Meg Dalton

As an undergraduate student in the early 1980s, Marie Egan, MD, volunteered at the Children’s Hospital of Philadelphia. She’d often spend time in the hospital playrooms with children with serious illnesses. Many of them had cystic fibrosis (CF), a lethal genetic disorder.

“I saw these children who were very ill,” said Egan, vice chair of research in the Department of Pediatrics and interim chief of pediatric pulmonary, allergy, immunology, and sleep medicine at the Yale School of Medicine (YSM). “At the time, these kids often didn’t make it out of childhood.”

Today, people with CF are living longer but not without complications, especially in the lungs, pancreas, and other organs. There’s still no cure, and treatment usually comprises only symptom management.

But in a new study, Egan and several Yale colleagues describe the benefits of a novel approach for treating CF, and perhaps someday even curing the disease. Using in utero gene editing, the researchers developed a method to deliver corrective genetic material to the fetus of mice with CF via tiny particles called nanoparticles.

The results suggest that CF and other genetic diseases might eventually be curable in utero.

“If we could intervene while the organs are developing, then people would truly be cured of the disease,” said Egan, corresponding author of the study published in the journal 

Proceedings of the National Academy of Sciences

The study was made possible by a multidisciplinary team across the YSM community. That includes members of the labs of Egan, who is also director of Yale’s Cystic Fibrosis Center and professor in the department of pediatrics and cellular and molecular physiology; Mark Saltzman, the Goizueta Foundation Professor of Biomedical Engineering and professor of cellular & molecular physiology and dermatology at YSM and of chemical engineering at Yale School of Engineering and Applied Science; Peter Glazer, the Robert E. Hunter Professor of Therapeutic Radiology and at YSM the chair of the department of therapeutic radiology; and David Stitelman, associate professor of pediatric surgery and obstetrics, gynecology & reproductive sciences at YSM. The research team also included first author Adele Ricciardi, of the University of Pennsylvania, a former member of Glazer’s lab.

“The manuscript represents the work of Yale undergraduates, medical students, graduate students, and residents, all of whom are devoted to careers in academic medicine, and we are very proud to be a part of their training,” said Stitelman, who is also surgical director of the Yale Fetal Care Center.

"If we could intervene while the organs are developing, then people would truly be cured of the disease."

Cystic fibrosis arises from mutations in the CF transmembrane conductance regulator, or CFTR. Because the disease is caused by a single mutation, researchers saw it as a great candidate for gene editing, specifically in utero gene editing. The disease can cause serious damage to organs like the lungs and pancreas even before birth. Treating CF in the womb could mitigate this damage and improve the quality of life for those living with the disease.

“If you look at data today, our CF patients are now living into adulthood or even old age,” Egan said. “But the amount of therapy that people take, and the cost, is enormous. If you could actually develop a therapy where you could deliver it once and you wouldn’t have to worry about all this, wouldn’t that be great?”

"Our nanoparticles are made of materials that have long proven to be safe in humans. Control of their properties is critically important for the successful delivery of the gene editing agents into cells."

In the study, researchers used synthetic molecules similar to DNA — called peptide nucleic acids, or PNAs — to correct CFTR. PNA can be tweaked to bind to a particular gene containing a mutation in a strand of DNA, which can cause a lesion that the cell then removes itself. The gene is then corrected when a strand of DNA without the mutation takes its place.

“PNAs can induce gene editing and are well suited to in vivo applications because they are easily formulated into nanoparticles,” Glazer said.

The researchers delivered the PNA to the fetuses of mice using tiny synthetic particles called nanoparticles, which are the same size as viruses to correct the CFTR mutation 

“Our nanoparticles are made of materials that have long proven to be safe in humans,” Saltzman said. “Control of their properties is critically important for the successful delivery of the gene editing agents into cells.”

After receiving this treatment, and following their subsequent birth, the mice showed sustained improvement in their CF symptoms — even, in some cases, into adulthood. The PNA-loaded nanoparticles, researchers say, had corrected some of the dysfunction caused by the disease in the lungs but also other organs.

This research was done in partnership with the National Institutes of Health (NIH) through a grant that includes reimbursement for facilities and administrative expenses (also known as indirect cost reimbursements) that are necessary to ensure the safe conduct of research and compliance with federal regulations.

In February, the NIH announced it would dramatically cut such reimbursements to universities, including Yale. The courts have blocked the cuts, but the threat remains.

At stake is research that saves lives, strengthens the economy, and bolsters national interests. Yale projects in danger include research that saves infants born with heart defects, extends the lives of cancer patients, addresses mental health challenges, and prevents and slows the effects of Alzheimer’s disease.

Ultimately, researchers say, the study suggests the potential to cure cystic fibrosis using a single in utero gene editing treatment. While these findings are significant, Egan said, more research is needed to understand how the method might translate to a human population. However, she said, if the method proves effective in humans, it could lead to a groundbreaking shift in the treatment of genetic diseases, from solely managing symptoms to fixing the underlying causes.

“There’s so much for us to learn before that happens,” Egan said. “But I do think this is an incredible time, and we are seeing gene editing therapies in the clinic now. There are definitely some diseases where this approach has made a huge difference in the outcomes of children.”

The study was supported by grants from the National Institutes of Health, the National Institute of General Medical Sciences, and the Cystic Fibrosis Foundation.

