Tracking the brain’s blood flow during neurosurgery represents one of the most critical and challenging parts of the operation. A brief interruption can mean the difference between permanent damage and full recovery, but it’s difficult to track blood flow across the surgical field.

Now, researchers at The University of Texas at Austin have developed a new way to monitor blood flow with standard camera hardware. The method, called sinusoidal intensity modulation speckle imaging (SIMSI), uses the physics of dynamic light scattering to image blood flow noninvasively, across a wide field of view and without high-speed cameras. The paper is published in the journal Proceedings of the National Academy of Sciences.

A new approach to tracking blood flow

“This has been a fundamental challenge in the field for a long time,” said Andrew Dunn, a professor in the Cockrell School of Engineering’s Department of Biomedical Engineering, whose lab published the finding. “SIMSI gives us a way to get quantitative, physically meaningful numbers from a technique that is already fast and practical enough to use in the clinic.”

In addition to neurosurgery, this technology could improve monitoring during cardiac surgery, help assess tissue viability in reconstructive procedures, guide treatment decisions in stroke care, and support research into conditions ranging from dementia to traumatic brain injury.

Building on earlier speckle imaging work
This research builds on a technique called laser speckle contrast imaging (LSCI), which has been widely used in biomedical research to image blood flow. When laser light illuminates tissue, the movement of red blood cells causes the resulting laser speckle pattern, a granular, shimmering image, to blur in proportion to how fast the cells are moving. By analyzing that blur, researchers can map blood flow across an entire field of view simultaneously, without touching the tissue or injecting dyes.

However, conventional LSCI provides only relative measurements. It can tell that flow increased or decreased, but not by exactly how much, or what the absolute blood flow is.

How SIMSI makes flow quantitative

SIMSI solves this problem by adding precise modulation to the laser illumination, varying light intensity in a sinusoidal pattern at a controlled frequency within each camera exposure. This seemingly simple addition, combined with a new imaging model, encodes information about fast blood flow dynamics into the speckle images, allowing researchers to make more quantitative, physically meaningful measurements without relying on high-speed cameras.

The result is a camera-based system that produces quantitative maps of rapid blood flow dynamics across a wide field of view without contacting the tissue.

Capturing fast dynamics with standard cameras

“Blood flow can change over many timescales, but the optical fluctuations that reveal how fast blood is moving are often too fast for standard cameras to sample directly,” said Hengfa Lu, a postdoctoral fellow in Dunn’s Functional Optical Imaging Laboratory who led the development of the SIMSI framework.

“By intentionally modulating the illumination during the exposure and using a newly derived imaging model, we can recover fast blood flow dynamics that would otherwise be averaged out. That gives researchers a more quantitative way to study how tissue is functioning, from stroke recovery in the lab to, eventually, surgical decision-making.”

From early research to clinical translation

Dunn has been working on this research for 25 years, dating back to documenting the first use of laser speckle to image brain blood flow in 2001. He translated this technology to the operating room for the first time in 2010. In 2024, Dunn worked with UT’s Discovery to Impact commercialization hub to license his SpeckleView surgical imaging platform to Austin-based medical device startup Dynamic Light.

Looking ahead, the team sees SIMSI as part of a broader effort to make fast, quantitative blood flow imaging more accessible. By pairing engineered illumination with a physics-based imaging model, the approach enables standard cameras to capture rapid dynamics while maintaining long, light-efficient exposures. That could open new opportunities in stroke research, brain injury, surgery and other areas where blood flow is an early signal of tissue health.

Autism spectrum disorder (ASD), commonly referred to as autism, is a neurodevelopmental condition characterized by differences in social interactions, communication, behavior and the processing of sensory stimuli. Notably, the experiences, aptitudes and needs of autistic people can vary significantly.

Some neuroscientists have been exploring the possibility that this well-documented diversity partly reflects differences in the brain’s organization and underlying neurobiology. However, so far only a few studies have been able to link differences in autistic behavior to specific neurobiological processes.

Researchers at the Italian Institute of Technology’s Center for Neuroscience and Cognitive Systems (CNCS@UNITN) and the Child Mind Institute in New York carried out a study aimed at better delineating the brain connectivity patterns associated with autism.

Their findings, published in Nature Neuroscience, led to the identification of two distinct autism subtypes characterized by distinct connectivity patterns.

