© Javier Jaén
Heart failure is a serious condition that occurs when a damaged heart loses its ability to pump blood around the body. It affects as many as 100 million people worldwide and it is a progressive disease such that five years after a diagnosis, 50% of patients with heart failure will be dead.
The UK-based company Ceryx Medical has created a new bioelectronic device called Cysoni, which is designed to adjust the pace of the heart as a patient breathes in and out. This mimics a normal physiological process called respiratory sinus arrhythmia, which can be absent in people with heart failure. The company has just began the first trial of Cysoni on human subjects.
This podcast features the biomedical engineer Stuart Plant and the physicist Ashok Chauhan, who are Ceryx Medical’s CEO and senior scientist respectively. In a wide-ranging conversation with Physics World’s Margaret Harris, they talk about how bioelectronics could be used treat heart failure and some other diseases. Chauhan and Plant also chat about challenges and rewards of developing medical technologies within a small company.
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Medical devices are thoroughly tested before being introduced into the clinic. But traditional testing approaches do not fully account for the diversity of patient populations. This can result in the launch to market of devices that may underperform in some patient subgroups or even cause harm, with often devastating consequences.
Aiming to solve this challenge, University of Leeds spin-out adsilico is working to enable more inclusive, efficient and patient-centric device development. Launched in 2021, the company is using computational methods pioneered in academia to revolutionize the way that medical devices are developed, tested and brought to market.
Sheena Macpherson, adsilico’s CEO, talks to Tami Freeman about the potential of advanced modelling and simulation techniques to help protect all patients, and how in silico trials could revolutionize medical device development.
Medical devices currently go through a series of testing phases before reaching the market, including bench testing, animal studies and human clinical trials. These trials aim to establish the device’s safety and efficacy in the intended patient population. However, the patient populations included in clinical trials often do not adequately represent the full diversity of patients who will ultimately use the device once it is approved.
Traditional clinical trials tend to underrepresent women, ethnic minorities, elderly patients and those with rare conditions. This exclusion occurs for various reasons, including restrictive eligibility criteria, lack of diversity at trial sites, socioeconomic barriers to participation, and implicit biases in trial design and recruitment.
As a result, the data generated from these trials may not capture important variations in device performance across different subgroups.
This lack of diversity in testing can lead to devices that perform sub-optimally or even dangerously in certain demographic groups, with potentially life-threatening device flaws going undetected until the post-market phase when a much broader patient population is exposed.
A poignant example is the recent vaginal mesh scandal. Mesh implants were widely marketed to hospitals as a simple fix for pelvic organ prolapse and urinary incontinence, conditions commonly linked to childbirth. However, the devices were often sold without adequate testing.
As a result, debilitating complications went undetected until the meshes were already in widespread use. Many women experienced severe chronic pain, mesh eroding into the vagina, inability to walk or have sex, and other life-altering side effects. Removal of the mesh often required complex surgery. A 2020 UK government inquiry found that this tragedy was further compounded by an arrogant culture in medicine that dismissed women’s concerns as “women’s problems” or a natural part of aging.
This case underscores how a lack of comprehensive and inclusive testing before market release can devastate patients’ lives. It also highlights the importance of taking patients’ experiences seriously, especially those from demographics that have been historically marginalized in medicine.
adsilico is pioneering the use of advanced computational techniques to create virtual patient populations for testing medical devices. By leveraging massive datasets and sophisticated modelling, adsilico can generate fully synthetic “virtual patients” that capture the full spectrum of anatomical diversity in humans. These populations can then be used to conduct in silico trials, where devices are tested computationally on the virtual patients before ever being used in a real human. This allows identification of potential device flaws or limitations in specific subgroups much earlier in the development process.
Virtual patients are created using state-of-the-art generative AI techniques. First, we generate digital twins – precise computational replicas of real patients’ anatomy and physiology – from a diverse set of fully anonymized patient medical images. We then apply generative AI to computationally combine elements from different digital twins, producing a large population of new, fully synthetic virtual patients. While these AI-generated virtual patients do not replicate any individual real patient, they collectively represent the full diversity of the real patient population in a statistically accurate way.
Medical devices can be virtually implanted and simulated in these diverse synthetic anatomies to study performance across a wide range of patient variations. This enables comprehensive virtual trials that would be infeasible with traditional physical or digital twin approaches. Our solution ensures medical devices are tested on representative samples before ever reaching real patients. It’s a transformative approach to making clinical trials more inclusive, insightful and efficient.
