Atlas of human brain blood vessels highlights changes in Alzheimer’s disease MIT researchers characterize gene expression patterns for 22,500 brain vascular cells across 428 donors, revealing insights for Alzheimer’s onset and potential treatments. Your brain is powered by 400 miles of blood vessels that provide nutrients, clear out waste products, and form a tight protective barrier — the blood-brain barrier — that controls which molecules can enter or exit. However, it has remained unclear how these brain vascular cells change between brain regions, or in Alzheimer’s disease, at single-cell resolution. To address this challenge, a team of scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), The Picower Institute for Learning and Memory, and The Broad Institute of MIT and Harvard recently unveiled a systematic molecular atlas of human brain vasculature and its changes in Alzheimer’s disease (AD) across six brain regions, in a paper published June 1 in Nature Neuroscience. Alzheimer's disease is a leading cause of death, affects one in nine Americans over 65, and leads to debilitating and devastating cognitive decline. Impaired blood-brain barrier (BBB) function has long been associated with Alzheimer’s and other neurodegenerative diseases, such as Parkinson's and multiple sclerosis. However, the molecular and cellular underpinnings of BBB dysregulation remain ill-defined, particularly at single-cell resolution across multiple brain regions and many donors.

Navigating vascular complexity

Embarking deep into the complexities of our gray matter, the researchers created a molecular atlas of human brain vasculature across 428 donors, including 220 diagnosed with Alzheimer's and 208 controls. They characterized over 22,514 vascular cells from six different brain regions, measuring the expression of thousands of genes for each cell. The resulting datasets unveiled intriguing changes in gene expression across different brain regions and stark contrasts between individuals afflicted with AD and those without. “Alzheimer's therapy development faces a significant hurdle — brain alterations commence decades before cognitive signs make their debut, at which point it might already be too late to intervene effectively,” comments MIT CSAIL principal investigator and electrical engineering and computer science (EECS) Professor Manolis Kellis. “Our work charts the terrain of vascular changes, one of the earliest markers of Alzheimer's, across multiple brain regions, providing a map to guide biological and therapeutic investigations earlier in disease progression.” Kellis is the study's co-senior author, along with MIT Professor Li-Huei Tsai, director of the Picower Institute and the Picower Professor in the Department of Brain and Cognitive Sciences.

The little cells that could

The threads of our human brain vasculature, and every part of our brain and body, are composed of millions of cells, all sharing the same DNA code, but each expressing a different subset of genes, which define its functional roles and distinct cell type. Using the distinct gene expression signatures of different cerebrovascular cells, the researchers distinguished 11 types of vascular cells. These included endothelial cells that line the interior surface of blood vessels and control which substances pass through the BBB, pericytes that wrap around small vessels and provide structural support and blood flow control, smooth muscle cells that form the middle layer of large vessels and whose contraction and relaxation regulates blood flow and pressure, fibroblasts that surround blood vessels and hold them in place, and they distinguished arteriole, venule, and capillary veins responsible for the different stages of blood oxygen exchange. The abundance of these vascular cell types differed between brain regions, with neocortical regions showing more capillary endothelial cells and fewer fibroblasts than subcortical regions, highlighting the regional heterogeneity of the BBB.

Clues and suspects

Armed with these annotations, the next phase was studying how each of these cell types change in AD, revealing 2,676 genes whose expression levels change significantly. They found that capillary endothelial cells, responsible for transport, waste removal, and immune surveillance, showed the most changes in AD, including genes involved in clearance of amyloid beta, one of the pathological hallmarks of AD, providing insights on the potential mechanistic implications of vascular dysregulation on AD pathology. Other dysregulated processes included immune function, glucose homeostasis, and extracellular matrix organization, which were all shared among multiple vascular cell types, and also cell-type-specific changes, including growth factor receptors in pericytes, and transporter and energy in endothelial cells, and cellular response to amyloid beta in smooth muscle cells. Regulation of insulin sensing and glucose homeostasis in particular suggested important connections between lipid transport and Alzheimer’s regulated by the vasculature and blood-brain-barrier cells, which could hold promise for new therapeutic clues. “Single-cell RNA sequencing provides an extraordinary microscope to peer into the intricate machinery of life, and ‘see’ millions of RNA molecules bustling with activity within each cell,” says Kellis, who is also a member of the Broad Institute. “This level of detail was inconceivable just a few years ago, and the resulting insights can be transformative to comprehend and combat complex psychiatric and neurodegenerative disease."

