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.

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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.

 

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

 

 

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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.

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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.

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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.

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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.

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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.

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.

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© 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.

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© European Union | Copernicus Sentinel-2

A negative scan does not always mean recurrent prostate cancer is nowhere to be found. Sometimes, the disease may simply be too small or too faint to appear yet. New research suggests that repeating a PSMA PET scan later can reveal cancer that was missed the first time. Among patients with rising prostate-specific antigen (PSA) [...]

Naked mole-rats have long puzzled biologists with their striking eusocial structure, where most adults remain reproductively suppressed while a queen controls the colony’s future. Now, a new study identifies a chemical “queen odour” that links social status to endocrine control, revealing a mammalian analogue to insect queen pheromones.

The molecule, termed IPM, is enriched by the queen and acts as the key mediator of reproductive suppression in non-breeding females. When IPM is present, circulating prolactin rises while progesterone is suppressed, locking the colony into a stable reproductive hierarchy characterized by reduced conflict and maintained group cohesion.

Crucially, the team connects this behavioural outcome to the brain. Using multiple approaches—including non-invasive functional ultrasound imaging (fUSI)—they show that IPM provokes strong activation of olfactory neurons and central neural circuits. This provides a neural substrate for how a chemical cue can trigger colony-wide physiological change, rather than relying on social learning or passive association.

The experiments also clarify sensory routing. Ablating olfaction abolishes aggression initiation and lowers prolactin levels, directly tying IPM detection to reproductive suppression. While the findings are consistent with sensing through the main olfactory epithelium, the authors note that the vomeronasal system may still contribute.

Queen removal normally destabilizes colonies, leading to aggression, reproductive competition, prolactin decline, and, in extreme cases, mortality. Yet when IPM is added to queenless bedding, the colony remains stable for months. During this period, fecal progesterone metabolite levels stay low, indicating sustained ovarian quiescence alongside elevated prolactin.

The study also addresses a longstanding paradox: transferring soiled bedding and urine from parent colonies fails to maintain suppression in isolated individuals. The authors propose that IPM’s chemistry helps explain this discrepancy. As a long-chain fatty acid ester with low volatility, IPM may persist within the colony environment but fail to travel effectively over distance or survive transfer pathways that preferentially carry other chemicals.

Comparative work extends beyond naked mole-rats. IPM is detected in breeding females of four additional Fukomys species. Although reproductive suppression mechanisms may differ across species, the presence of a similar queen-linked odorant suggests convergent chemical strategies for managing reproductive skew in eusocial mammals.

Overall, the research reframes reproduction control as a chemosensory-to-endocrine pathway: IPM activates olfactory–hypothalamic signalling, elevating prolactin and inhibiting GnRH release. This “social hormone” model offers a new principle for social evolution—chemical cues can synchronize behaviour and physiology at the scale of an entire mammal colony.

Subject of Research: Reproductive suppression and social hierarchy in eusocial mammals (naked mole-rats and related Fukomys species)

Article Title: A queen odour mediates reproductive suppression in a eusocial mammal

Article References:

Khallaf, M.A., Hart, D.W., Luo, W. et al. A queen odour mediates reproductive suppression in a eusocial mammal.
Nature (2026). https://doi.org/10.1038/s41586-026-10772-5

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41586-026-10772-5

Keywords: IPM; queen odour; reproductive suppression; prolactin; GnRH; olfaction; eusociality; functional ultrasound imaging; Fukomys

Neuroscientists have long believed that language processing was only found in regions of the brain’s left hemisphere, including the well-known Broca’s area. However, new evidence suggests the brain’s language network is far more widespread than previously thought. 

Researchers at the Massachusetts Institute of Technology (MIT) analyzed functional magnetic resonance imaging (fMRI) scans from 772 participants and identified 17 additional brain regions involved in language comprehension. These newly discovered areas are distributed across the brain, including the cerebellum, hippocampus, amygdala, and several regions of the cerebral cortex. In total, this accounts for five percent of the adult brain volume (roughly the size of a large strawberry). 