In the cardiac space, for example, we might start with MRI scans of the heart from a broad cohort. We then computationally combine elements from different patient scans to generate a large population of new virtual heart anatomies that, while not replicating any individual real patient, collectively represent the full diversity of the real patient population. Medical devices such as stents or prosthetic heart valves can then be virtually implanted in these synthetic patients, and various simulations run to study performance and safety across a wide range of anatomical variations.
The in silico approach using virtual patients helps protect all patients by allowing more comprehensive device testing before human use. It enables the identification of potential flaws or limitations that might disproportionately affect specific subgroups, which can be missed in traditional trials with limited diversity.
This methodology also provides a way to study device performance in groups that are often underrepresented in human trials, such as ethnic minorities or those with rare conditions. By computationally generating virtual patients with these characteristics, we can proactively ensure that devices will be safe and effective for these populations. This helps prevent the kinds of adverse outcomes that can occur when devices are used in populations on which they were not adequately tested.
In silico trials using virtual patients are intended to supplement, rather than fully replace, human clinical trials. They provide a powerful tool for both detecting potential issues early and also enhancing the evidence available preclinically, allowing refinement of designs and testing protocols before moving to human trials. This can make the human trials more targeted, efficient and inclusive.
In silico trials can also be used to study device performance in patient types that are challenging to sufficiently represent in human trials, such as those with rare conditions. Ultimately, the combination of computational and human trials provides a more comprehensive assessment of device safety and efficacy across real-world patient populations.
In silico trials have the potential to significantly reduce the use of animals in medical device testing. Currently, animal studies remain an important step for assessing certain biological responses that are difficult to comprehensively model computationally, such as immune reactions and tissue healing. However, as computational methods become increasingly sophisticated, they are able to simulate an ever-broader range of physiological processes.
By providing a more comprehensive preclinical assessment of device safety and performance, in silico trials can already help refine designs and reduce the number of animals needed in subsequent live studies.
Looking ahead, we envision a future where advanced in silico models, validated against human clinical data, can fully replicate the key insights we currently derive from animal experiments. As these technologies mature, we may indeed see a time when animal testing is no longer a necessary precursor to human trials. Getting to that point will require close collaboration between industry, academia, regulators and the public to ensure that in silico methods are developed and validated to the highest scientific and ethical standards.
At adsilico, we are committed to advancing computational approaches in order to minimize the use of animals in the device development pipeline, with the ultimate goal of replacing animal experiments altogether. We believe this is not only a scientific imperative, but an ethical obligation as we work to build a more humane and patient-centric testing paradigm.
Beyond improving device safety and inclusivity, the in silico approach can significantly accelerate the development timeline. By frontloading more comprehensive testing into the preclinical phase, device manufacturers can identify and resolve issues earlier, reducing the risk of costly failures or redesigns later in the process. The ability to generate and test on large virtual populations also enables much more rapid iteration and optimization of designs.
Additionally, by reducing the need for animal testing and making human trials more targeted and efficient, in silico methods can help bring vital new devices to patients faster and at lower cost. Industry analysts project that by 2025, in silico methods could enable 30% more new devices to reach the market each year compared with the current paradigm.
The use of in silico methods in medicine is rapidly expanding, but still nascent in many areas. Computational approaches are increasingly used in drug discovery and development, and regulatory agencies like the US Food and Drug Administration are actively working to qualify in silico methods for use in device evaluation.
Several companies and academic groups are pioneering the use of virtual patients for in silico device trials, and initial results are promising. However, widespread adoption is still in the early stages. With growing recognition of the limitations of traditional approaches and the power of computational methods, we expect to see significant growth in the coming years. Industry projections suggest that by 2025, 50% of new devices and 25% of new drugs will incorporate in silico methods in their development.
Our near-term focus is on expanding our virtual patient capabilities to encompass an even broader range of patient diversity, and to validate our methods across multiple clinical application areas in partnership with device manufacturers.
Ultimately, our mission is to ensure that every patient, regardless of their demographic or anatomical characteristics, can benefit from medical devices that are thoroughly tested and optimized for someone like them. We won’t stop until in silico methods are a standard, integral part of developing safe and effective devices for all.
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A team of scientists based in the US has developed a non-invasive headset device designed to track changes in blood flow and assess a patient’s stroke risk. The device could make it easier to detect early signs of stroke, offering patients and physicians a direct, cost-effective approach to stroke prevention.