“This study was inspired by a certain frustration with how neuroimaging findings in autism have often been interpreted,” Alessandro Gozzi, senior author of the paper, told Medical Xpress.

“Autism is extremely heterogeneous clinically, and for many years imaging studies have also reported heterogeneous and sometimes apparently conflicting findings: some studies found reduced functional connectivity, others found increased connectivity, and others found more complex patterns.

“This led to a long debate about what this variability means. In many cases, it was treated as noise, a manifestation of the reproducibility crisis in neuroscience, or as a problem to be averaged away.”

In collaboration with Adriana Di Martino and her colleagues at the Child Mind Institute, Gozzi and his team at IIT set out to test a new hypothesis. Specifically, they hypothesized that widely reported differences in the brain connectivity patterns of autistic individuals are not random “noise,” but they are instead biologically meaningful.

“In other words, we wanted to determine whether different patterns of brain connectivity in autism reflect different underlying biological mechanisms,” said Gozzi.

“To test this idea rigorously, we designed a cross-species study. We started from mouse models, because in mice we can study autism-relevant genetic and biological perturbations under controlled experimental conditions, and we can more directly interrogate causal mechanisms.”

Studying the connectivity patterns linked to autism

As part of their study, Gozzi and his colleagues studied the brain connectivity patterns associated with 20 different mouse models of autism, as well as those captured in human patients. These are mice that are genetically engineered in different ways that prompt them to exhibit behaviors like those observed in autistic people.

First, the researchers tried to determine whether the highly varied connectivity patterns that emerged across different mouse models of autism could be linked to specific molecular and cellular processes. Subsequently, they tried to determine whether the same patterns could also be observed in brain imaging data collected from autistic people.

“Our broader goal was to turn what has often been seen as a limitation of autism imaging—its variability—into a mechanistic clue,” said Gozzi. “Rather than asking whether the autistic brain is simply more connected or less connected, we asked whether there are distinct connectivity subtypes that point to different forms of underlying biology.”

To capture the connections between different brain regions in both mice and humans, the team used resting-state functional MRI (fMRI). This is an imaging technique that records brain activity that spontaneously emerges when humans and animals are awake but not engaged in any task, by tracking the flow of blood in the brain.

“In imaging neuroscience, we quantify this communication using a measure called functional connectivity,” explained Gozzi. “Put simply, if the activity of two brain regions fluctuates together over time, we infer that these regions are functionally connected.”

“Our approach had three main steps. First, we measured functional connectivity across a large panel of mouse models relevant to autism, each carrying different autism-associated genetic or biological perturbations. Second, we asked whether these models naturally grouped into distinct connectivity patterns, and whether those patterns could be linked to different molecular or cellular mechanisms.”

The final part of the team’s study was aimed at determining whether the connectivity subtypes observed in mice, or corresponding subtypes, were also present in humans. To do this, Gozzi closely collaborated with Di Martino and her colleagues at the Child Mind Institute, who collected brain scans from autistic and non-autistic children.

“The key idea was to use the mouse data as a biological “Rosetta Stone: if a given connectivity pattern in mice is associated with synaptic biology, or with immune-related mechanisms, we can then look for similar patterns in human brain scans,” said Gozzi. “In this way, the animal models helped us interpret human imaging heterogeneity in mechanistic terms, rather than only describing it statistically.”

Two different autism connectivity subtypes

The analyses carried out by Gozzi, Di Martino and their colleagues ultimately led to the identification of two different patterns in functional connectivity that were observed both in mouse models of autism and humans.

The first of these patterns was characterized by a reduced communication between brain regions (i.e., hypoconnectivity), while the other entailed an increased communication between brain regions (i.e., hyperconnectivity).

“Importantly, these patterns did not simply entail ‘more’ or ‘less’ connectivity everywhere, but organized brain-wide patterns that could be linked to distinct biology,” said Gozzi.

“This suggests that at least part of autism heterogeneity can be parsed into biologically meaningful brain-connectivity subtypes. The hypoconnected subtype was linked to synaptic pathways, suggesting altered communication between neurons.

“By contrast, the hyperconnected subtype was associated with immune-related pathways and with alterations in gene regulation, suggesting that neuroimmune mechanisms and dysregulated transcriptional programs may contribute to a different form of circuit dysfunction.”

The team’s observations could improve the neuroscientific understanding of autism. If they are validated in further studies, they could eventually also inform the development of new support tools and practices that account for differences between individual patients.

