called phytic acid. Then they heated the mixture in a sealed reactor at 220ºC (428ºF) for 5 hours to form tiny structures composed of a carbon core surrounded by a phosphorus-rich shell called phosphorus-modified carbonized polymer dots or PCDs.

To incorporate the PCDs into film packaging, the researchers dissolved equal amounts of a plant fiber from konjac, called glucomannan, and another plant-derived molecule, called carboxymethylcellulose, in warm water to form a network of entangled molecular chains. Then, they added 1.5 milligrams of PCD per milliliter to this mixture, which linked the chains together like molecular fasteners. Finally, they added a moisture-binding chemical called glycerin to prevent brittleness. The researchers cast this mixture onto separate Petri dishes and dried them, forming thin, plastic-like PCD films.

To test the films, the researchers placed Escherichia coli bacteria between 2 sterilized film sheets and exposed the samples to either darkness or simulated sunlight for 20 minutes. They then transferred the microbes onto agar plates and incubated them for 15 hours, allowing surviving bacteria to form visible colonies. They compared the number of bacterial colonies with those of untreated samples to calculate the percentage of bacteria the films eliminated in each environment. 

Tests under darkness showed that the films eliminated about 60% of the bacteria. The researchers attributed this result to the positive electrical charge of PCDs, which attracts the bacteria’s negatively charged cell surfaces. The electrical attraction stresses their cell membranes, interfering with essential life processes. This result suggested that the films use a separate charge-based antibacterial mechanism apart from PDI, which requires light to work.

Under simulated sunlight, the films eliminated nearly 100% of the bacteria. The researchers identified 2 principal mechanisms causing this. First, the PCDs acted as tiny heaters that generated localized heat, which stretched holes in the bacterial membranes, allowing contents to leak out of their cells through a process called photoporation. Second, the PCDs generated reactive oxygen molecules that could then penetrate the bacteria. Once inside the cells, these molecules irreversibly damage the bacteria’s internal components and genetic material through PDI. 

The researchers also investigated whether PCD films could serve as food packaging by using them to wrap fresh strawberries. Compared with conventional nonbiodegradable polyethylene packaging where visible rotting occurred, the PCD films kept the strawberries fresh for more than 4 days by suppressing bacterial growth and regulating moisture levels. When the team tested whether the films were toxic, they found that the films are safe for humans and compatible for food applications. They also buried the films in soil for 24 days and found that they degraded by 78%, demonstrating biodegradability suitable for packaging fresh foods.

The team concluded that PCD films could be a biodegradable food packaging that combines plant-based materials with multiple antibacterial strategies. The films retained antibacterial activity in darkness and became more effective under sunlight through the combined effects of photoporation and PDI. These findings demonstrated that by combining multiple mechanisms into a single material, researchers can create multifunctional food packaging that remains effective across different storage conditions.

The post Harnessing sunlight to fight bacteria appeared first on Sciworthy.

Parkinson’s disease has long been a diagnosis of observation and inference. Doctors watched for tremor, rigidity, and slowness of movement, and only at autopsy could the telltale clumps of alpha-synuclein protein—the pathological signature of the disease—be confirmed inside the brain. That diagnostic fog may finally be lifting. A new five-year study drawing on the landmark Parkinson’s Progression Markers Initiative (PPMI) has followed patients whose disease was defined not by their symptoms alone, but by biology: a positive cerebrospinal fluid seed amplification assay, the test that detects misfolded alpha-synuclein circulating in the nervous system. The results, published in Annals of Clinical and Translational Neurology, offer one of the clearest longitudinal pictures yet of how biologically confirmed Parkinson’s disease actually progresses in the era of modern treatment—and the findings are already reshaping how scientists think about staging, enrollment in clinical trials, and the very definition of the disease.

The research team set out with two central questions. First, what happens clinically to patients who test positive for synuclein seeding in their spinal fluid over five years of careful observation? Second, does a patient’s baseline stage on a new biological staging system—the Neuronal Synuclein Disease Integrated Staging System, or NSD-ISS—predict how quickly they will cross meaningful clinical milestones? Both questions cut to the heart of a quiet revolution in neurology. For more than a century, Parkinson’s was classified by what patients looked like in the examination room. Now, thanks to validated biomarkers, researchers can classify it by what is happening at the molecular level, potentially years before disabling symptoms emerge.

The seed amplification assay, or SAA, is the technological engine behind this shift. The test exploits a peculiar property of misfolded alpha-synuclein: it acts as a template that recruits normal, healthy synuclein proteins and forces them to misfold as well, seeding the aggregates known as Lewy bodies that riddle the brains of Parkinson’s patients. In the laboratory, a tiny sample of cerebrospinal fluid is mixed with synthetic alpha-synuclein and monitored for hours. If pathological seeds are present, the reaction accelerates into a detectable fluorescence signal. The assay has been validated across multiple international cohorts and, critically, against postmortem brain tissue, giving neurologists a window into pathology they previously could only glimpse after death. A complementary technique—detecting phosphorylated alpha-synuclein in small skin biopsies—has added a second, less invasive line of biological evidence.

Armed with these tools, two research groups have proposed frameworks for redefining Parkinson’s disease biologically. The SynNeurGe criteria classify patients by the combined presence of pathological alpha-synuclein biomarkers, neuroimaging evidence of neurodegeneration, and disease-relevant genetic variants. The Neuronal Synuclein Disease criteria take a parallel approach, defining disease by the presence of pathological synuclein as measured by a validated biomarker, with or without evidence of dopaminergic dysfunction detected through dopamine transporter imaging. The integrated staging system then arranges these biological anchors along a seven-stage ladder: Stage 0 reserved for carriers of fully penetrant mutations in the SNCA gene; Stages 1A and 1B for people with synuclein pathology but no symptoms, depending on whether dopaminergic dysfunction is present; Stages 2A and 2B for those with subtle signs that stop short of functional impairment; and Stages 3 through 6 capturing progressively severe clinical disability.