Stroke remains the leading cause of death and long-term disability, affecting 15 million people worldwide every year. In the United States, someone dies from a stroke roughly every 3 min. Those who survive are often left physically and cognitively impaired.
About 80% of strokes occur when a blood clot blocks an artery that carries blood to the brain (ischaemic stroke). In other cases, a blood vessel can rupture and bleed into the brain (haemorrhagic stroke). In both types of stroke, deprived of oxygen from the loss of blood flow, millions of brain cells rapidly die every minute, causing devastating disability and even death.
As debilitating as stroke is, current methods for assessing stroke risk remain limited. Physicians typically use a questionnaire that assesses factors such as demographics, blood test results and pre-existing medical conditions to estimate a patient’s risk. While non-invasive techniques exist to detect changes after the onset of a stroke, by the time a stroke is suspected and patients are rushed to the emergency room, critical damage may have already been done.
Consequently, there remains an acute need for tools that can proactively monitor and quantify stroke risk before an event occurs.
Seeking to bridge this gap, in a study published in Biomedical Optics Express, a research team, led by Charles Liu of the Keck School of Medicine at the University of Southern California and Changhuei Yang of California Institute of Technology, developed a headset device to monitor changes in the brain’s blood flow and volume while a patient holds their breath.
“Stroke is essentially a brain attack. The stroke world has been trying to draw a parallel between a heart attack and a brain attack,” explains Liu. “When you have a heart disease, under normal circumstances – like sitting on the couch or walking to the kitchen – your heart may seem fine. But if you start walking uphill, you might experience chest pain. For heart diseases, we have the cardiac stress test. During this test, a doctor puts you on a treadmill and monitors your heart with EKG leads. For stroke, we do not have a scalable and practical equivalent to a cardiac stress test.”
Indeed, breath holding temporarily stresses the brain, similar to the way that walking uphill or running on a treadmill would stress the heart in a cardiac stress test. During breath holding, blood volume and blood flow increase in response to lower oxygen and higher carbon dioxide levels. In turn, blood vessels dilate to mitigate the pressure of this increase in blood flow. In patients with higher stroke risk, less flexible blood vessels would impede dilation, causing distinct changes in blood flow dynamics.
Researchers have long had access to various imaging techniques to measure blood dynamics in the brain. However, these methods are often expensive, invasive and impractical for routine screening. To circumvent these limitations, the team built a device comprising a laser diode and a camera that can be placed on the head with no external optical elements, making it lightweight, portable, and cost-effective.
The device transmits infrared light through the skull and brain. A camera positioned elsewhere on the head captures the transmitted light through the skull. By tracking how much the light intensity decreases as it travels through the skull and into the camera, the device can measure changes in blood volume.
When a coherent light source such as a laser scatters off a moving sample (i.e., flowing blood), it creates a type of granular interference pattern, known as a speckle pattern. These patterns fluctuate as blood moves through the brain – the faster the blood flow, the quicker the fluctuations. This technique, called speckle contrast optical spectroscopy (SCOS), enables the researchers to non-invasively measure the blood flow rate in the brain.
The researchers tested the device on 50 participants, divided into low- and high-risk groups based on a standard stroke-risk calculator. During a breath-holding exercise, they found significant differences in blood dynamic changes between people with high stroke risk and those at lower risk.
Specifically, the high-risk group exhibited a faster blood flow rate but a lower volume of blood in response to the brain’s oxygen demands, suggesting restricted blood flow through the stiff vessels. Overall, these findings establish physiological links between stroke risk and blood dynamics measurements, highlighting the technology’s potential for stroke diagnosis and prevention.
The team plans to expand these studies to a broader population to reinforce the validity of the results. “Our goal is to further develop this concept to ensure it remains portable, compact, and easy to operate without requiring specialized technicians. We believe the design is scalable, aligning well with our vision of accessibility, allowing diverse and underrepresented communities to benefit from this technology,” says co-lead author Simon Mahler, a postdoctoral scholar in the Yang lab at Caltech.
The researchers also aim to integrate machine learning into data analysis and conduct clinical trials in a hospital setting, testing their approach’s effectiveness in stroke prevention. They are also excited about the applications of their device in other neurological conditions, including brain injuries, seizures, and headaches.
The post Laser-based headset assesses stroke risk using the brain’s blood flow appeared first on Physics World.