“Two individuals may both receive an autism diagnosis, and may even show overlapping behavioral features, but the brain and molecular mechanisms contributing to their condition could be quite different,” said Gozzi.

“That distinction matters if we want to move toward more precise and personalized interventions. I would emphasize that this is not yet a clinical diagnostic tool. However, it provides a framework for future precision psychiatry in autism.

“Instead of asking only whether the autistic brain is ‘more connected’ or ‘less connected,’ we can begin to ask which circuit-level subtype is present, what biology it reflects, and whether different subtypes might respond differently to interventions.”

Directions for future research

The findings of this study could soon inspire more studies exploring the functional connectivity patterns associated with ASD. Meanwhile, Gozzi and his colleagues are planning further research aimed at understanding what experiences and behavioral differences might be associated with the two connectivity subtypes that they uncovered.

“Together with Di Martino and her colleagues, we want to apply this approach to human datasets that include not only high-quality brain imaging, but also deep phenotyping: detailed information about cognition, sensory symptoms, development, adaptive functioning, clinical history, genetics, and other biological measures,” explained Gozzi.

“This will be essential to understand how these brain-based subtypes relate to the real-world diversity of autism.”

As part of their next studies, Gozzi and his colleagues also hope to delineate the features of the autism subtypes they uncovered in greater detail. While they have so far identified two subtypes, they believe that there may be additional ones.

“If we can build richer and larger mouse imaging datasets, including more models and more biological perturbations, we may be able to partition this space more finely—perhaps identifying three, four, or more mechanistically distinct subtypes,” said Gozzi. “This will take years, but it is an important direction we are already working on.”

The researchers also hope to pin-point the physiological processes associated with hyperconnectivity and hypoconnectivity. They have already come up with a few hypotheses regarding these patterns’ underlying physiology, which they are currently testing experimentally.

“We are now trying to decode these fMRI signals in terms of neuronal activity, circuit dynamics, and excitation/inhibition balance,” added Gozzi. “Ultimately, we want to go beyond demonstrating that these connectivity patterns exist, also understanding what they imply for brain function and dysfunction.”

Chromosomal instability is a common feature in many solid tumors and is associated with greater aggressiveness. For years, its main contribution to cancer was thought to be driving the evolution of tumor genomes, causing cells to gain chromosomes with growth-promoting genes or lose chromosomes with tumor-suppressor genes.

A study led by Dr. Marco Milán’s laboratory at IRB Barcelona shows that chromosomal instability promotes tumor growth through a completely new mechanism. The work, published in EMBO Reports, reveals that tumor cells with an incorrect number of chromosomes can become senescent and release signals that alter the behavior of neighboring tissues.

The study, conducted in Drosophila melanogaster, shows that these senescent cells not only promote tumor growth and invasiveness but also induce the death of nearby healthy cells. This, in turn, fuels the tumor’s growth.

“What we are seeing is that the tumor doesn’t just grow due to its own internal alterations. It also interacts with the surrounding healthy tissue, preventing its cells from proliferating and ultimately killing them. And that process is necessary for the tumor to grow further,” explains Dr. Marco Milán, an ICREA researcher and head of the Development and Growth Control laboratory at IRB Barcelona.

Cells that stop dividing but remain active

When a cell has an incorrect number of chromosomes—a condition known as aneuploidy—its internal functioning becomes unbalanced. When cells can no longer properly manage the accumulated damage, they trigger alarm signals. Often, they enter senescence, a state in which they stop dividing, increase in size and release molecules that influence surrounding cells.

In principle, senescence is a protective mechanism. In specific situations, such as an injury, senescent cells help attract the immune system to repair tissue. The problem arises when these cells are not cleared and persist in the body chronically. When that happens, their sustained activity can generate inflammation and promote pathological processes such as aging or cancer.

In this work, the team studied senescent cells caused by aneuploidy—that is, cells with an abnormal number of chromosomes. Although these cells present highly diverse alterations, the researchers observed that they share a common response. This shared program includes halting cell division, activating stress response mechanisms and an increased capacity to secrete signals into their environment.

The research was carried out in Drosophila melanogaster, a model that allows scientists to observe in a living organism how cells with chromosomal instability interact with surrounding tissues. Many of the behaviors described here, such as aneuploidy, senescence and the secretion of inflammatory signals, have also been observed in mammalian cells.