PPMI, the international observational study launched in 2010, provided the ideal laboratory for testing whether this staging framework means anything in the real world. The researchers focused on the sporadic Parkinson’s cohort: participants diagnosed within two years of enrollment who had never taken dopaminergic medication, whose examinations showed cardinal motor features, and whose dopamine transporter scans confirmed the characteristic deficit in the striatum. Crucially, the team selected only those participants who met biological NSD criteria through a positive CSF seed amplification assay, recruited before 2020 to guarantee at least five years of follow-up. This design deliberately stripped away a longstanding source of noise in Parkinson’s research: the clinical heterogeneity that arises when a “Parkinson’s” diagnosis might actually encompass unrelated neurodegenerative processes that mimic the disease but follow entirely different biological courses.

Over the five-year observation window, participants underwent an unusually thorough annual workup. Motor and non-motor function was tracked with the Movement Disorders Society Unified Parkinson’s Disease Rating Scale across all four of its parts, alongside the Hoehn and Yahr staging scale and the Schwab and England activities of daily living score. Smell was measured with the University of Pennsylvania Smell Identification Test, autonomic function with the SCOPA-AUT, mood with the Geriatric Depression Scale, and REM sleep behavior disorder risk with a dedicated screening questionnaire. Cognition was assessed with the Montreal Cognitive Assessment and, from the study’s third year onward, formal clinician diagnoses of normal cognition, mild cognitive impairment, or dementia. Medication burden was quantified as levodopa equivalent daily dose, and dopamine transporter imaging was repeated at years one, two, and four, quantified both in the putamen—the region most affected in Parkinson’s—and across the striatum as a whole.

The broad message from the five-year trajectories is one of measurable, biologically anchored progression. Patients recruited as freshly diagnosed, biologically confirmed sporadic Parkinson’s patients showed the expected decline across motor scales and dopaminergic imaging, with the earlier PPMI analysis by Simuni and colleagues having already documented significant—though modest—correlation between worsening clinical scores and falling DAT binding over five years. What the new analysis adds is the biological filter: by restricting the cohort to synuclein-seeding-positive individuals, the study reduces the contamination from look-alike conditions that has historically muddied progression estimates. When a cohort is defined by its underlying pathology rather than its outward symptoms, the resulting disease course becomes a truer reflection of what alpha-synuclein itself does to the nervous system over time.

Perhaps the most consequential findings concern prediction. If the NSD-ISS staging system is to earn its place in research clinics and, eventually, in therapeutic trials, it must do more than organize patients neatly on a page—it must forecast what comes next. The study analyzed whether a patient’s baseline stage predicted survival and the time required to reach clinically meaningful disease milestones: crossing thresholds on the clinical rating scales, advancing in Hoehn and Yahr stage, slipping in daily living independence, or developing cognitive impairment. The logic is straightforward and powerful. A patient sitting at Stage 2B—with confirmed synuclein pathology, dopaminergic dysfunction, and subtle signs but no functional impairment—should, in theory, march down the staging ladder at a predictable pace. Demonstrating that baseline stage genuinely stratifies risk would give trial designers a rational tool for enrichment, allowing them to recruit patients at the stage where a candidate drug is most likely to show benefit.

That trial-design implication is not academic. Across neurodegenerative disease research, therapeutic development is pivoting decisively toward biomarker-defined enrollment. The bitter lessons of Alzheimer’s trials—where anti-amyloid therapies only proved effective once trials recruited based on biological confirmation rather than syndrome alone—have not been lost on the Parkinson’s community. Drugs targeting alpha-synuclein directly, whether through immunotherapy, aggregation inhibition, or other mechanisms, are entering trials that increasingly require positive seed amplification assays or other biological confirmation as a gate for entry. A validated staging system that predicts five-year trajectory would allow sponsors to select participants early enough in the disease process for neuroprotective strategies to matter, while reserving later-stage patients for symptomatic interventions. The five-year PPMI data provide exactly the kind of naturalistic benchmark that such enrichment strategies demand.

The study also marks a conceptual milestone: the description of Parkinson’s disease under contemporary management. Patients diagnosed today are treated differently than those diagnosed twenty years ago, and their disease course may differ as a result. Describing outcomes in a biologically defined, prospectively observed cohort establishes a modern baseline against which future disease-modifying therapies can be judged. When an experimental drug claims to slow progression, the comparison will be against trajectories like those documented here—precise, biomarker-anchored, and free of the diagnostic uncertainty that plagued earlier natural history studies. In that sense, the paper functions simultaneously as a clinical report and as a foundation stone for the next generation of Parkinson’s trials.

What emerges from five years of watching synuclein-positive patients is a disease that can now be seen, staged, and tracked before it fully announces itself. The combination of CSF seed amplification assays, dopamine transporter imaging, and structured clinical assessment has converted a syndrome defined in the examination room into a biological disease measurable in the laboratory. If the staging system validated in this cohort continues to predict who declines fastest and who reaches milestones soonest, neurologists may one day tell a newly diagnosed patient not only what they have, but with unprecedented confidence what lies ahead—and researchers may finally test neuroprotective drugs in the early biological window where they stand the best chance of changing the story. For a disease that has resisted precise definition since James Parkinson first described it in 1817, that is a transformation worth watching.

Scientists in Brazil have demonstrated that a combination of three non-destructive imaging techniques, visible-spectrum (RGB) photography, X-ray radiography, and three-dimensional magnetic resonance imaging (MRI), can detect and characterize hidden damage in soybean seeds that conventional quality tests frequently miss. The study, published in Smart Agricultural Technology, comes at a critical moment: Brazil is the world’s largest soybean producer and exporter, with an estimated harvest of 166 million tons for the 2024/2025 growing season, and seed quality is increasingly under threat from the very climate extremes that help drive the country’s agricultural productivity.