Articles

Marie Egan, MD, and her team at Yale have developed a novel in utero gene editing approach to potentially cure cystic fibrosis (CF) by delivering corrective genetic material to fetuses using nanoparticles. This method targets the CFTR mutation, aiming to prevent organ damage before birth. The study, involving a multidisciplinary team, demonstrated sustained improvement in CF symptoms in mice. While promising, further research is needed to assess its applicability to humans. The study highlights the potential shift from symptom management to addressing the root causes of genetic diseases.

In a new study, Yale researchers develop a method that has the potential to cure cystic fibrosis prior to birth. 

The cure for cystic fibrosis might start in the womb

Emergency departments (EDs) in the U.S. have seen a surge in patient visits for mental health conditions over the past decade. And frequently these visits include episodes of patient agitation, or aggressive, sometimes violent, behavior.

Once agitation occurs, clinicians work to quickly diagnose potential causes and intervene before it escalates. In the ED, that may mean having to use physical restraints or intramuscular medications to care for an agitated patient, which are known to increase risk of injury, including blunt chest trauma, respiratory depression, and even sudden death.

It can be difficult predicting if and when agitation might happen. But in a new study, Yale researchers describe a potential solution for doing just that. Using artificial intelligence (AI), they created a prediction model for identifying patients at risk of becoming agitated, an advance they say could make the ED safer for patients and providers alike.

“In general, patients that experience psychiatric emergencies or behavioral emergencies first land in our doors,” said Ambrose Wong, MD, MSEd, MHS, associate professor of emergency medicine at Yale School of Medicine (YSM) and lead author of the study published in the journal 

Prediction models are becoming more common within modern medicine. They’re being used to assist the decision-making of clinicians within cardiology, diabetes care, chronic kidney disease, and more. But the development of prediction models for mental health has been limited.

For this study, researchers sought to address this gap by developing, training, and validating a new modeling tool using common information collected in the ED to identify which patients might become agitated. This information was based on electronic health record data from more than 3 million ED visits by patients 18 or older at nine hospitals in the northeastern U.S. between 2015 and 2022. The researchers also drew from past research, input from hospital staff, and studies on restraint and sedation use.

The research team looked for patterns in this wide range of information (nearly 700 possible factors in total). But before building their model, they carefully cleaned and organized the data — removing poor-quality entries and merging similar data points — to ensure that the information was consistent and ready for analysis. Ultimately, they narrowed their focus to the most relevant factors, testing combinations of the top 20, 50, 100, and 200 predictors.

In developing the model, the team split the data into 2 parts: 1 to train the model and one to test how well it worked. They tested 3 machine learning methods to find the best fit and to ensure the model wouldn’t just memorize patterns from the training data but could reliably make predictions based on new cases. They also checked for fairness across age, sex, and race to avoid unintended bias.

The final model used 50 key features, including a patient’s age, insurance status, past medical history, medications, reason for visiting the ED, and whether they had a primary care provider. Although race and ethnicity were included in the dataset to check for fairness, that information was intentionally excluded from the model to prevent bias. Some of the strongest warning signs for future agitation, they found, included frequent past visits to the ED, abnormal vital signs, relevant medical history, and any previous use of restraints or sedation.

“What we don’t want is for the model to be self-fulfilling but instead predict agitation early in a visit before symptoms even develop,” Wong said. “Our ultimate goal is to better allocate critical and limited resources to those that are in most need or that have the greatest benefit.”

In addition to showing that machine learning can be a powerful tool for predicting patient agitation in the ED, the study could help improve both patient and provider safety by focusing on prevention rather than reactive treatment like sedation or restraints, the researchers say.

“On the patient side of things, physical restraint or chemical restraint can be a pretty dehumanizing experience,” said Andrew Taylor, associate professor adjunct of biomedical informatics and data science at YSM and senior author of the study. “On the staffing side, when people become violent or aggressive, there can be both emotional trauma and physical trauma to staff, too. Anything to decrease that [restraint] is both patient-centered and provider-centered.”

Ultimately, researchers say, the study was a test case for the team’s prediction model. Next steps include scaling the model, getting buy-in from clinicians, and implementing it in hospital systems. “We know the model works, but we really have to get it implemented,” Taylor said. “We have to build up that clinical support around it and look at the pragmatics of when to deploy it and how to deploy it so that it best fits with our staff’s workflow.”

Taylor is also professor of emergency medicine at the University of Virginia. Wong is also the director of simulation research at the Yale Center for Healthcare Simulation.

Other Yale authors include Atharva Sapre, Kaicheng Wang, Bidisha Nath, Dhruvil Shah, Anusha Kumar, Isaac Faustino, Riddhi Desai, Yue Hu, Leah Robinson, Can Meng, Guangyu Tong, Edward Melnick, and James Dziura.

This study was supported by grants from the National Institute of Mental Health, the National Institute of Nursing Research, the National Institute on Minority Health and Health Disparities, and the Patient-Centered Outcomes Research Institute.

Emergency departments in the U.S. have experienced a rise in mental health-related visits, often involving patient agitation. Yale researchers developed an AI-based prediction model to identify patients at risk of agitation, aiming to enhance safety for both patients and providers. The model, trained on data from over 3 million ED visits, uses 50 key features to predict agitation, excluding race and ethnicity to prevent bias. The study highlights the potential of machine learning in proactive management, emphasizing prevention over reactive measures like sedation or restraints.

A new Yale study investigates how artificial intelligence might help identify patients at risk of developing agitation in the emergency department. 