A tumor that modifies its environment

Milán’s laboratory has spent more than a decade studying how chromosomal instability contributes to tumor development. Previous work by the group had identified signals secreted by senescent cells that promote tumor growth, invasion and systemic effects on the body.

In this new study, the team describes a fourth function of these cells: their ability to damage neighboring healthy tissue. The researchers identified several molecules secreted by senescent cells that act on nearby normal cells. Some of these molecules, such as Dilp8 (relaxin in humans) and ImpL2 (IGFBP7 in humans), reduce the proliferation of neighboring cells. Others, such as the cytokines Upd1 and Upd3 (IL-6 in humans) and Eiger (a molecule equivalent to TNF in mammals), help induce the death of these nearby healthy cells.

“We know that the tumor needs these neighboring cells to die in order to grow more. What we don’t yet know for sure is why. One possibility is that the death of these cells releases nutrients, such as amino acids or other metabolites, which the tumor can then exploit,” explains Kaustuv Ghosh, co-first author of the study alongside Aishwarya Kunchur.

Moving forward, the team wants to study the heterogeneity of these aneuploid senescent cells with greater resolution.

To do this, “we plan to use single-cell analysis to see if the gain or loss of specific chromosomes is associated with specific behaviors within the tumor,” Dr. Milán says.

Most folks know intermittent fasting helps with weight loss, usually by limiting your daily eating window or cutting calories a couple of times a week. But does your age change how well this works for you—and might there be some hidden dangers?

Intermittent fasting, such as time-restricted eating or the 5:2 diet, is very popular. With this eating method, you will consume your food within an eight-hour period and fast the rest. With the 5:2 diet, you eat normally five days of the week and drastically cut your calories on the other two days. Many studies show that these techniques are effective, but whether the effectiveness is distributed equally across the population, and especially among people of different ages, is unknown.

A recent comprehensive analysis of 28 clinical trials involving over 1,800 adults shows that intermittent fasting (IF) cuts down body weight and BMI, irrespective of age and sex. However, this deep dive into the data uncovers a surprising truth: the metabolic journey on IF is far from uniform.

A 20-something experiences a distinctly different set of physiological adaptations from those of a 60-year-old. This age-dependent response challenges the traditional one-size-fits-all approach to fasting, revealing that while weight loss is universal, the underlying health impacts are profoundly influenced by where you are in life.

When fat loss comes at the price of muscle

Health isn’t just about the number on the scale. Alarmingly, the study, published in the journal Nutrients, found that in many groups a large share of pounds lost was lean mass, not just fat.

In fact, weight loss often carries a known penalty: typically, 20–30% of the dropped weight is muscle. One fasting trial reported 65% of the weight loss came from lean tissue. Without careful planning, those who fast can end up thinner and weaker. That might be especially dangerous for older adults, who naturally lose muscle each year.

It is essential to prevent muscle loss. Increasing research indicates that strength training and a higher intake of protein can replace the fasting lean-mass drains. For instance, scientists found that exercise paired with a time-restricted eating plan preserved muscle: fasters who also did aerobic or resistance workouts lost fat but preserved their lean tissue.

A study of alternate-day fasting in conjunction with exercise resulted in a loss of 6 kg of body weight (5 kg fat, 0 kg muscle) and even dropped LDL cholesterol by 12%. The takeaway: fast with weights. Experts now recommend that anyone on an IF diet should boost protein intake and do resistance training to help the muscles hang on during the calorie cuts.

A surprise: Rising LDL

Even as many health markers improve on IF, one finding raised eyebrows: bad cholesterol rose on average, across age groups. Prior analyses had reported that intermittent fasting typically lowers LDL cholesterol, yet this review saw just the opposite.

After fasting, participants’ LDL-C tended to creep up in most age brackets. This was enough to set off alarm bells. As one of the authors warns, “the generalized risk of LDL-C elevation across age strata dictates a mandate for vigilant lipid monitoring.” Therefore, even if your blood sugar and triglycerides improve, regular cholesterol tests are crucial for those on fasting diets to ensure heart health.

Fasting with safeguards: protein, weights and checks

It should be noted that this is not a general condemnation of fasting. According to the authors, IF is “an effective weight-management tool,” not a cure for all. It’s all about customizing. This involves, in practice, fasting and muscle-friendly activities. Fueling up on lots of high-quality protein combined with gym activity—especially as we age—can help preserve strength.