“Even though there are all these distant components, it’s pretty restricted in terms of volume. You don’t need that much of the brain to do language,” says senior author Evelina Fedorenko, an MIT associate professor of brain and cognitive sciences and a member of MIT’s McGovern Institute for Brain Research, in a statement. 

Locating the Brain’s Hidden Language Centers

To find these hidden language-related regions in the brain, the researchers revisited years of brain imaging data collected since 2013. Additionally, participants completed a standard “language localizer” task, during which they read or listened to meaningful sentences and sequences of meaningless nonwords. By comparing brain activity between these two conditions, the research team identified regions that responded more strongly to language.

“When we initially started looking at language, in the first couple of papers, I tried to be comprehensive and include anything that seemed consistent across participants, and there was a huge amount of resistance,” said Fedorenko. “People would say things like, ‘Well, we know those are not language areas, so please focus on the language areas.’”

The team decided to adopt a more flexible statistical approach to detect weaker signals and identify previously overlooked language-sensitive regions beyond the classical language areas. They then compared these regions’ responses during a spatial working memory task to distinguish language-specific areas from those involved in what would be considered general cognitive functions.

Cerebellum Discoveries

Interestingly, five of the newly identified language regions are located in the cerebellum, a structure traditionally associated with movement and coordination. Some of these cerebellar regions were also active during non-language tasks, suggesting they may help integrate information across different brain systems rather than process language alone.

“It’s a very simple paradigm that lets you identify this core language system in individual brains,” says MIT postdoc Agata Wolna, the lead author of the paper.

Additional language-sensitive regions were identified in the hippocampus and amygdala, areas better known for their roles in memory and emotion. Their involvement suggests that understanding language may rely more on integrating memory, emotional context, and higher-order cognitive functions than previously understood in science.

“We always see this frontal temporal network, but there’s quite a lot of evidence that there are other regions that are also critical for language processing,” said Wolna. “By using a laxer threshold and zooming in on areas with weak MRI signal, we tried to maximize the chances of finding small and weakly responsive regions outside of this left frontal temporal system.”

The findings challenge long-held assumptions about the brain’s organization and can open new avenues for research into how language processing is understood in neuroscience.

The recent study was published in the Journal of Neuroscience. 

Chrissy Newton is a PR professional and the founder of VOCAB Communications. She currently appears on The Discovery Channel and Max and hosts the Rebelliously Curious podcast, which can be found on YouTube and on all audio podcast streaming platforms. Follow her on X: @ChrissyNewton, Instagram: @BeingChrissyNewton, and chrissynewton.com. To contact Chrissy with a story, please email chrissy @ thedebrief.org.

A groundbreaking imaging technology developed by researchers at the University of Oklahoma promises to revolutionize liver transplant evaluations by enabling more comprehensive and rapid assessments of donor organ health. With thousands of patients awaiting liver transplants nationwide, transplant teams urgently need tools that can deliver swift, accurate insights without invasive procedures. The team, led by Dr. Qinggong Tang, has adapted polarization-sensitive optical coherence tomography (PS-OCT)—an advanced imaging modality originally utilized in retinal scans—to noninvasively evaluate donor livers at high resolution.

Traditional liver assessments rely heavily on biopsies, where small tissue samples are extracted and examined for signs of disease or damage. While considered the gold standard, biopsies are inherently limited, providing only a microscopic snapshot of a vast organ that may vary in condition. This narrow sampling can cause uncertainty, sometimes leading to the discard of viable organs. The OU team sought to overcome these limitations by employing PS-OCT, which not only captures detailed cross-sectional images of liver tissue but also measures its polarization properties, revealing subtle structural and biochemical features linked to liver health.

The engineers designed a portable PS-OCT system optimized for clinical and operating room environments, allowing transplant teams to scan liver surfaces quickly and noninvasively. The imaging data are then analyzed using advanced artificial intelligence algorithms, co-developed with Dr. Chongle Pan’s computer science lab. These AI tools quantify critical parameters indicative of hepatic steatosis (fat accumulation) and inflammation, two primary factors influencing liver viability. By training their models on datasets from over thirty clinical cases—comparing PS-OCT results with biopsy outcomes and post-transplant organ function—the researchers demonstrated strong correlations between imaging biomarkers and actual clinical metrics.