The research, led by Rafael Mateus Alves and colleagues at institutions including the Seed Technology Center of Embrapa Soybean and the University of São Paulo, was funded by the São Paulo Research Foundation (FAPESP). Its central premise is deceptively simple: because seed quality is largely determined in the field, before harvest even begins, researchers and seed companies need tools that can look inside a seed without killing it. Destructive assays such as the tetrazolium viability test, which stains metabolically active tissue red, are the industry standard, but they end the life of the sample. That is a particular problem for breeding programs, where each seed represents a unique genetic combination that must survive analysis if it is to be planted.

The seed itself is a small miracle of engineering. A protective coat, derived from the ovule integuments and marked by a scar called the hilum, regulates water uptake and shields the embryo within. The embryo contains the plumule, which gives rise to the plant’s first true leaves; the apical meristem, precursor of the aerial plant; and the hypocotyl–radicle axis, which breaks through the soil and forms the primary root. Flanking this axis are the cotyledons, storage organs packed with macromolecules that are metabolized and shipped to the growing axis during germination. Damage to any of these structures, from insects, fungi, drought, heat, or rough handling during harvest, can silently reduce a seed lot’s vigor, the capacity for rapid, uniform germination that underpins successful crop establishment.

To interrogate this architecture non-invasively, the team assembled ten representative seeds from production fields, commercial lots, and storage samples, each embodying a distinct damage category: stink bug injury, purple seed stain caused by fungi, green seeds from incomplete maturation, wrinkled seeds from heat and drought stress, weathering-related coat ruptures, severe structural failure, mechanical harvest damage, and internal galleries bored by the cigarette beetle (Lasioderma serricorne) during storage. Every seed was imaged sequentially with each of the three modalities and then subjected to the tetrazolium test as a qualitative reference standard.

The technical pipeline was meticulously standardized. For visible-light imaging, seeds were mounted on acetate sheets and photographed with a Leica DMLB microscope equipped with a DFC 310FX digital camera, oriented in four standardized positions with the hilum consistently to the right. X-ray radiographs were acquired on a Faxitron MX-20 DC-12 system operating at 34 kilovolts, with a nine-second exposure and an object-to-source distance of 11.4 centimeters; uniform contrast settings were applied to all images to make damaged tissues directly comparable. The MRI work used a horizontal 2 Tesla superconducting magnet coupled to a Bruker Avance III spectrometer operating at 85.24 megahertz for proton nuclei. Before scanning, seeds were conditioned to raise their moisture content from roughly 8–10 percent (wet basis) by incubation on moistened germination paper for three hours at 25 °C, a step that ensured sufficient hydrogen signal for imaging. A three-dimensional FLASH pulse sequence, with a 3-millisecond echo time and 75-millisecond repetition time, collected data on a 128×128×128 matrix over a 12.8-millimeter field of view, yielding isotropic voxels of 100 micrometers per side. Datasets were zero-padded to 256³ before Fourier transformation and rendered in three dimensions using ImageJ with the 3Dscript plugin.

The resulting image galleries are striking. On seeds punctured by stink bugs, the dominant pest of Brazilian soybean fields, RGB images revealed darkened patches on the cotyledons and near the plumule, the visible legacy of insect feeding that lacerates tissue and injects yeasts that degrade it from within. X-ray images flagged zones of reduced radiation absorption in the same darkened regions, while MRI cross-sections exposed the depth of tissue destruction with unusual clarity, precisely because degraded tissue no longer absorbs water and therefore produces no magnetic resonance signal. The tetrazolium test confirmed that these zones lacked respiratory activity, meaning they were metabolically dead.

Not every symptom tells the same story, however, and the multimodal approach proved especially powerful at distinguishing superficial from structural damage. Seeds with purple seed stain, caused by Cercospora fungi that secrete the toxin cercosporin, showed internal structures that appeared entirely normal in both X-ray and MRI. Yet the tetrazolium test produced an unusually intense red coloration at the stained coat, indicating that fungal activity had compromised the seed coat’s ability to regulate water uptake, allowing the staining solution to penetrate more deeply into the embryo. Similarly, green seeds, which retain chlorophyll because heat and drought interrupted maturation, showed no structural anomalies under X-ray or MRI, but their membranes proved unusually permeable to the tetrazolium solution, hinting at the physiological deterioration typical of immature tissue.

Where MRI truly distinguished itself was in detecting damage invisible to every other method. One seed that appeared flawless in visible light and radiographically normal in X-ray images revealed, in its three-dimensional MRI reconstruction, internal ruptures running through the central cotyledons, the likely consequence of rapid, uneven drying that forces differential cellular expansion and eventual tissue collapse. In mechanically damaged seeds, the X-ray images showed a crack in the hypocotyl–radicle axis that the camera could not see, but MRI went further, revealing the full extent of the fissure and the displacement of the axis relative to the cotyledons. The team also documented a dramatic structural failure nicknamed the “popcorn effect,” in which alternating wetting and rapid drying cycles tear the seed coat and force the cotyledons apart, a phenomenon increasingly reported in Brazilian seed lots as late-season weather grows more erratic.

To move beyond qualitative observation, the researchers extracted grayscale histograms from each X-ray image, normalizing pixel frequencies across the full 0–255 intensity range. Two statistical descriptors, mean gray level and Shannon entropy, were then compared across damage categories using one-way ANOVA followed by Tukey’s honest significance test. The analysis yielded clear separation: undamaged seeds showed the narrowest distributions with peaks at high grayscale values, while beetle-infested seeds and wrinkled seeds displayed the broadest distributions with heavy weight in the lower gray ranges, reflecting hollowed-out tissues and sparse, deformed structures. Mean gray level differed significantly across categories, peaking in undamaged seeds and bottoming out in wrinkled ones, while Shannon entropy, a measure of histogram complexity, was highest in beetle-damaged seeds and lowest in healthy ones. Because each damage category produced a characteristic radiographic fingerprint, the authors suggest these histogram metrics could support automated, quantitative screening in commercial seed laboratories.