Harvard experts, for example, found that exercise added to a feeding-window diet prevented muscle loss found in fasters without exercise. Meanwhile, check your lipids: getting screened for cholesterol is a good idea while fasting if you make it a habit.

What’s the conclusion? Indeed, intermittent fasting will help lose weight for the young and old. Age plays a role in side effects. According to one of the study’s authors, “IF is an effective weight-management tool but elicits distinct, age-specific metabolic trajectories.”

In your 20s, then, fasting is metabolically different from fasting in your 60s. In the future, research will determine ways to preserve muscle and manage LDL spikes. Until that time, eat smart: treat interventional fasting like a tool in a toolbox of healthy habits, not a cure-all.

Non-invasive eye scans allow doctors a zoomed-in, three-dimensional look beneath the eye’s surface without causing discomfort or pain to the patient. Used routinely in clinics worldwide, the scans produce detailed views of individual layers of the eye’s interior to help diagnose conditions that threaten vision. But with that level of precision comes a flood of data—hundreds of images per scan that physicians have to review manually, a time-consuming process that is vulnerable to human error.

Now, researchers at Washington University School of Medicine in St. Louis, in collaboration with colleagues at the University of Washington in Seattle and Genentech, Inc., have developed an experimental artificial intelligence (AI) system that can speed the scan review process and help doctors spot subtle signs of eye disease sooner. The technology, called OCTCube-M, includes a family of three AI models that are designed to read and interpret 3D images of the eye’s retina as well as other types of eye scans.

In a new study, the researchers found that, compared with older models, the new AI system more accurately identified eight different retinal diseases, including age-related macular degeneration, a common disease that damages the retina and is the leading cause of blindness in people over 50. It also was more accurate in its predictions of how fast a severe form of this condition, called geographic atrophy, would progress.

The findings describing the technology in its research stage were published recently in Nature Biomedical Engineering.

“Today’s eye scans provide physicians an unprecedented, highly detailed view of the inside of the eye, revealing structures and subtle changes that would otherwise go undetected,” said the study’s co-corresponding author Aaron Lee, MD, the Arthur W. Stickle Distinguished Professor of Ophthalmology and Visual Sciences and head of the John F. Hardesty, MD Department of Ophthalmology & Visual Sciences at WashU Medicine.

“But we still lack the tools to help physicians process the volume of generated images. Our AI system has the potential to empower physicians to make faster diagnoses, tailor treatment more precisely and design clinical trials that bring new therapies to patients faster.”

Additionally, the study showed that the model could infer health risks beyond the eye, predicting outcomes such as heart attack, stroke and kidney failure based solely on retinal imaging. The tiny blood vessels in the retina are anatomically and developmentally the same as those in the kidney, and the processes that lead to plaque buildup inside the walls of blood vessels that feed the heart and brain also leave signatures in the eye.

“The model has the potential to turn a simple eye exam into a powerful tool for helping to detect illness beyond the eye,” said Lee. “It opens the door to earlier detection, more precise monitoring and potentially better outcomes for patients who might otherwise go undiagnosed until their disease is far more advanced.”

A diagnosis needle in a haystack of data

At least 2.2 billion people worldwide have vision impairment, according to the World Health Organization. The imaging method known as optical coherence tomography has transformed the diagnosis and care of conditions that cause vision loss by generating, via a single and swift scan, hundreds of cross-sectional images that together form a detailed, 3D picture of the retina and the optic nerve. It can reveal early signs of different eye diseases such as glaucoma, macular degeneration and diabetic retinopathy, among other conditions.

AI, meanwhile, has become a powerful tool for processing large medical datasets, and Lee and colleagues have made notable contributions to the field. Several years ago, they published, in Nature, results describing a model that is better at diagnosing eye disease in two-dimensional retinal images compared to older models.

Because the model was trained on 2D tomography images, the researchers sought to determine if adding 3D tomography images could further improve disease diagnosis and prognosis. Because disease often extends in all three dimensions around the fovea, a small pit in the center of the retina responsible for the sharp, detailed vision required to read text and recognize faces, they hypothesized that training models on 3D images would provide more complete and accurate views of the tissue. To that end, more than 26,000 3D optical coherence tomography images comprising 1.62 million individual retinal slices—cross-sectional images of the retina—were used to train OCTCube-M.