Maestros of dysregulation

Genes do not act on a whim, and they do not act alone. Cellular processes are governed by a complex cast of regulators, or transcription factors, that dictate which groups of genes should be turned on or off in different conditions, and in different cell types. These regulators are responsible for interpreting our genome, the ‘book of life,’ and turning it into the myriad of distinct cell types in our bodies and in our brains. These regulators might be responsible when something goes wrong, and they could also be critical in fixing things and restoring healthy cellular states. With thousands of genes showing altered expression levels in Alzheimer’s disease, the researchers then sought to find the potential masterminds behind these changes. They asked if common regulatory control proteins target numerous altered genes, which may provide candidate therapeutic targets to restore the expression levels of large numbers of target genes. Indeed, they found several such ‘master controllers,’ involved in regulating endothelial differentiation, inflammatory response, and epigenetic state, providing potential intervention points for drug targets against AD.

Cellular murmurings

Cells do not function in isolation; rather, they rely on communication with each other to coordinate biological processes. This intercellular communication is particularly complex within the cellular diversity of the brain, given the many factors involved in sensing, memory formation, knowledge integration, and consciousness. In particular, vascular cells have intricate interactions with neurons, microglia, and other brain cells, which take on heightened significance during pathological events, such as in Alzheimer's disease, where dysregulation of this cellular communication can contribute to the progression of the disease. The researchers found that interactions from capillary endothelial cells to neurons, microglia, and astrocytes were highly increased in AD, while interactions in the reverse direction, from neurons and astrocytes to capillary endothelial cells, were decreased in AD. This asymmetry could provide important cues for potential interventions targeting the vasculature and specifically capillary endothelial cells, with ultimate broad positive impacts on the brain. “The dynamics of vascular cell interactions in AD provide an entry point for brain interventions and potential new therapies,” says Na Sun, an EECS graduate student and MIT CSAIL affiliate and first author on the study. “As the blood-brain barrier prevents many drugs from influencing the brain, perhaps we could instead manipulate the blood-brain barrier itself, and let it spread beneficiary signals to the rest of the brain. Our work provides a blueprint for cerebrovasculature interventions in Alzheimer's disease, by unraveling how cellular communication can mediate the impact of genetic variants in AD."

Going off script: genetic plot twists

Disease onset in our bodies (and in our brains) is shaped by a combination of genetic predispositions and environmental exposures. On the genetic level, most complex traits are shaped by hundreds of minuscule sequence alterations, known as single-nucleotide polymorphisms (or SNPs, pronounced snips), most of which act through subtle changes in gene expression levels. No matter how subtle their effects might be, these genetic changes can reveal causal contributors to disease, which can greatly increase the chance of therapeutic success for genetically-supported target genes, compared to targets lacking genetic support. To understand how genetic differences associated with Alzheimer’s might act in the vasculature, the researchers then sought to connect genes that showed altered expression in Alzheimer’s with genetic regions associated with increased Alzheimer’s risk through genetic studies of thousands of individuals. They linked the genetic variants (SNPs) to candidate target genes using three lines of evidence: physical proximity in the three-dimensional folded genome, genetic variants that affect gene expression, and correlated activity between distant regulatory regions and target genes that go on and off together between different conditions. This resulted in not just one hit, but 125 genetic regions, where Alzheimer’s-associated genetic variants were linked to genes with disrupted expression patterns in Alzheimer’s disease, suggesting they might mediate these causal genetic effects, and thus may be good candidates for therapeutic targeting. Some of these predicted hits were direct, where the genetic variant acted directly on a nearby gene. Others were indirect when the genetic variant instead affected the expression of a regulator, which then affected the expression of its target genes. And yet others were predicted to be indirect through cell-cell communication networks.