Each modality, the authors stress, occupies a distinct niche. RGB imaging is fast, cheap, and ideal for surface screening but blind to anything beneath the coat. X-ray radiography, already a mainstay of seed testing labs, reveals internal voids, malformations, and cavities rapidly and non-destructively, though it cannot report on metabolic viability and demands careful standardization. MRI, with its superb soft-tissue contrast and genuine three-dimensional resolution, excels at detecting subtle physiological and structural alterations, latent mechanical injury, hydration-induced ruptures, and moisture redistribution, but its cost and acquisition time currently confine it to research settings. The tetrazolium test remains valuable as a reference but is destructive and subject to interpreter bias.

The authors argue the techniques should be viewed as complementary rather than competing, and they sketch a future in which RGB and X-ray screening handle high-throughput commercial quality control while MRI resolves ambiguous or high-stakes cases, such as breeding lines and seed physiology studies. Advances in imaging hardware, automation, and artificial intelligence-based analysis, they note, could eventually fuse these modalities into scalable phenotyping workflows. For now, the message is clear: in an era when a single season of heat, drought, and hungry insects can undermine an entire seed lot, the ability to see damage in all three dimensions, without sacrificing the seed itself, may prove as transformative for agriculture as it has been for medicine.

Strength training helps protect independence in aging by preserving the muscle needed for daily movement, balance, and resilience. Healthy aging means staying independent, moving well, and continuing to take part in daily life as the years pass. For many people, the most meaningful signs of good health are practical ones: standing up from a chair [...]

In a development that could reshape how scientists study the mechanics of human movement, a team of biomedical engineers and computer scientists has unveiled an open-source software tool capable of tracking the tiny, intricate bones of the wrist as they glide and rotate during motion. The new tool, called Hierarchical 3D Registration, or 3DH, is a module within the SlicerAutoscoperM extension of the widely used 3DSlicer computing environment. Described in a study published in BioMedical Engineering OnLine, the software was validated against previously established methods using both sequential three-dimensional computed tomography, or 3DCT, scans of living participants performing thumb tasks and dynamic four-dimensional CT, or 4DCT, images of cadaveric wrists moving through flexion and extension. The results demonstrate that the open-source approach can match the accuracy of independent, previously published methodologies while offering researchers everywhere a free and reproducible path to quantifying skeletal kinematics.

The wrist has long been one of the most challenging joints for biomechanists to study. It contains eight small carpal bones, including the scaphoid and lunate, along with the radius of the forearm and the metacarpal bones of the hand, all articulating in a tightly packed arrangement that allows the remarkable range of motion humans rely on every day. Understanding how these bones move relative to one another, a field of study known as arthrokinematics, is essential for unraveling the causes of wrist instability, osteoarthritis, ligament injuries and the long-term outcomes of surgical repairs. Yet measuring those movements in living, moving joints has historically required either invasive techniques, labor-intensive manual analysis or proprietary software that few laboratories can access or verify.

Quantitative tracking of skeletal structures in vivo, with both efficiency and high temporal resolution, matters to a wide range of questions in biomechanical research, and the authors of the new study argue that the field has been held back by a shortage of accessible, semi-automated registration tools. Image registration, the computational process of aligning one three-dimensional image volume to another, lies at the heart of motion tracking from CT data. When a bone moves between two scans, its position and orientation change, and a registration algorithm must find the transformation that best overlays the bone’s image from one time point onto the next. Done well, this yields precise measurements of rotation and translation. Done poorly or inconsistently, it introduces errors that can swamp the small movements of carpal bones, which may shift by only a few millimeters and a few degrees during normal function.

The new 3DH module addresses this problem with a hierarchical strategy for registering bones across sequences of image volumes. The researchers presented and evaluated the software using two distinct classes of data drawn from previous studies. The first consisted of sequential 3DCT datasets of human participants performing various thumb tasks, in which the radius, the first metacarpal and the trapezium were the target bones. The second consisted of dynamic 4DCT datasets capturing simulated flexion-extension motion in cadaveric wrists, providing time-resolved, three-dimensional image volumes of the scaphoid and lunate in motion. By testing the tool on both static sequential scans and true dynamic imaging, the team was able to demonstrate that 3DH is not limited to a single imaging paradigm but can serve as a unified tracking solution across modalities.

To determine whether the new open-source tool could be trusted, the investigators compared the arthrokinematics computed with 3DH against values that had been previously calculated using independent approaches for the same 3DCT and 4DCT datasets. The statistical framework for this comparison was the Bland-Altman analysis, a standard method for assessing agreement between two measurement techniques. Rather than simply correlating the two sets of results, Bland-Altman analysis quantifies the mean bias, the systematic difference between methods, and the 95 percent limits of agreement, the range within which most individual differences between the methods are expected to fall. This approach is particularly well suited to method-comparison studies because it reveals whether a new technique introduces systematic error and how widely individual measurements might deviate from those of the established gold standard.

The performance figures reported in the study are striking. For the 3DCT data, in which the radius, first metacarpal and trapezium were tracked, the mean bias for the helical angle, a single-axis description of three-dimensional rotation, was just minus 0.06 degrees, with 95 percent limits of agreement ranging between minus 1.87 degrees and 1.74 degrees. Translation measurements fared nearly as well: the mean bias was 0.12 millimeters, with limits of agreement spanning from minus 2.23 millimeters to 2.00 millimeters. In practical terms, the open-source tool reproduced the rotational and translational measurements of the earlier methods to within roughly two degrees and two millimeters at the extremes, differences small enough to preserve the biological signal in most wrist kinematics experiments.