Analysis of human breath can provide a non-invasive method for cancer screening or disease diagnosis. The level of isoprene in exhaled breath, for example, provides a biomarker that can indicate the presence of lung cancer. Now a research collaboration from China and Spain has used nanoflakes of indium oxide (In2O3)-based materials to create a gas sensor with the highest performance of any isoprene sensor reported to date.
For effective cancer screening or diagnosis, a gas sensor must be sensitive enough to detect the small amounts of isoprene present in breath (in the parts-per-billion (ppb) range) and able to differentiate isoprene from other exhaled compounds. The metal oxide semiconductor In2O3 is a promising candidate for isoprene sensing, but existing devices are limited by high operating temperatures and low detection limits.
To optimize the sensing performance, the research team – led by Pingwei Liu from Zhejiang University and Qingyue Wang from Institute of Zhejiang University – developed a series of sensors made from nanoflakes of pure In2O3, nickel-doped (InNiOx) or platinum-loaded (Pt@InNiOx). The sensors comprise an insulating substrate with interdigitated gold/titanium electrodes, coated with a layer of roughly 10 nm-thick nanoflakes. When the sensor is exposed to isoprene, adsorption of isoprene onto the nanoflakes causes an increase in the detected electrical signal.
“The nanoflakes’ two-dimensional structure provides a relatively high surface area and pore volume compared with the bulk structure, thus promoting isoprene adsorption and enhancing electron interaction and electrical signals,” Wang explains. “This improves the sensitivity of the gas sensor.”
The researchers – also from Second Affiliated Hospital, Zhejiang University School of Medicine and Instituto de Catálisis y Petroleoquímica, CSIC – assessed the isoprene sensing performance of the various sensor chips. All three exhibited a linear response to isoprene concentrations ranging from 500 ppb to the limit-of-detection (LOD) at the operating temperature of 200 °C. Pt@InNiOx showed a response at least four times higher than InNiOx and In2O3, as well as an exceptionally low LOD of 2 ppb, greatly outperforming any previously reported sensors.
The Pt@InNiOx sensor also showed high selectivity, exhibiting 3–7 times higher response to isoprene than to other volatile organic compounds commonly found in breath. Pt@InNiOx also exhibited good repeatability over nine cycles of 500 ppb isoprene sensing.
The team next examined how humidity affects the sensors – an important factor as exhaled breath usually has a relative humidity above 65%. The InNiOx and Pt@InNiOx sensors maintained a stable current baseline in the presence of water vapour. In contrast, the In2O3 sensor showed more than a 100% baseline increase. Similarly, the isoprene sensing performance of InNiOx and Pt@InNiOx was unaffected by water vapor, while the In2O3 response decreased to less than 0.5% as relative humidity reached 80%.
The team also used simultaneous spectroscopic and electrical measurements to investigate the isoprene sensing mechanism. They found that nanoclusters of platinum in the nanoflakes play a pivotal role by catalysing the oxidation of isoprene C=C bonds, which releases electrons and triggers the isoprene-sensing process.
As the performance tests indicated that Pt@InNiOx may provide an optimal sensing material for detecting ultralow levels of isoprene, the researchers integrated Pt@InNiOx nanoflakes into a portable breath sensing device. They collected exhaled breath from eight healthy individuals and five lung cancer patients, and then transferred the exhaled gases from the gas collection bags into the digital device, which displays the isoprene concentration on its screen.
The sensing device revealed that exhaled isoprene concentrations in lung cancer patients were consistently below 40 ppb, compared with more than 60 ppb in healthy individuals. As such, the device successfully distinguished individuals with lung cancer from healthy people.
“These findings underscore the effectiveness of the Pt@InNiOx sensor in real-world scenarios, validating its potential for rapid and cost-effective lung cancer diagnosis,” the researchers write. “Integrating this ultrasensitive sensing material into a portable device holds significant implications for at-home surveillance for lung cancer patients, enabling dynamic monitoring of their health status.”
Looking to future commercialization of this technology, the researchers note that this will require further research on the sensing materials and the relationship between breath isoprene levels and lung cancer. “By addressing these areas and finishing the rigorous clinical trials, breath isoprene gas sensing technology could become a transformative tool in the noninvasive detection of lung cancer, ultimately saving lives and improving healthcare,” they conclude.
“Currently, we’re cooperating with a local hospital for large-scale clinical testing and evaluating the potentials to be applied for other cancers such as prostate cancer,” Wang tells Physics World.
The researchers report their findings in ACS Sensors.
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