Beyond providing a more holistic view of donor liver condition, PS-OCT could enhance decision-making speed, potentially expanding the pool of organs deemed suitable for transplantation by identifying healthy tissue regions missed by biopsy. Such improvements may reduce organ discard rates and improve transplant success. Encouraged by initial findings, the OU team plans a large multisite clinical trial involving institutions like UT Southwestern and the Cleveland Clinic to validate and refine this approach across diverse patient populations.

This work exemplifies OU’s interdisciplinary innovation platform that integrates optical bioimaging, machine learning, pathology, and organ perfusion technology to translate engineering advances directly into clinical practice. The researchers also intend to extend PS-OCT evaluation methods to other organs, including kidneys and lungs, widening the impact of this technology.

Supported by National Science Foundation grants and institutional partnerships, the team’s efforts mark a significant step toward redefining how transplanted organs are assessed. Dr. Tang emphasizes that this is just the beginning, envisioning a future where noninvasive, AI-enhanced imaging facilitates safer, faster, and more effective organ transplantation worldwide.

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Subject of Research: Donor liver evaluation using advanced imaging techniques
Article Title: Human donor liver viability evaluation with polarization-sensitive optical coherence tomography
News Publication Date: 24-Jun-2026
Web References: http://dx.doi.org/10.1126/scitranslmed.adv7124
Image Credits: University of Oklahoma / Ellie Pinter

Keywords

Organ transplantation, Optical coherence tomography, Polarization-sensitive imaging, Liver viability, Artificial intelligence, Biomedical imaging, Noninvasive diagnostics, Medical innovation

In the realm of modern ophthalmology, Optical Coherence Tomography (OCT) has revolutionized the way eye diseases are diagnosed and monitored. This non-invasive imaging technology allows clinicians to peer through the layers of the retina, producing detailed cross-sectional images that reveal the structural integrity of this delicate tissue without the need for surgical intervention. However, despite OCT’s widespread adoption and profound clinical impact, the technology is inherently limited by physical constraints that occasionally obscure or degrade the crucial information contained within these images. The newly developed Spatio-Temporal Optical Coherence Tomography (STOC-T) promises to address these limitations by fundamentally altering data acquisition methods, thereby enhancing image quality and diagnostic precision at the earliest possible stage.

OCT functions by directing light into the eye and interpreting the backscattered photons to construct images of internal structures like the retina and choroid. But the journey of photons through tissue is fraught with challenges. When light encounters complex biological matrices, it scatters unpredictably, producing signals that blend with those carrying useful information. This phenomenon, known scientifically as “optical crosstalk,” causes image blur and loss of contrast, making it difficult to discern fine detail that could be critical for early disease detection. Optical crosstalk is essentially a breakdown of coherence within the returning light, as photons from a single spatial point arrive dispersed across multiple detector pixels. For ophthalmologists and patients alike, this translates to potentially missed or delayed diagnosis of conditions where subtle changes herald disease onset.

The breakthrough reported by Professor Maciej Wojtkowski and his team from the International Centre for Translational Eye Research (ICTER) introduces STOC-T, an innovative approach that goes beyond conventional image enhancement techniques. Unlike software filters applied post-hoc to clean up images, STOC-T reforms the optics of data collection itself—imposing controlled, repetitive phase modulations onto the illuminating light during scanning. By employing distinct spatial phase masks that alter the wavefront of the illuminating beam, the system captures a series of images, each with a differently patterned illumination. Scattered photons, which respond chaotically to these phase shifts, become decorrelated and diminish upon averaging multiple frames. Conversely, photons faithfully reflecting true tissue architecture maintain coherent behavior, thus strengthening their visibility in the final reconstructed image.

This methodology can be likened to differentiating a single voice in a bustling crowd. Just as a consistent voice can be isolated amid random background chatter using adaptive recording strategies, STOC-T distinguishes meaningful optical signals from noise in real-time. This preemptive discrimination obviates the need for computational post-processing to “clean” images after data acquisition, which historically cannot fully recover information lost to scattering. Defensive design of the imaging process ensures that interfering signals are suppressed from the outset, preserving delicate cellular-level details of the retina and choroid that are indispensable for early diagnosis.