The dynamic 4DCT results told a similar story for the carpal bones. Tracking the scaphoid and lunate through simulated flexion-extension, 3DH produced mean biases of the Euler angle rotations, which decompose three-dimensional rotation into three orthogonal components, of less than 0.77 degrees about any direction. The widest 95 percent limits of agreement ranged between minus 7.11 degrees and 8.65 degrees. For translations, mean biases were less than 0.5 millimeters in any direction, with the widest limits of agreement ranging between minus 2.36 millimeters and 1.43 millimeters. The wider limits of agreement for rotations in the dynamic data likely reflect the added challenges of time-resolved imaging, including motion blur and the smaller, more complexly shaped carpal bones, but the near-zero mean biases indicate that the tool does not systematically drift away from established values.

Crucially, all target bones were successfully tracked in both the 3DCT and 4DCT datasets, meaning the hierarchical registration algorithm converged on correct solutions in every case tested. This robustness matters because registration algorithms can occasionally fail, locking onto incorrect alignments or diverging entirely, and such failures typically demand time-consuming manual intervention. A semi-automated tool that reliably succeeds across diverse bones, motion tasks and imaging protocols removes a significant bottleneck. Manual bone tracking in CT volume sequences can take hours per bone per trial, and the labor multiplies rapidly in studies involving multiple participants, multiple bones and dozens of motion cycles. By automating this process within a validated framework, 3DH promises to accelerate the pace at which wrist biomechanics data can be collected and analyzed.

The choice to build the module inside 3DSlicer, and specifically within the SlicerAutoscoperM extension, is a deliberate strategic decision with implications well beyond the wrist. 3DSlicer is a free, open-source platform for medical image computing and visualization with a large international user base spanning research, clinical and educational communities. By embedding 3DH in this ecosystem, the developers have made the tool immediately available to any laboratory already working with 3DSlicer, complete with the platform’s established infrastructure for loading DICOM data, segmenting anatomical structures and visualizing results. The authors note that the ability to track bones from both 3DCT and 4DCT data sources, with agreement to prior methodologies, demonstrates that the 3DH approach is a capable tool that could promote collaboration and consistency of results through open-source software.

Consistency is a quietly urgent issue in biomechanics. When different laboratories use different proprietary tools with undisclosed algorithms, it becomes difficult to distinguish true biological differences between study populations from mere methodological discrepancies. Open-source registration changes that calculus: the code can be inspected, verified, extended and reused, and results from different institutions become directly comparable because they are produced by identical algorithms. This transparency also lowers the barrier to entry for research groups in settings that cannot afford commercial licenses, potentially broadening the global community capable of conducting rigorous skeletal kinematics research.

The study itself emerged from a collaboration spanning several institutions. The team included Joseph J. Crisco, Amy M. Morton and John D. Holtgrewe of the Department of Orthopedics at The Warren Alpert Medical School of Brown University and Rhode Island Hospital; Cesar Lopez, Andrew Thoreson and Kristin D. Zhao of the Assistive and Restorative Technology Laboratory and the Department of Physical Medicine and Rehabilitation at the Mayo Clinic in Rochester, Minnesota; and Jean-Christophe Fillion-Robin and Beatriz Paniagua of Kitware Inc. in Clifton Park, New York. The involvement of Kitware, a company known for its leadership in open-source scientific computing, underscores the blend of academic rigor and software engineering expertise required to produce a research tool robust enough for widespread distribution. Funding was provided by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health under award numbers AR059185, AR078924 and AR071338.

The implications of this work extend naturally to the future of musculoskeletal imaging. As newer CT technologies, including photon-counting detector systems, deliver ever-finer spatial resolution and as dynamic 4DCT becomes more widely available, the volume of image data available for kinematic analysis will grow dramatically. Tools like 3DH, which are fast, semi-automated and validated across modalities, will be essential for converting that imaging bounty into quantitative biomechanical knowledge. For patients with wrist disorders, the downstream promise is equally tangible: better measurements of how carpal bones actually move in living joints can inform more accurate diagnoses, improved surgical planning, more realistic computational models of joint loading and ultimately better-designed treatments for conditions ranging from scapholunate instability to thumb-base arthritis. With the tool now openly available in the 3DSlicer environment, researchers around the world can begin putting it to work.

The Denisovans, the enigmatic archaic human relatives first identified from a finger bone and a handful of teeth in a Siberian cave, have long been known almost exclusively through their DNA. Now, a landmark study published in Nature has pulled back the curtain on how these ancient hominins actually lived — what they ate, how they hunted, what tools they made, and the environments they braved — through an extraordinary assemblage of fossils and artefacts from Bianfu Cave in Yunnan Province, southwestern China. The findings paint the most detailed portrait to date of Denisovan biology, behaviour and ecology in eastern Asia, and suggest that these vanished people left a deep genetic and cultural imprint on later populations across Southeast Asia and Oceania.

The research team, led by Qingfeng Ruan, Hua Li and Song Xing and their colleagues, conducted a comprehensive analysis of hominin fossils and the associated archaeological remains recovered from Bianfu Cave. The hominin fossils themselves date to roughly 167,000 to 134,000 years ago, placing them firmly in the Middle Pleistocene. But the cultural sequence preserved in the cave’s sediments is far longer, stretching from approximately 190,000 years ago to about 70,000 years ago. That span — some 120,000 years of continuous or near-continuous occupation — makes Bianfu Cave one of the longest cultural records that can most parsimoniously be attributed to Denisovans anywhere in the world.

The Denisovan identity of the fossils was established through two independent and technically demanding lines of evidence. The first involved analysis of the enamel–dentine junction, the internal boundary between the enamel cap and the underlying dentine of the teeth. This three-dimensional morphological interface acts something like a fingerprint of evolutionary relationships, revealing diagnostic traits of the tooth’s internal architecture that are invisible from the outside but characteristic of particular hominin lineages. The second line of evidence came from palaeoproteomics, the recovery and identification of ancient proteins preserved within fossilized tissues. Because proteins are far more abundant and durable than DNA, they can survive in warm, humid environments like those of southern China, where genetic material degrades rapidly. Sequencing these ancient proteins allowed the researchers to place the Bianfu hominins on the Denisovan branch of the human family tree with confidence.