ApoE4 and cognitive decline

MIT researchers develop advanced machine learning models to detect pancreatic cancer. MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma. Prismatic perspectives pancreatic cancer The path forward The first documented case of pancreatic cancer dates from the 18th century. Since then, researchers have embarked on a long and difficult journey to better understand this elusive and deadly disease. To date, early intervention is the most effective cancer treatment. Unfortunately, due to its location deep within the abdomen, the pancreas is particularly difficult to detect early on. Scientists from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), as well as Limor Appelbaum, a staff scientist in the Department of Radiation Oncology at Beth Israel Deaconess Medical Center (BIDMC), wanted to better identify potential high-risk patients. They set out to create two machine-learning models for the early detection of pancreatic ductal adenocarcinoma (PDAC), the most common type of cancer. To gain access to a large and diverse database, the team collaborated with a federated network company and used electronic health record data from multiple institutions across the United States. This vast data set contributed to the models' reliability and generalizability, making them applicable to a wide range of populations, geographical locations, and demographic groups. The two models—the “PRISM” neural network and the logistic regression model (a statistical technique for probability)—outperformed current methods. The team’s comparison showed that while standard screening criteria identify about 10 percent of PDAC cases using a five-times higher relative risk threshold, Prism can detect 35 percent of PDAC cases at this same threshold. Using AI to detect cancer risk is not a new phenomenon; algorithms analyze mammograms, CT scans for lung cancer, and assist in the analysis of Pap smear tests and HPV testing, to name a few applications. “The PRISM models stand out for their development and validation on an extensive database of over 5 million patients, surpassing the scale of most prior research in the field,” says Kai Jia, an MIT PhD student in electrical engineering and computer science (EECS), MIT CSAIL affiliate, and first author on an open-access paper in eBioMedicine outlining the new work. “The model uses routine clinical and lab data to make its predictions, and the diversity of the U.S. population is a significant advancement over other PDAC models, which are usually confined to specific geographic regions, like a few health-care centers in the U.S. Additionally, using a unique regularization technique in the training process enhanced the models' generalizability and interpretability.” “This report outlines a powerful approach to use big data and artificial intelligence algorithms to refine our approach to identifying risk profiles for cancer,” says David Avigan, a Harvard Medical School professor and the cancer center director and chief of hematology and hematologic malignancies at BIDMC, who was not involved in the study. “This approach may lead to novel strategies to identify patients with high risk for malignancy that may benefit from focused screening with the potential for early intervention.”

Prismatic perspectives pancreatic cancer

The journey toward the development of PRISM began over six years ago, fueled by firsthand experiences with the limitations of current diagnostic practices. “Approximately 80-85 percent of pancreatic cancer patients are diagnosed at advanced stages, where cure is no longer an option,” says senior author Appelbaum, who is also a Harvard Medical School instructor as well as radiation oncologist. “This clinical frustration sparked the idea to delve into the wealth of data available in electronic health records (EHRs).” The CSAIL group’s close collaboration with Appelbaum made it possible to understand the combined medical and machine learning aspects of the problem better, eventually leading to a much more accurate and transparent model. “The hypothesis was that these records contained hidden clues — subtle signs and symptoms that could act as early warning signals of pancreatic cancer,” she adds. “This guided our use of federated EHR networks in developing these models, for a scalable approach for deploying risk prediction tools in health care.” Both PrismNN and PrismLR models analyze EHR data, including patient demographics, diagnoses, medications, and lab results, to assess PDAC risk. PrismNN uses artificial neural networks to detect intricate patterns in data features like age, medical history, and lab results, yielding a risk score for PDAC likelihood. PrismLR uses logistic regression for a simpler analysis, generating a probability score of PDAC based on these features. Together, the models offer a thorough evaluation of different approaches in predicting PDAC risk from the same EHR data. One paramount point for gaining the trust of physicians, the team notes, is better understanding how the models work, known in the field as interpretability. The scientists pointed out that while logistic regression models are inherently easier to interpret, recent advancements have made deep neural networks somewhat more transparent. This helped the team to refine the thousands of potentially predictive features derived from EHR of a single patient to approximately 85 critical indicators. These indicators, which include patient age, diabetes diagnosis, and an increased frequency of visits to physicians, are automatically discovered by the model but match physicians' understanding of risk factors associated with pancreatic cancer.