STOC-T’s performance has been rigorously tested both in laboratory settings and on living tissue. In one compelling demonstration, a standard imaging target obscured by a highly scattering artificial medium and even by rat skin rendered virtually invisible under conventional OCT. Upon integrating STOC-T’s phase modulation technique, the obscured structural features resurfaced with remarkable clarity. This experiment underscores the scale of the optical crosstalk problem and the potency of STOC-T, since the objects remained physically present but were previously hidden by noise.

The true clinical significance comes from STOC-T’s application to human retinal imaging, where it achieves a lateral resolution near five micrometers—a scale sufficiently fine to resolve individual photoreceptors and ganglion cells as well as intricate vasculature within the choroid. This extraordinary level of detail enables prospective monitoring of microscopic disease processes with an acuity far surpassing existing OCT methods. Additionally, STOC-T provides new insights through optoretinography (ORG), capturing functional responses of photoreceptors to flickering light stimuli at frequencies up to 45 Hz. These functional measurements resemble electrophysiological studies conducted via invasive patch-clamp techniques, suggesting that STOC-T could non-invasively monitor cellular function—a significant leap for early detection of retinal dysfunction before structural damage occurs.

The potential clinical impact of this technology cannot be overstated. Visual impairment affects more than 2.2 billion individuals worldwide, with over a billion cases attributable to conditions amenable to early diagnosis and intervention. Diseases such as glaucoma, diabetic retinopathy, and age-related macular degeneration often become irreversibly severe due to delayed detection. STOC-T’s capacity to enhance diagnostic accuracy at the cellular and functional levels augments the ophthalmologist’s ability to initiate timely therapies, potentially halting or reversing vision loss before clinical symptoms manifest.

Despite these promises, STOC-T remains an experimental technique, currently limited by technical demands. The system requires cutting-edge hardware, including a high-speed CMOS camera capable of capturing quarter-million frames per second and a tunable near-infrared laser source spanning 800 to 870 nanometers in wavelength. The immense data volume generated—exceeding 8.5 gigabytes per acquisition—also presents significant computational challenges for real-time processing and analysis. These hurdles explain why STOC-T is not yet in widespread clinical use, although ongoing advances in photonic hardware and data science are likely to mitigate these barriers.

Looking forward, the research team envisions enhancing the system’s flexibility using multimode optical fibers for phase modulation. Such fibers, with diameters around 50 micrometers and lengths extending to hundreds of meters, support hundreds of propagation modes. They offer the theoretical potential to reduce optical crosstalk noise by a factor approaching 30 without complex electronic controls, simplifying implementation while preserving image quality improvements.

Professor Wojtkowski emphasizes that STOC-T represents a transformative conceptual advance rather than a final product. The roadmap to widespread application involves optimizing speed, minimizing data volume, refining phase encoding strategies, and automating image reconstruction workflows. The foundational principle—shaping the acquisition process to preempt noise contamination—opens avenues not only for ophthalmology but also for diverse biomedical imaging fields where light scattering impairs image fidelity. This innovation exemplifies how deep physical understanding combined with technical ingenuity can reshape diagnostic imaging, making previously invisible biological details accessible and advancing patient care.

In summary, STOC-T addresses a critical bottleneck in OCT imaging by employing spatio-temporal phase modulation to separate meaningful tissue signals from scattered noise at the data collection stage. This technique enhances resolution, contrast, and functional imaging capability, holding significant promise for earlier and more accurate diagnosis of a broad spectrum of vision-threatening diseases. Although currently laboratory-bound due to technical demands, STOC-T’s theoretical and experimental foundations herald a new era in optical imaging, where noise is never blindly accepted but actively prevented from degrading our view of living tissue.

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Subject of Research: Human tissue samples

Article Title: Spatio-temporal optical coherence imaging and tomography for in vivo applications

News Publication Date: 25-May-2026

Web References: DOI: 10.1117/1.JBO.31.11.113504

Image Credits: Optica

Keywords

Optical coherence tomography, STOC-T, ophthalmology, retinal imaging, optical crosstalk, phase modulation, optoretinography, photoreceptors, biomedical optics, imaging noise reduction, high-resolution microscopy, optical imaging innovations

WASHINGTON — Vantor has chosen BAE Systems to build its next generation of high-resolution imaging satellites. The selection reunites Vantor with the former Ball Aerospace business that helped develop DigitalGlobe’s […]

The post Vantor selects BAE Systems to build next-generation imaging satellites appeared first on SpaceNews.