The fossil collection itself is remarkable. It includes four teeth that substantially expand the known range of dental variation within the Denisovan lineage — a critical contribution, since Denisovans were previously represented by a very small number of dental specimens, making it difficult to gauge how variable their teeth could be. The new fossils also include two cranial fragments and a radius, a forearm bone, providing rare glimpses of Denisovan skull and postcranial anatomy that were previously almost entirely unknown. Together, these remains transform Denisovans from a genome and a scatter of isolated teeth into a population with recognizable skeletal biology.

Beyond the fossils, the archaeological record from Bianfu Cave offers something equally precious: a window into Denisovan daily life. The researchers reconstructed the ancient environment using faunal remains and pollen preserved in the cave deposits. These records indicate that across Marine Isotope Stages 6 through 4 — a period encompassing major glacial–interglacial climatic swings between roughly 190,000 and 57,000 years ago — the landscape around the cave was dominated by coniferous forest or a mosaic of forest and steppe. This was a cool, variable environment that Denisovans exploited successfully across tens of thousands of years, demonstrating a resilience to climatic change that adds to growing evidence of their adaptability, from the high altitudes of the Tibetan Plateau to the subtropical latitudes of mainland Southeast Asia.

The subsistence evidence is particularly striking. Analysis of the animal bones from the cave shows that its inhabitants practised specialized hunting focused on medium- to large-bodied prey. This was not opportunistic scavenging but a sustained, targeted hunting strategy — one that would have required knowledge of animal behaviour, coordinated effort and effective weaponry. Specialized big-game hunting of this kind has often been associated with modern humans and late Neanderthals in western Eurasia, so finding it in a Denisovan context more than 150,000 years ago dramatically narrows the perceived behavioural gap between these archaic lineages and our own species.

Yet the technological strategy documented at Bianfu Cave is, at first glance, surprisingly simple. The stone tool assemblage is characterized by expedient core reduction and tool production — that is, the inhabitants struck flakes from cores as needed, used them, and discarded them, rather than investing in carefully shaped, curated tools that were carried, maintained and resharpened over time. Alongside this, the Denisovans of Bianfu Cave made pervasive use of unmodified bones, employing skeletal elements of their prey directly as tools without altering them beforehand.

Taken together, the researchers interpret this pattern as an adaptive system that prioritized the exploitation of what they call object affordance — the inherent functional possibilities already present in a raw material or object — over intensive tool manufacture. A suitably shaped bone fragment could serve as a hammer, a digging implement or a scraping tool with little or no modification; a sharp flake struck from a convenient core could cut and butcher without further refinement. In the rich, resource-diverse environments of subtropical southwestern China, where raw materials and animal resources were plentiful, such a flexible, low-investment strategy may have been not a sign of simplicity but a highly efficient way of life. It suggests that Denisovans measured success not by the sophistication of their toolkit but by how skilfully they read and exploited the opportunities their landscape provided.

This behavioural portrait gains added significance when set against the wider geography of Denisovan presence. Over the past decade and a half, Denisovans have been documented across a staggering range: from the original genomic discoveries in the Altai Mountains of Siberia, first reported in 2010, to the mandible from the Tibetan Plateau described in 2019, Denisovan DNA recovered from sediments at Baishiya Karst Cave in 2020, a molar from the Annamite Chain of northern Laos announced in 2022, a male Denisovan mandible from Pleistocene Taiwan reported in 2025, and proteomic identification of the 146,000-year-old Harbin cranium, also in 2025. Bianfu Cave now anchors this story firmly in southwestern China, a region long suspected to have been a Denisovan heartland and a critical corridor for hominin movements between mainland Asia and Southeast Asia.

The study’s authors argue that the region’s Denisovans point to a substantial genetic and cultural legacy in later human populations. Modern populations in Southeast Asia and Oceania carry the highest known proportions of Denisovan ancestry anywhere in the world — a legacy of interbreeding that occurred as modern humans moved through and beyond island Southeast Asia. The new evidence from Bianfu Cave suggests that the ways of life practised by Denisovans in this region — their hunting strategies, their flexible technological approaches, their hard-won knowledge of tropical and subtropical environments — may have been part of the inheritance, alongside their genes, that helped later human populations thrive as they spread across the diverse and challenging landscapes of Asia and the Pacific.

For palaeoanthropologists, the significance of the Bianfu Cave discoveries is difficult to overstate. Denisovans have been the least anatomically documented of all known hominin groups — a lineage defined first by DNA and only slowly fleshed out with fragmentary fossils. The new teeth, cranial fragments and radius from Yunnan, combined with an unmatched 120,000-year archaeological sequence, mean that researchers can finally study Denisovans not just as a genome or a set of molecular signatures but as a living, adapting population: hunters of large game, makers and users of expedient tools, and inhabitants of conifer forests that swung between glacial cold and interglacial warmth. As analysis of the Bianfu material continues, it promises to reshape our understanding of how archaic humans lived in eastern Asia and how deeply their story is woven into our own.

A major trial found that implantable cardioverter-defibrillators may benefit some patients younger than 70 with mild-to-moderate heart dysfunction and cardiac scarring. A more than decade-long randomized trial suggests that implantable cardioverter-defibrillators may benefit some patients who have heart scarring and only mild-to-moderate loss of heart function, a group that currently falls outside standard treatment guidelines. [...]

Those who remember PC gaming in the days before Steam may have fond memories of the "big box" packaging that made those games stand out as shelf eye candy and sought-after in-home collectibles. Today, PC gaming platform GOG is hoping to reignite some of the nostalgia for those days by adding downloadable 3D models and printable, foldable high-res packaging scans for a handful of PC games on the platform.