The path forward

Inhalable sensors could enable early lung cancer detection. The diagnostic, which requires only a simple urine test to read the results, could make lung cancer screening more accessible worldwide. Inhalable particles Accurate diagnosis MIT, January 5, 2024 - Using a new MIT technology, diagnosing lung cancer could be as simple as inhaling nanoparticle sensors and then taking a urine test to see if a tumor is present. The new diagnostic uses nanosensors that can be delivered via inhaler or nebulizer. When the sensors come into contact with cancer-linked proteins in the lungs, they generate a signal that accumulates in the urine and can be detected with a simple paper test strip. This method has the potential to replace or supplement the current gold standard for lung cancer diagnosis, low-dose computed tomography (CT). According to the researchers, it could have a particularly large impact in low- and middle-income countries where CT scanners are not widely available. “Around the world, cancer is going to become more and more prevalent in low- and middle-income countries. The epidemiology of lung cancer globally is that it’s driven by pollution and smoking, so we know that those are settings where accessibility to this kind of technology could have a big impact,” says Sangeeta Bhatia, the John and Dorothy Wilson Professor of Health Sciences and Technology and of Electrical Engineering and Computer Science at MIT, and a member of MIT’s Koch Institute for Integrative Cancer Research and the Institute for Medical Engineering and Science. Bhatia is the senior author of the paper, which appears today in Science Advances. Qian Zhong, an MIT research scientist, and Edward Tan, a former MIT postdoc, are the lead authors of the study.

Inhalable particles

To help diagnose lung cancer as early as possible, the U.S. Preventive Services Task Force recommends that heavy smokers over the age of 50 undergo annual CT scans. However, not everyone in this target group receives these scans, and the high false-positive rate of the scans can lead to unnecessary, invasive tests. Bhatia has spent the last decade developing nanosensors for use in diagnosing cancer and other diseases, and in this study, she and her colleagues explored the possibility of using them as a more accessible alternative to CT screening for lung cancer. These sensors consist of polymer nanoparticles coated with a reporter, such as a DNA barcode, that is cleaved from the particle when the sensor encounters enzymes called proteases, which are often overactive in tumors. Those reporters eventually accumulate in the urine and are excreted from the body. Previous versions of the sensors, which targeted other cancer sites such as the liver and ovaries, were designed to be given intravenously. For lung cancer diagnosis, the researchers wanted to create a version that could be inhaled, which could make it easier to deploy in lower resource settings. “When we developed this technology, our goal was to provide a method that can detect cancer with high specificity and sensitivity, and also lower the threshold for accessibility, so that hopefully we can improve the resource disparity and inequity in early detection of lung cancer,” Zhong says To achieve that, the researchers created two formulations of their particles: a solution that can be aerosolized and delivered with a nebulizer, and a dry powder that can be delivered using an inhaler. Once the particles reach the lungs, they are absorbed into the tissue, where they encounter any proteases that may be present. Human cells can express hundreds of different proteases, and some of them are overactive in tumors, where they help cancer cells to escape their original locations by cutting through proteins of the extracellular matrix. These cancerous proteases cleave DNA barcodes from the sensors, allowing the barcodes to circulate in the bloodstream until they are excreted in the urine. In the earlier versions of this technology, the researchers used mass spectrometry to analyze the urine sample and detect DNA barcodes. However, mass spectrometry requires equipment that might not be available in low-resource areas, so for this version, the researchers created a lateral flow assay, which allows the barcodes to be detected using a paper test strip. The researchers designed the strip to detect up to four different DNA barcodes, each of which indicates the presence of a different protease. No pre-treatment or processing of the urine sample is required, and the results can be read about 20 minutes after the sample is obtained. “We were really pushing this assay to be point-of-care available in a low-resource setting, so the idea was to not do any sample processing, not do any amplification, just to be able to put the sample right on the paper and read it out in 20 minutes,” Bhatia says.

Accurate diagnosis

MIT Engineers develop a vibrating, ingestible capsule that might help treat obesity Swallowing the device before a meal could create a sense of fullness, tricking the brain into thinking it’s time to stop eating. When you eat a large meal, your stomach sends signals to your brain that cause you to feel full, allowing you to realize it's time to stop eating. These messages can also be sent by a stomach full of liquid, which is why dieters are often advised to drink a glass of water before eating. MIT engineers have developed a new method for capitalizing on this phenomenon, employing an ingestible capsule that vibrates within the stomach. These vibrations activate the same stretch receptors that detect when the stomach is distended, giving the illusion of fullness. The researchers discovered that giving this pill to animals 20 minutes before eating not only stimulated the release of hormones that signal satiety, but also reduced the animals' food intake by about 40%. Scientists still have a lot to learn about the mechanisms that influence human body weight, but if further research shows that this technology can be used safely in humans, such a pill could offer a minimally invasive way to treat obesity, according to the researchers. “For somebody who wants to lose weight or control their appetite, it could be taken before each meal,” says Shriya Srinivasan PhD ’20, a former MIT graduate student and postdoc who is now an assistant professor of bioengineering at Harvard University. “This could be really interesting in that it would provide an option that could minimize the side effects that we see with the other pharmacological treatments out there.” Srinivasan is the lead author of the new study, which appears today in Science Advances. Giovanni Traverso, an associate professor of mechanical engineering at MIT and a gastroenterologist at Brigham and Women’s Hospital, is the senior author of the paper.