In the ever-evolving landscape of energy storage technologies, the lithium–sulfur (Li–S) battery stands out for its promise to deliver unmatched energy density and reduced costs compared to traditional lithium-ion batteries. However, progress toward commercial viability has been hampered by complex electrochemical challenges, particularly those involving interfacial charge transfer dynamics. A new study, recently published in Nature, unveils groundbreaking insights into the intricate reaction pathways occurring at solid–liquid interfaces within Li–S batteries, illuminating mechanisms that govern lithium sulfide deposition under realistic, lean electrolyte conditions.

The difficulty in understanding and controlling the conversion of lithium polysulfides (LiPSs) during battery cycling stems largely from the dynamic and highly concentrated nature of reactant distributions at the electrode–electrolyte interface. In these environments, the balance between surface-bound and dissolved species dictates the overall electrochemical performance. The new research deploys advanced in situ liquid-cell electron microscopy techniques to directly visualize these sophisticated concentration gradients, a feat that has eluded scientists due to the liquid phase’s complexity and the scales involved.

What emerges from this study is a revelation of concentration-driven phase segregation phenomena right at the interfacial layers, termed high-concentration interfacial layers (HCILs). These HCILs form as LiPSs aggregate in the immediate vicinity of the electrode surface during cycling, dramatically altering the local reaction environment. This segregation is not merely a passive occurrence; it actively directs charge-transfer dynamics by establishing competitive conditions between surface-mediated and solution-based pathways. The delicate interplay in these zones influences where and how lithium sulfide (Li₂S) nucleates and grows, directly impacting the battery’s capacity retention and cycling stability.

Complementing the microscopy observations, computational explorations using density functional theory (DFT) provide a molecular-level understanding of how LiPS clusters modify their electronic structure upon aggregation. The study identifies that the molecular geometries and orbital hybridizations evolve as LiPSs coalesce, resulting in enhanced electronic interactions that facilitate charge transfer through the concentrated clusters. This phenomenon fundamentally challenges previously held models which treated LiPS species largely as isolated entities, offering a new paradigm that accounts for collective behavior in highly concentrated solutions.

The practical implications of these mechanistic insights are far-reaching. By comprehending how HCILs govern interfacial reaction pathways, the researchers have engineered electrodes that strategically balance surface and solution reactions amidst high LiPS concentrations. These meticulously optimized electrodes enable impressive fast-charging capabilities—reaching rates up to 4 C with current densities of 26.8 mA cm⁻²—without compromising energy density, which itself surges beyond 400 Wh kg⁻¹. Such milestones represent a substantial leap forward in the achievable performance metrics for Li–S battery technology.

This study’s significance extends beyond the immediate Li–S chemistry, addressing a broader challenge in electrochemical energy conversion: capturing and controlling complex interfacial phenomena under dynamically varying concentrations. The in situ liquid-cell electron microscopy method establishes a powerful approach for observing reactive species and phase evolution directly in their native electrochemical environment. As such, it holds promise to transform research across a host of energy storage and catalysis systems where interfacial reactions dictate overall function.

Notably, this work also clarifies why lean electrolyte conditions—essential for practical high-energy devices—have historically been so difficult to optimize. The rich detail uncovered concerning phase segregation and charge transfer competition suggests that managing HCIL formation and stability may be the key to unlocking robust Li₂S deposition and dissolution cycles. Hence, this strategy could mitigate common pitfalls like cathode passivation and capacity fading, which have limited long-term operational viability.

The fusion of experimental visualization with robust theoretical modeling provides a comprehensive framework that future researchers and engineers can exploit to rationally design electrode interfaces. Moving past empirical trial-and-error, the ability to predict and control molecular aggregation behavior and its effects on electronic properties opens new avenues for tuning battery chemistry on an atomic scale. Such sophistication could accelerate the sustainable deployment of lithium–sulfur batteries within electric vehicles, grid storage, and portable electronics.

