Amid this unmet need, researchers at the School of Medicine developed a method to identify the proteins ticks release while feeding on their hosts. Over time, humans and animals can develop resistance to tick bites by producing antibodies that recognize these proteins, blocking disease transmission. 

To pinpoint which proteins trigger this immune response, the team created an antigen library called IscREAM, which contains over 3,000 deer tick antigens and allows for rapid screening of those most likely to aid in developing future diagnostic tests and vaccines.  

“IscREAM stands for the Ixodes scapularis rapid extracellular antigen monitoring, and is a pretty apt name because who wouldn’t scream when they find a tick on them!” wrote Thomas Hart, former postdoctoral associate at the School of Medicine who spearheaded the creation of IscREAM. “We took almost every possible antigen — over 3,000 different proteins — from I. scapularis and engineered yeast cells to display the protein on their surface.”

Creating IscREAM

Hart explained that IscREAM provided a way to effectively categorize the thousands of proteins ticks secrete to find those that lead to resistance. To identify which tick proteins the immune system responded to, the team combined engineered yeast cells expressing tick proteins with an antibody isolated from blood from tick-exposed Lyme disease patients, guinea pigs and mice. 

The team then monitored the yeast cells to see which ones the tick-resistant antibodies bound to. This data, Hart explained, helped researchers filter through the proteins to identify which triggered an immune system response. 

This information allowed the team to identify new ways to induce tick resistance, or reduced likelihood of tick attachment, feeding and disease transmission, in individuals who have not yet been bitten by a tick. 

The Research Journey

According to Yingjun Cui, a researcher at the School of Medicine who authored the study, the team’s work first began with the development of an mRNA anti-tick vaccine. This process, Cui explained, involved collaborating with Drew Weissman’s lab at the University of Pennsylvania, who was awarded a Nobel Prize in Physiology or Medicine for their role in developing COVID-19 mRNA vaccines.

The vaccine works by injecting mRNA — the instructions for a protein that causes an immune-response — into the cells, building tick resistance. Cui emphasized that the initial vaccine was limited in tick-borne disease prevention, so the team continued work after publishing their first study on the topic in 2021 to improve the vaccine. 

The most recent study incorporating IscREAM took the past two years to complete. As Hart explained, the team first looked through all of the proteins in deer ticks to find the candidate proteins secreted or exposed outside the tick cells. Then, the team began their screens of each tick antigen in their expansive library. 

“Creating IscREAM was honestly a lot of fun. It was a lot of work, of course, but it was interesting work, and it’s always exciting to build a technology like this, and to see it come to fruition,” wrote Hart. 

The team worked with the Weissman lab to craft three mRNA cocktails encoding 25 antigens in tick cement — the “glue” they use to bind their mouthpieces to hosts — that would cause an immune system response in the host. From 2023 to 2024, the team immunized guinea pigs with each cocktail and found that one in particular resulted in lower tick weight and early detachment. 

“We are narrowing down the antigen number now, and our initial experiment has a promising result that the guinea pigs immunized with a small cocktail containing 5 mRNAs developed comparative tick rejection to the big cocktail,” Cui wrote. 

Future applications 

Erol Fikrig, professor of medicine at the School of Medicine, said that the team was inspired to take a new global approach to examining all the interactions a tick has with the antibody response in the human body. 

For Fikrig, the team’s research aims to answer two key questions. 

“The first question is, when you’re bitten by a tick, what is it you’re recognizing in terms of making an antibody to that tick bite?” said Fikrig. “The second thing is, are there targets in there that could be linked to tick rejection?”

In Fikrig’s view, the answer for the first question could help pave the way for new diagnostic tests for early tick bites. In the future, patients concerned about having Lyme disease could perform a test to check for Lyme disease and a test if they have had a recent tick bite to confirm their suspicions. 

In terms of the second, Fikrig believes that the team’s work identifying proteins associated with tick rejection shows promise for preventing tick-borne disease transmission.

“In this paper, we show that when you immunize with a cocktail of 25 proteins, we can develop an immune response,” noted Fikrig. “It’s detectable against all of those proteins, which means that it’s a potential target for an anti tick vaccine.”

Fikrig categorized the study as a first step toward creating a diagnostic test and vaccine, and hopes that further research could help bring both to the public within the next 10 years. 

There are over 40,000 tick-borne disease cases in the United States each year. 

The post Yale research may pave way for diagnostic tests and vaccines for tick-borne diseases appeared first on Yale Daily News.

Researchers have been studying the medical applications of machine learning for only around a decade, and the team focused their efforts on pushing the boundaries of this innovative tool with a unique experimental design. The research stands out as a crucial milestone, as their reported prediction strengths are relatively high for clinical measures. 