A sense of fullness

When the stomach stretches, specialized cells known as mechanoreceptors detect it and send signals to the brain via the vagus nerve. As a result, the brain increases the production of insulin as well as other hormones like C-peptide, Pyy, and GLP-1. All of these hormones work together to aid digestion, feeling full, and stopping eating. At the same time, ghrelin, a hunger-promoting hormone, decreases. Srinivasan became interested in controlling this process as a graduate student at MIT by artificially stretching the mechanoreceptors that line the stomach with vibration. Previous research had shown that applying vibration to a muscle can create the illusion that the muscle has stretched further than it actually has. “I wondered if we could activate stretch receptors in the stomach by vibrating them and having them perceive that the entire stomach has been expanded, to create an illusory sense of distension that could modulate hormones and eating patterns,” Srinivasan says. As a postdoc at MIT's Koch Institute for Integrative Cancer Research, Srinivasan collaborated closely with Traverso's lab, which has pioneered many novel approaches to drug and electronic device delivery. Srinivasan, Traverso, and their colleagues created a capsule the size of a multivitamin that contains a vibrating element for this study. When the pill, which is powered by a small silver oxide battery, enters the stomach, acidic gastric fluids dissolve the capsule's gelatinous membrane, completing the electronic circuit that activates the vibrating motor. In an animal study, the researchers discovered that when the pill begins to vibrate, it activates mechanoreceptors, which send signals to the brain via vagus nerve stimulation. The researchers monitored hormone levels while the device was vibrating and discovered that they mirrored hormone release patterns seen after a meal, even when the animals had fasted. The researchers then examined how this stimulation affected the animals' appetites. They discovered that when the pill was activated for about 20 minutes before the animals were given food, they consumed 40% less on average than when it was not activated. The animals also gained weight at a slower rate when they were given the vibrating pill. “The behavioral change is profound, and that’s using the endogenous system rather than any exogenous therapeutic. We have the potential to overcome some of the challenges and costs associated with delivery of biologic drugs by modulating the enteric nervous system,” Traverso says. The current version of the pill is designed to vibrate for about 30 minutes after arriving in the stomach, but the researchers plan to explore the possibility of adapting it to remain in the stomach for longer periods of time, where it could be turned on and off wirelessly as needed. In the animal studies, the pills passed through the digestive tract within four or five days. The study also found that the animals did not show any signs of obstruction, perforation, or other negative impacts while the pill was in their digestive tract.

An alternative approach

MIT engineers design a robotic replica of the heart’s right chamber. The realistic model could aid the development of better heart implants and shed light on understudied heart disorders. Robotic Replica - A ballet of beats A heart’s shelf-life December 08, 2023 - MIT engineers have created a robotic replica of the right ventricle of the heart that mimics the beating and blood-pumping action of a living heart. The robo-ventricle is made up of real heart tissue and synthetic, balloon-like artificial muscles that allow scientists to control the ventricle's contractions while also observing how its natural valves and other intricate structures work. The artificial ventricle can be programmed to simulate both healthy and diseased states. The researchers manipulated the model to simulate right ventricular dysfunction conditions such as pulmonary hypertension and myocardial infarction. The model was also used to test cardiac devices. For example, the researchers implanted a mechanical valve to repair a naturally malfunctioning valve, then observed how the ventricle's pumping changed as a result. They claim that the new robotic right ventricle, or RRV, can be used as a realistic platform for studying right ventricle disorders and testing devices and therapies to treat them. “The right ventricle is particularly susceptible to dysfunction in intensive care unit settings, especially in patients on mechanical ventilation,” says Manisha Singh, a postdoc at MIT’s Institute for Medical Engineering and Science (IMES). “The RRV simulator can be used in the future to study the effects of mechanical ventilation on the right ventricle and to develop strategies to prevent right heart failure in these vulnerable patients.” Singh and her colleagues report details of the new design in an open-access paper appearing today in Nature Cardiovascular Research. Her co-authors include Associate Professor Ellen Roche, who is a core member of IMES and the associate head for research in the Department of Mechanical Engineering at MIT; along with Jean Bonnemain, Caglar Ozturk, Clara Park, Diego Quevedo-Moreno, Meagan Rowlett, and Yiling Fan of MIT; Brian Ayers of Massachusetts General Hospital; Christopher Nguyen of Cleveland Clinic; and Mossab Saeed of Boston Children’s Hospital.