When compared to the model trained on 2D images, OCTCube-M more accurately identified six of the eight retinal diseases by about four to six percentage points. That translates to the tool finding 43 to 60 additional cases out of every 1,000 individuals with eye disease. This was true across scans taken from individuals at multiple clinical sites, imaging modalities and diverse patient populations.

The eight diseases identified by the model include serious conditions that primarily affect the back of the eye, including the retina and optic nerve. Together they are the leading causes of vision loss and are linked to other conditions such as diabetes, hypertension and cardiovascular disease.

The researchers, including Cecilia S. Lee, MD, the Jane Hardesty Poole Distinguished Professor in ophthalmology and visual sciences at WashU Medicine; Sheng Wang, Ph.D., an assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington; and Miao Zhang, Ph.D., a senior AI scientist at San Francisco-based biotech company Genentech, then adapted the 3D model by adding data from two other eye imaging techniques—infrared retinal imaging and fundus autofluorescence imaging.

By combining optical coherence tomography with one or both of the other imaging types, the AI models can construct a more complete view of the eye and a deeper understanding of what’s happening inside, Aaron Lee explained. Indeed, the model trained on all three imaging types excelled at predicting the growth rate of the severe form of macular degeneration, geographic atrophy, significantly outperforming the current state-of-the art model that trains only on fundus autofluorescence images of the retina by an average of nearly 50%.

Geographic atrophy affects about 5 million people worldwide, and there are few effective treatment options. By providing information on the growth rate of the condition, Lee and colleagues’ tool could effectively detect and classify the stage of the illness, information that researchers could use to design better clinical trials of potential therapies for the disease.

“By better predicting how fast disease will worsen, we can run smaller, more efficient studies,” Lee said. “That could lower costs, shorten the time it takes to test new therapies, reduce the number of people exposed to treatments that don’t work and help effective drugs reach patients sooner.”

Next, the WashU Medicine researchers will train OCTCube-M with larger datasets encompassing more patients, more diseases and even more types of imaging data to continue improving upon it.

Wearable devices like smartwatches and fitness trackers have revolutionized the way we monitor our health. Worn around the clock, these devices quietly collect valuable data—from heart rate and blood oxygen levels to sleep quality—giving users a real-time window into their well-being without disrupting their daily lives.

A recent study has found a way to track heart rate without requiring people to wear anything at all. Instead, all they need is for someone to use their smartphone, which is quite convenient, since the average person already spends upwards of five hours a day on their phone.

This technology, supported by deep learning, captures an eight-second video using the front camera every time a user unlocks their phone. The video information is then used to passively monitor heart rate from a distance by detecting subtle changes in skin color caused by blood flow. To ensure participants’ privacy was not compromised, videos remained on their devices until they were manually reviewed and approved for upload.

According to findings published in Nature, the system not only met industry accuracy standards, keeping heart-rate measurement errors below 10%, but also maintained its accuracy across all skin tones—a win, given that many existing technologies perform poorly on darker skin tones.

Keeping a close watch on the heart

The heart does the crucial job of circulating oxygen and nutrient-rich blood throughout the body to keep all the cells able to respire and perform their specific tasks. Heart rate, especially the resting heart rate , provides a window into how the heart is functioning, as well as our overall health and energy levels. Measuring resting heart rate in a clinical setting requires a person to rest for prolonged periods, which makes long-term tracking difficult.

Wearables solve this by passively collecting heart rate data throughout the day, when one is resting or sleeping, to estimate RHR, thereby revealing useful changes in cardiovascular health and illness. However, such wearables are not accessible to most people; fortunately, smartphones are.

Studies show that 69% of adults globally and 90% in the US own a smartphone, with an average of 144 interactions with the device each day.

For this study, the researchers recruited 696 participants with diverse ages, sexes, and skin tones. More than 192,000 videos from 485 participants were used to train the neural network for their new passive heart-rate monitoring (PHRM) system. It measured the heart rate using a technique called remote photoplethysmography (rPPG).

With every heartbeat, a surge of blood passes through the vessels in the face, slightly altering the amount of light reflected by the skin. These changes, though invisible to the naked eye, can be captured on a smartphone’s camera, allowing the system to measure a person’s heart rate.

After the system was up and running, a separate group of 211 participants was used to validate its performance in both laboratory and real-world environments.