Thus far, the "Printable Big Box" collection on GOG is limited to just five games: Armikrog, Descent, Hitman: Codename 47, Myst, and Star Trek: 25th Anniversary. Players browsing those games on the GOG store will now see "3D Boxes" listed among the included "Goodies" before purchase. Owners of those games on GOG can download a ZIP file containing a version of the box art as an animated 3D object file (in an easy-to-open GLB format) and a "flat printable template" in PDF, SVG, and Adobe Illustrator formats. Those downloads allow for what GOG calls "a proper papercraft project for anyone who misses shelves full of PC game boxes."

The 3D model animates to show the front flap opening. Credit: GOG

"Physical media doesn’t survive the way software can—boxes get lost, crushed, thrown out," GOG writes in today's announcement. "Scanning them, and letting you print your own, is preservation in a different shape: the same mission that keeps these games running on modern PCs, applied to the objects they used to come in."

Read full article

Comments

© GOG

Depression in later life may be an early warning from the brain, appearing years before Alzheimer’s begins to affect memory and thinking A new study in JNeurosci found that faster accumulation of tau, a protein closely tied to Alzheimer’s, was associated with worsening depressive symptoms in older adults who remained cognitively healthy. Teodora Markova of [...]

Pat Fauquet is the next stop onThe Road to Macstock is a conversation about her session, “Pills, Processes, and Procedures,” explaining how Apple devices can help manage medications, appointments, research, and communication with doctors. Drawing from years of health challenges and tech experience, she highlights the Apple Watch, iPhone, iPad, and Mac as practical tools for personal advocacy, caregiving, and preparedness. This might be the most important session this year. 

💾

The Road to Macstock takes a serious turn as Mike T. Rose previews his workshop on becoming a digital caretaker for family members and loved ones. Mike discusses scams targeting older adults, password sharing, remote support, Apple Passwords, financial preparedness, and the need to balance protection, respect, independence, and trust when helping others manage their digital lives. 

💾

Expansion microscopy has spent the past decade changing the rules of super-resolution imaging. Instead of relying solely on increasingly sophisticated optics, the technique physically enlarges biological specimens so that molecules separated by nanometers become easier to distinguish with ordinary fluorescence microscopes. Now, researchers have introduced a method designed to push that concept toward one of the most difficult goals in microscopy: seeing the shapes of individual proteins directly.

The approach, described by Ahmed H. Shaib, Mahmoud M. Alawieh and Stefan O. Rizzoli in Nature Protocols, combines one-step nanoscale expansion microscopy with fluctuation-based super-resolution analysis. The resulting workflow, called ONE microscopy, is intended to make molecular-scale imaging more accessible to laboratories that do not have access to cryo-electron microscopes or specialized high-end optical systems.

Traditional light microscopy is limited by diffraction, a physical effect that causes light from two nearby objects to blur together when they are too close. Even modern super-resolution methods can require complex instruments, intense labeling strategies or extensive computational processing. Expansion microscopy takes a different route. Researchers anchor biological molecules within a swellable polymer network and then expand the material, increasing the physical distance between fluorescent labels.

The expansion step does not automatically reveal every molecular detail. A protein’s structure can still be represented by only a small number of fluorescent signals, and conventional images may contain noise, background fluorescence and motion-related fluctuations. ONE microscopy addresses these limitations by analyzing changes in fluorescence intensity over time. These fluctuations contain information about the presence, position and behavior of labeled molecules that may not be obvious in a single frame.

In practical terms, the method links chemical preparation, physical enlargement and computational analysis into a single workflow. Samples are embedded in a polymer gel, labeled to identify the proteins of interest and then expanded. After expansion, researchers acquire image sequences using conventional fluorescence equipment. Specialized software analyzes the temporal variation in the recorded signals, extracting spatial information beyond what a standard diffraction-limited image would provide.

This combination is significant because it shifts the focus from simply locating a protein to examining its overall shape. For many biological questions, knowing that a protein is present is not enough. Its size, orientation and structural organization can determine how it interacts with membranes, vesicles, organelles or neighboring proteins. Directly observing those features could help researchers investigate molecular machines in their native cellular environments rather than relying exclusively on purified samples or averaged structural models.

The protocol is designed to work across a broad range of biological materials, including purified proteins, cultured cells and tissues. That flexibility could make the technique useful for researchers studying protein organization at multiple scales. A purified protein might provide a controlled test of shape reconstruction, while cells and tissues could reveal how the same protein is arranged amid the crowded and complex environment of living biology.

A major part of the reported advance is the accompanying software package. Fluctuation-based imaging can be powerful, but its usefulness depends on reliable data processing, and computational analysis has often been a barrier for non-specialist users. The authors present the software as stable and user-friendly, with the goal of making the analysis more efficient, reproducible and practical for laboratories using standard fluorescence microscopes.

ONE microscopy does not replace cryo-electron microscopy, which remains capable of resolving structures at atomic or near-atomic scales under appropriate conditions. Nor does it eliminate the challenges associated with labeling, gel chemistry, image quality and sample preparation. Expansion can introduce distortions, and successful imaging depends on preserving the relationship between the fluorescent labels and the underlying structures. Nevertheless, the method offers a complementary strategy: rather than averaging thousands or millions of molecules, it aims to examine individual protein shapes through expanded, fluorescence-labeled specimens.

The researchers describe the workflow as a practical framework for protein imaging on conventional equipment. By combining physical separation of fluorophores with information extracted from fluorescence fluctuations, the method brings nanoscale structural analysis closer to routine biological imaging. Its broader impact may come not from replacing existing forms of super-resolution, but from making a previously specialized capability more reproducible and attainable for laboratories investigating how individual proteins operate inside cells and tissues.

Subject of Research: One-step nanoscale expansion microscopy for visualizing individual protein shapes using conventional fluorescence microscopes.

Article Title: ONE microscopy.