Robotic Replica - A ballet of beats

The right ventricle is one of the heart’s four chambers, along with the left ventricle and the left and right atria. Of the four chambers, the left ventricle is the heavy lifter, as its thick, cone-shaped musculature is built for pumping blood through the entire body. The right ventricle, Roche says, is a “ballerina” in comparison, as it handles a lighter though no-less-crucial load. “The right ventricle pumps deoxygenated blood to the lungs, so it doesn’t have to pump as hard,” Roche notes. “It’s a thinner muscle, with more complex architecture and motion.” This anatomical complexity has made it difficult for clinicians to accurately observe and assess right ventricle function in patients with heart disease. “Conventional tools often fail to capture the intricate mechanics and dynamics of the right ventricle, leading to potential misdiagnoses and inadequate treatment strategies,” Singh says To improve understanding of the lesser-known chamber and speed the development of cardiac devices to treat its dysfunction, the team designed a realistic, functional model of the right ventricle that both captures its anatomical intricacies and reproduces its pumping function. The model includes real heart tissue, which the team chose to incorporate because it retains natural structures that are too complex to reproduce synthetically. “There are thin, tiny chordae and valve leaflets with different material properties that are all moving in concert with the ventricle’s muscle. Trying to cast or print these very delicate structures is quite challenging,” Roche explains

A heart’s shelf-life

A new ultrasound wearable device can measure how full your bladder is The wearable device, designed to monitor bladder and kidney health, could be adapted for earlier diagnosis of cancers deep within the body. Ultrasound wearable device - Wearable monitoring Bladder volume MIT researchers have created a patch-like wearable ultrasound monitor that can image organs within the body without the use of an ultrasound operator or the application of gel. The researchers demonstrated that their patch can accurately image the bladder and determine how full it is in a new study. According to the researchers, this could make it easier for patients with bladder or kidney disorders to determine whether these organs are functioning properly. This method could also be used to monitor other organs within the body by relocating the ultrasound array and adjusting the frequency of the signal. Such devices may be able to detect cancers that form deep within the body, such as ovarian cancer, earlier. “This technology is versatile and can be used not only on the bladder but any deep tissue of the body. It’s a novel platform that can do identification and characterization of many of the diseases that we carry in our body,” says Canan Dagdeviren, an associate professor in MIT’s Media Lab and the senior author of the study. Lin Zhang, an MIT research scientist; Colin Marcus, an MIT graduate student in electrical engineering and computer science; and Dabin Lin, a professor at Xi’an Technological University, are the lead authors of a paper describing the work, which appears today in Nature Electronics.

Ultrasound wearable device - Wearable monitoring

Dagdeviren's lab, which specializes in the development of flexible, wearable electronic devices, recently created an ultrasound monitor that can be incorporated into a bra and used to screen for breast cancer. The team used a similar approach in the new study to develop a wearable patch that can adhere to the skin and take ultrasound images of organs located within the body. The researchers decided to focus on the bladder for their first demonstration, partly inspired by Dagdeviren's younger brother, who was diagnosed with kidney cancer a few years ago. He had difficulty completely emptying his bladder after having one of his kidneys surgically removed. Dagdeviren wondered if an ultrasound monitor that shows how full the bladder is could help patients like her brother or others with bladder or kidney problems. “Millions of people are suffering from bladder dysfunction and related diseases, and not surprisingly, bladder volume monitoring is an effective way to assess your kidney health and wellness,” she says Currently, the only way to measure bladder volume is to visit a medical facility and use a traditional, bulky ultrasound probe. Dagdeviren and her colleagues wanted to create a wearable option for patients to use at home. To accomplish this, the researchers created a flexible patch of silicone rubber embedded with five ultrasound arrays made from a new piezoelectric material developed specifically for this device. The arrays are arranged in the shape of a cross, allowing the patch to image the entire bladder, which measures approximately 12 by 8 centimeters when full. The patch's polymer is naturally sticky and adheres gently to the skin, making it simple to attach and detach. When applied to the skin, underwear or leggings can help keep it in place.

Bladder volume