Moreover, the study emphasizes the profound impact of molecular-level phenomena on macroscopic performance metrics such as charging speed and energy density. By showing that the electrochemical environment at the nanoscale directly modulates reaction kinetics and phase evolution, this research bridges a critical knowledge gap between fundamental science and technological application. It highlights the necessity for integrated approaches combining real-time imaging, theoretical insight, and materials engineering.

Looking ahead, the insights gained are poised to influence material selection, electrolyte formulation, and cell architecture design. Tailoring these elements to favor desirable HCIL characteristics could lead to batteries that not only charge rapidly and store vast amounts of energy but also maintain stability over many cycles. Furthermore, the foundational principles elucidated may inspire innovations beyond lithium–sulfur systems, potentially transforming a range of emerging battery chemistries where interface complexity rules.

In conclusion, this remarkable study marks a pivotal advance in understanding and harnessing the subtleties of interfacial charge transfer within lithium–sulfur batteries. By unraveling the competitive reactions shaped by concentration gradients and molecular aggregation, it charts a pathway toward high-performance batteries ready for real-world demands. As energy storage solutions continue to underpin the clean energy transition, such elegant fusion of fundamental science and engineering innovation will be indispensable for propelling next-generation technologies.

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Subject of Research: Charge transfer and reaction mechanisms at solid–liquid interfaces in lithium–sulfur batteries.

Article Title: Revealing competitive interfacial reactions in high-energy Li–S batteries.

Article References:
Zhou, S., Pei, F., Zheng, Q. et al. Revealing competitive interfacial reactions in high-energy Li–S batteries. Nature (2026). https://doi.org/10.1038/s41586-025-09473-2

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41586-025-09473-2

Often hailed as “the window to the soul,” the human eye has long fascinated scientists and philosophers alike. However, beyond its poetic allure, emerging research suggests that the eyes provide a vital window into the health of the brain. A groundbreaking new study spearheaded by the University of Florida’s biomedical engineering department has unveiled a transformative approach to predicting Alzheimer’s disease risk factors by analyzing retinal photographs with cutting-edge deep learning algorithms. This development promises to revolutionize early detection and intervention for one of the most devastating neurodegenerative diseases.

Traditionally, diagnosing Alzheimer’s disease has been a challenge due to its insidious onset and prolonged development over decades. Most diagnostic protocols focus on detecting the disease in its later stages, by which time significant, irreversible brain damage has already occurred. Recognizing this limitation, Dr. Ruogu Fang and her interdisciplinary team leveraged advances in artificial intelligence and biomedicine to explore the retina—a part of the central nervous system accessible via non-invasive imaging—as a potential biosensor for Alzheimer’s risk.

The study capitalized on a rich database of over 40,000 retinal images collected from the UK Biobank, a comprehensive health resource with longitudinal patient data. Retinal photography is commonly conducted during routine eye exams, diabetes management, glaucoma monitoring, and cataract assessments. By harnessing these widely available, inexpensive images, the research team circumvented the need for costly, invasive, or logistically complex diagnostic tools such as MRI or PET scans, thus holding promise for scalable, population-wide screening.

Employing deep learning, a subset of machine learning characterized by neural networks modeled after the human brain, the researchers developed an AI framework that scrutinized retinal morphology. This model was able to discern subtle variations in retinal arteries, veins, and the optic nerve head, features imperceptible to the human eye but correlated strongly with known risk factors for Alzheimer’s disease. Such morphological markers serve as proxies for neurovascular health, which is crucial given Alzheimer’s recognized association with vascular impairment.

One of the most striking revelations of this research was the model’s capacity not only to predict intrinsic biological traits such as sex and blood pressure but also modifiable lifestyle factors that influence Alzheimer’s risk. The AI effectively inferred behaviors like smoking, alcohol consumption, and even insomnia from retinal images alone. This objective measurement circumvents the notorious unreliability of self-reported lifestyle data, often plagued by bias and inaccuracies, thereby enriching risk assessment precision.