Article References: Shaib, A.H., Alawieh, M.M. & Rizzoli, S.O. ONE microscopy. Nature Protocols (2026). https://doi.org/10.1038/s41596-026-01399-x

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41596-026-01399-x

Keywords: expansion microscopy, ExM, nanoscale imaging, super-resolution microscopy, fluctuation-based analysis, protein structure, fluorescence microscopy, single-protein imaging, cryo-electron microscopy, biological imaging

Foundation models are promising to become the “universal translators” of biomedical data—but a new perspective argues that medicine should resist treating them as all-knowing digital doctors. In a review published in Nature Biomedical Engineering, researchers describe how large, adaptable artificial intelligence systems are reshaping biomedical imaging while warning that impressive benchmark scores can conceal serious weaknesses in clinical environments. Their central message is direct: foundation models are most likely to improve healthcare by augmenting specialists, not replacing them.

Foundation models are designed to learn broad representations from enormous and diverse datasets before being adapted to specific tasks. In biomedical imaging, those data may include magnetic resonance imaging, computed tomography, X-rays, ultrasound, digital pathology slides and ophthalmic images. The same underlying model could theoretically identify tumors, segment organs, estimate disease risk, retrieve similar cases and generate clinical reports. The vision becomes even broader when imaging is combined with pathology, electronic health records and genomic information, creating a composite system intended to analyze a patient across multiple biological scales.

That ambition reflects a major change from traditional medical AI. Earlier systems were generally built for one narrowly defined task, such as detecting pneumonia on chest radiographs or outlining a brain lesion on MRI scans. Foundation models instead attempt to create a reusable backbone that can be transferred across diseases, hospitals and imaging technologies. Technically, they learn statistical patterns in high-dimensional data, often through self-supervised training in which the system predicts missing, transformed or associated information. Afterward, developers fine-tune or prompt the model for particular clinical applications.

But biomedical imaging is not a single, uniform world. Images differ according to scanner manufacturer, acquisition protocol, patient population, disease prevalence and local clinical practice. A model trained primarily on data from large academic hospitals may perform very differently in community clinics, rural settings or countries with limited equipment. This problem, known as domain shift, can occur even when images appear visually similar. Small changes in image quality, contrast, hardware or patient demographics may alter the statistical distribution learned by the model and undermine its predictions.

The authors introduce a framework called real-world evaluation and assessment of foundation models, or REAL-FM, to examine whether these systems are ready for practical use. Rather than focusing only on accuracy scores, REAL-FM considers several dimensions at once, including the quality and representativeness of training data, technical readiness, clinical value, integration into existing workflows and responsible artificial intelligence. The framework is intended to help clinicians interpret claims about new models and to push developers toward evaluations that resemble actual medical practice.

One of the sharpest distinctions in the perspective is between pattern recognition and causal reasoning. Foundation models can become extraordinarily skilled at recognizing visual associations—for example, linking a particular texture or anatomical feature with a diagnosis present in their training data. Yet association is not the same as understanding why a disease occurs, how it will progress or whether a treatment will benefit an individual patient. A model may identify a correlation that is valid in one hospital but reflects a hidden confounder, such as a scanner type, reporting convention or patient-selection pattern, rather than a biological signal.

This limitation becomes especially dangerous when models are moved beyond simplified benchmarks. Many benchmark datasets provide carefully curated images, clear labels and narrowly defined tasks. Clinical care is messier: scans can be incomplete, diagnoses can be uncertain, records may contain contradictory information and several conditions may coexist. The perspective highlights a lack of verified generalization across such conditions, along with a shortage of prospective, outcome-based validation. In a prospective study, an AI system would be evaluated while care is actually being delivered, with researchers measuring whether it improves diagnostic accuracy, treatment decisions, patient outcomes or workflow efficiency.

Data scarcity is another obstacle. The largest foundation models require vast quantities of data, but medical information is difficult to collect, standardize and share. Patient records are fragmented across institutions, imaging data may lack reliable annotations and rare diseases are inherently underrepresented. Privacy regulations and governance requirements further complicate the creation of centralized datasets. As a result, a model may appear broadly capable while remaining poorly tested in the populations and clinical circumstances where errors could have the greatest consequences.

The researchers argue that human oversight therefore remains indispensable. In practical terms, foundation models may be most useful as clinical assistants that prioritize images for review, highlight suspicious regions, summarize longitudinal records or offer a second opinion that specialists can interrogate. Safe deployment will require transparent reporting of uncertainty, monitoring for performance drift, mechanisms for correcting errors and clear accountability when recommendations influence care. The future envisioned by the authors is not a single monolithic medical oracle, but a coordinated ecosystem of specialized AI systems, each evaluated for a defined clinical role and connected to expert-led workflows.

The perspective arrives as the biomedical AI field races toward increasingly general-purpose systems. Its warning is not that foundation models lack value, but that technical scale alone cannot establish clinical reliability. Before these models can be trusted with consequential decisions, they must demonstrate robustness across domains, usefulness in real workflows, safety under unexpected conditions and measurable benefits for patients. The proposed REAL-FM framework offers a way to separate viral demonstrations from durable medical progress—by asking not only what a model can recognize, but where it works, why it works and whether it makes care better.

Subject of Research: Foundation models and their real-world evaluation in biomedical imaging and clinical medicine.

Article Title: Foundation models in biomedical imaging: turning hype into reality

Article References: Muneer, A., Zhang, K., Hamdi, I. et al. Foundation models in biomedical imaging: turning hype into reality. Nature Biomedical Engineering 10, 1557–1575 (2026). https://doi.org/10.1038/s41551-026-01762-z

Image Credits: AI Generated

DOI: 10.1038/s41551-026-01762-z

Keywords: foundation models, biomedical imaging, medical artificial intelligence, clinical AI, multimodal AI, domain shift, causal reasoning, clinical validation, responsible AI, REAL-FM

Schizophrenia may leave a distinctive biological footprint across the brain’s communication network. Using specialized PET imaging, researchers have mapped where synaptic connections are most reduced in people with schizophrenia and identified a possible region from which the damage begins to spread. The study, which involved a Rutgers professor, was published in Molecular Psychiatry. Synapses are [...]

How we sleep may have lasting impacts for our brain health as we age. A new University of