The data indicated that a smartphone can accurately measure heart rate under different lighting conditions. The team tested PHRM against reference electrocardiograms and found heart rate measurements fell within 10% error across all skin tones and met industry accuracy standards.

By stitching together several short measurements across the day, the system estimated daily resting heart rate nearly as accurately as a professional-grade wearable strap.

Once privacy concerns are addressed and the technology is validated in larger populations, a system like PHRM has the potential to put heart-health monitoring in the hands of billions, including communities lacking access to traditional health care systems. The ability of smartphones to measure heart rate also leaves us with the question of what other health signals could we monitor using our phones?

Written for you by our author Sanjukta Mondal, ed

Infants exposed to the Zika virus during pregnancy may face hidden developmental challenges, even if they appear healthy at birth. A recent study at the University of Wisconsin-Madison highlights the need for better developmental screening during a child’s first year of life.

Zika virus is known to cause severe birth defects—such as brain damage and microcephaly, a much smaller head and brain. However, little is understood about why 30% of babies born without physical symptoms go on to experience developmental problems including vision and hearing loss.

To better understand what happens to newborns affected by Zika infection, UW-Madison occupational therapy professor Karla Ausderau and others studied pregnant rhesus macaque monkeys at the Wisconsin National Primate Research Center. The animals were exposed to Zika virus or a placebo early in pregnancy. The scientists followed the resulting infant monkeys through one year with behavioral tests, vision and hearing assessments, and social observations.

They found that, like human babies, monkeys with prenatal virus exposure, regardless of maternal vaccination status, had increased risk of vision delays, hearing loss and changes in maternal attachment despite having no outward symptoms at birth. The researchers recently published their findings in the journal Nature Communications.

Although the infant monkeys’ eyes appeared structurally normal, researchers found disruptions in how the eyes communicated with the brain, an issue known as cortical visual dysfunction. This type of visual impairment is also seen in children who struggle with vision despite having healthy eyes. Early visual delays appeared in the monkeys as early as 3 months of age; however, those differences resolved by 12 months. The early vision changes did not predict later developmental challenges, however, and researchers say these early disruptions may signal broader effects of prenatal exposure.

“Infants exposed to Zika before birth showed altered social-emotional development and changes in cortical visual function during infancy, even when they appeared healthy at birth. And we couldn’t predict those outcomes from the mother’s infection characteristics, which is a problem if we’re trying to identify which babies need closer follow-up,” says Emma Mohr, UW-Madison pediatrics professor and co-author of the new study.

The researchers also found that hearing loss appeared more often in Zika-exposed infants than in unexposed animals, although the difference was not statistically significant.

Social behavior told another important story. Zika-exposed infants spent more time clinging to their mothers than typically expected at this age and gained more weight than the control group due to increased access to nursing. In rhesus macaques, close maternal contact normally decreases as infants grow more independent. Researchers believe this prolonged attachment may reflect difficulties with sensory processing, emotional regulation and assessing threats, skills that are critical for healthy social development.

Zika-exposed infants also showed lower inhibition by approaching new objects and situations more quickly than expected. This behavior may signal early anxiety, delayed emotional learning, or challenges in interpreting sensory information from their environment, which is risky.

The study found that maternal virus levels, placental infection and antibody responses did not predict which infants experienced developmental differences, suggesting that common maternal biomarkers are poor indicators of a child’s long-term risk. Human studies have shown that Zika infection during pregnancy can persist for months and increase the risk of miscarriage and brain abnormalities. However, those severe outcomes were not observed in this animal study—limiting conclusions about how maternal immune responses relate to the most severe cases.

The findings do point to a clear message. Prenatal Zika exposure alone can influence early development, even in the absence of visible birth defects.

“Children with prenatal Zika exposure need long-term neurodevelopmental follow-up, not just a clean bill of health at birth. The subtle differences we’re detecting wouldn’t be picked up on a routine exam, but they’re the kinds of things that can shape learning, behavior and social development as kids grow,” Mohr says.

The study strengthens the case for routine developmental monitoring of all children exposed to Zika during pregnancy, regardless of symptoms at birth, according to the authors. Early detection could allow for timely interventions when delays emerge. They also stress that prevention remains the strongest defense.

“Vaccines and mosquito control are still the best tools we have,” says Mohr. “Once infection occurs, the damage may already be done.”