As Dr. Fang points out, retinal morphology functions less like a mere clinical questionnaire and more like an integrative biological sensor reflecting a cumulative lifetime burden of neurovascular and lifestyle insults. This conceptual shift underscores the retina’s role in encapsulating decades of physiological data, transforming retinal imaging into a powerful tool for capturing both present health and future vulnerability.

Furthermore, the implications extend beyond risk prediction. Identifying individuals with early signs of retinal changes could trigger timely clinical interventions long before cognitive symptoms manifest. Such preclinical detection enables the implementation of protective lifestyle adjustments, pharmacological therapies, or cognitive training programs designed to delay or mitigate Alzheimer’s progression.

The pioneering work built on previous findings from Fang’s group that demonstrated retinal images’ utility in detecting active Alzheimer’s disease cases. However, this new model pioneers the field by focusing on prodromal markers and risk factors instead of established dementia, bridging a critical gap in neurodegenerative diagnostics.

Importantly, the use of retinal photographs democratizes neurovascular health monitoring. Since retinal imaging is non-invasive, accessible, and affordable, it offers the prospect of integrating brain health assessment into routine eye care, thus expanding preventive neurology beyond specialized clinical settings.

This research also exemplifies the synergy between computational science and biomedical engineering. By combining sophisticated image analysis algorithms with large-scale patient data, it transcends traditional diagnostic constraints, marrying technology with medicine in a profoundly impactful manner.

While challenges remain, including the need for further validation across diverse populations and integration with other biomarkers, this study sets a new paradigm for early Alzheimer’s disease risk stratification. The possibility of preemptive interventions guided by retinal AI analysis heralds a future where the devastating trajectory of Alzheimer’s may be altered through timely and personalized healthcare.

In summary, the novel application of deep learning to retinal photographs opens an unprecedented window into brain health, heralding a new era of precision neurodiagnostics. It moves us closer to the long-sought goal of identifying Alzheimer’s risk decades in advance, fostering opportunities for early intervention and ultimately, better patient outcomes.

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Subject of Research: People

Article Title: Prediction of Alzheimer’s disease risk factors from retinal images via deep learning: Development and validation of biologically relevant morphological associations in the UK Biobank

News Publication Date: 16-Jun-2026

Web References:
DOI: 10.1177/13872877261457650

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Keywords

Alzheimer’s disease, Neurodegenerative diseases, Neurological disorders, Eye, Retina, Artificial intelligence, Image analysis, Deep learning, Biomedical engineering, Neurovascular integrity, Retinal biomarkers, Predictive modeling

A groundbreaking fluorescent reporter enabling real-time visualization of biologically active iron and oxygen at the single-cell level inside living organisms has been developed by researchers at the Institute of Science Tokyo. This revolutionary tool, named LiON (labile iron and oxygen notifier), ushers in a new era for cellular biology by offering unprecedented insight into the dynamic interplay and distribution of these essential elements within living tissues. Published in the esteemed journal Cell Reports Methods, this innovation promises to transform the study of fundamental physiological processes and complex diseases, from oncology and liver pathology to neurodegenerative disorders and aging.

Iron and oxygen are indispensable to cellular function, acting as core contributors to energy production, metabolic regulation, and DNA synthesis. Despite their vital roles, maintaining the delicate balance and spatial distribution of labile ferrous iron and reactive oxygen species remains critical, as disruptions often lead to cellular damage and disease. Historically, the challenge has been the absence of imaging tools capable of monitoring these elements within living cells at sufficient resolution, particularly differentiating biologically active forms from inert reservoirs.

Traditional methods of detecting iron, such as histochemical staining or mass spectrometry, require fixed tissue samples, offering only static snapshots devoid of temporal context. Although advanced clinical imaging modalities like magnetic resonance imaging enable in vivo iron mapping, they lack cellular resolution and fail to discriminate between active and stored iron pools. This limitation impairs the ability to study cellular heterogeneity and microenvironmental influences critical to disease pathogenesis.

To bridge this gap, the team led by Professor Toshiro Moroishi and doctoral researcher Ayato Maeda ingeniously exploited the iron- and oxygen-sensitive regulatory domain of FBXL5, a protein known to undergo degradation in low-iron and low-oxygen conditions. By genetically fusing this domain to a red fluorescent protein while coupling it with a stable green fluorescent protein, they engineered a ratiometric biosensor. Variations in red-to-green fluorescence ratios directly reflect intracellular labile iron and oxygen levels, enabling precise, quantitative assessments at single-cell resolution in living tissues.

Generating transgenic mice expressing LiON, the investigators revealed remarkable gradients and heterogeneity in iron and oxygen distribution across various organs and within homogeneous cell populations. For instance, they identified spatially distinct iron accumulation proximal to portal veins in the liver, highlighting areas susceptible to oxidative stress and metabolic dysfunction. These nuanced observations suggest complex regulatory mechanisms governing iron homeostasis that could not previously be elucidated in vivo.

Beyond fundamental biology, the capacity to monitor labile iron and oxygen in living systems has profound clinical implications. Iron dysregulation and hypoxia are hallmarks of cancer progression, neurodegeneration, and age-related tissue decline. The ability to dynamically track these parameters within specific cell types allows for the investigation of pathophysiological processes at an unprecedented scale, facilitating the development and evaluation of targeted therapies.

Importantly, the modular genetic design of LiON enables conditional activation in defined tissues or cell populations using well-established genetic tools. This versatility empowers researchers to tailor investigations according to disease models or biological questions, drastically expanding the utility of this fluorescent reporter across scientific disciplines. It also paves the way for high-throughput screening approaches and longitudinal studies previously unattainable.

Technically, the LiON sensor leverages competitive degradation pathways mediated by FBXL5’s hemerythrin-like domain, which senses iron and oxygen availability to regulate protein stability. When iron and oxygen are sufficient, FBXL5 remains stable, maintaining the red fluorescence. Under conditions of deficiency, FBXL5 is degraded, reducing red fluorescence and shifting the fluorescence ratio. By normalizing against stable green fluorescence, this ratiometric approach cancels out confounding factors such as expression levels or photobleaching, ensuring robust quantification.

Further development of LiON may enable integration with multiphoton or super-resolution microscopy, amplifying the ability to investigate cellular metabolism and microenvironmental niches in unprecedented detail. Moreover, its application in live imaging models can accelerate translational research by offering real-time insights into therapeutic interventions and disease progression, a vital step toward precision medicine.

The researchers emphasize that understanding heterogeneity in iron and oxygen management at the single-cell level will clarify diverse cellular responses within tissues, facilitating the identification of vulnerable or resistant cell populations. This information is invaluable for unraveling mechanisms underpinning complex diseases, optimizing drug delivery, and minimizing off-target effects.

As the first fluorescent reporter capable of in vivo, single-cell resolution imaging of labile iron and oxygen, LiON represents a transformative leap forward. The integration of this tool into mainstream biomedical research has the potential to catalyze breakthroughs spanning from fundamental cell biology to clinical therapeutics, establishing a new paradigm in the visualization and understanding of elemental biology.

In the words of Professor Moroishi, “LiON opens a window into cellular microenvironments that were previously invisible, enabling us to observe and quantify critical biochemical landscapes that dictate health and disease. We anticipate that this technology will become an indispensable asset across many branches of life science research.”

With the establishment of the Institute of Science Tokyo, a visionary new institution formed through the union of Tokyo Medical and Dental University and Tokyo Institute of Technology, this breakthrough exemplifies a commitment to pioneering innovative science aimed at enhancing human well-being. The convergence of multidisciplinary expertise and cutting-edge technology exemplified by LiON underscores the promise of collaborative research to address some of the most pressing biomedical challenges of our time.

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Subject of Research: Animals
Article Title: In vivo visualization of bioactive iron and oxygen using LiON, the labile iron and oxygen notifier
News Publication Date: 8-May-2026
Web References: DOI: 10.1016/j.crmeth.2026.101431
Image Credits: Institute of Science Tokyo
Keywords: Life sciences, Cell biology, Fluorescent proteins, Biomolecules, Biochemistry, Iron, Oxygen, Human health, Diseases and disorders, Neurodegenerative diseases, Cancer research, Clinical medicine