“While a lot of studies usually are using cross-sectional designs and comparing patients with PTSD compared to healthy controls or compared to trauma-exposed healthy controls, this study focused on recent trauma survivors during the first 14 months after trauma exposure,” said Dr. Ziv Ben-Zion, a Fulbright postdoctoral fellow at Yale and first author of the study.

According to Ben-Zion, the data used in the study was “quite unique” and collected as part of his doctoral research from 2015 to 2020, at Tel Aviv Sourasky Medical Center in Israel. 

Then, Ben-Zion recruited individuals who arrived at the emergency department after experiencing potentially traumatic events, the most common being car accidents. 

The patients who experienced high levels of PTSD one month after admission — who were most likely to develop chronic PTSD — were assessed one month, six months and 14 months after admission. To monitor each patient’s progress, clinical assessments and fMRI scans, recording brain structure and function, were performed. 

Ben-Zion shared good news: most of the patients recovered sometime during the 14 months of study. 

By the end of data collection, Ben-Zion had obtained a multi-domain data set detailing PTSD symptom severity — CAPS-5 total scores, on a scale of zero to 80 — as well as cognitive functioning and neural data for each of the 171 participants. 

This data set was used to develop the predictive machine learning model. The team used connectome-based predictive modeling, a machine learning technique originally developed in the Constable Lab at Yale that has gained popularity over the past decade.

The model works by applying 10-fold cross-validated regression models to whole-brain functional connectivity data derived from the fMRI BOLD signal to predict behavioral measures of interest, such as PTSD symptoms.

While the study showed no association between whole brain connectivity and symptoms at the six-month time point, there appeared to be high predictive ability at one month and 14 months. 

According to Ben-Zion, these findings align with current clinical knowledge about PTSD, which defines the six-month time point as a fragile and dynamic point in the recovery process.

After breaking down the PTSD symptoms into clusters based on the DSM-5, the team also noticed that different clusters were driving predictions at different time points, which suggests the connection of various regions in the brain to PTSD progression. 

For Dr. Scheinost, this finding will benefit the growing understanding of PTSD.

“I think it helps shift some of the neurobiological thinking about PTSD—moving away from characterizing a few key regions (like the amygdala) to more widespread, whole-brain alterations,” Scheinost wrote to the News. “That’s not to say that the amygdala or other single areas are not crucial to PTSD, just that we are likely only capturing a piece of the picture.”

A key aspect of the team’s work from the beginning was collaboration. Ben-Zion and his advisor, Dr. Ilan Harpaz-Rotem, first connected with AJ Simon, a doctoral student in Interdepartmental Neuroscience, and his rotation advisor at the time, Dr. Dustin Scheinost, around two years ago to expand their study on the initial data set. 

For Simon, who was a first-year rotating graduate student in Dr. Scheinost’s lab at the time, the project was an exciting new chance to explore machine learning models.

“I jumped on board because it was my opportunity to learn connectome-based predictive modeling and to apply it in a way where there was potential for translational impact,” said Simon.

The team worked on analyzing the data for six months and writing the paper for another six months before moving on to the review phase of their research, which took over a year before publication.

Looking to the future of the study, Dr. Ben-Zion hopes that other researchers will try to replicate the study with new data sets.

He noted that more researchers are currently publishing their own independent — and sometimes, inconsistent — findings, rather than focusing on replication to produce more robust results that build on prior studies.

While Harpaz-Rotem notes that it’s still a long way before MRI scans can be used as predictive clinical tools, the study shows promising results for the field’s future.

“I think the study demonstrates the capacity of [connectome-based predictive modeling] to be useful to identify the brain regions that are involved in the potential development of PTSD and think how we can intervene to prevent the development of PTSD based on this knowledge gained,” Harpaz-Rotem wrote.

To learn more about connectome-based predictive modeling, see here.

The post Yale researchers use machine learning to predict PTSD symptoms appeared first on Yale Daily News.

Associate Dean for Student Affairs John Francis celebrated the class as a “clinically astute and prepared group of students who have matched into exemplary training programs across the country.”

Match Day — a national milestone for graduating medical students — marks the culmination of months of applications, interviews and decision-making.

On Friday, all 87 doctoral and dual/joint degree students opened envelopes revealing where they will be training for the next stage of their medical careers.

“We are so very proud of them; they are among the best of the best,” said Francis. 

Amber Acquaye MED ’25 matched into psychiatry at Brigham and Women’s Hospital. She began her application process in late September, submitting through the Electronic Residency Application Service, a centralized system used by most programs. She likened the system to the Common Application used for college, noting it required uploading personal statements, meaningful experiences and recommendation letters.

She emphasized how costly the application process could be. While psychiatry applicants typically apply to about 30 programs, she said some peers applied to as many as 150 programs, each with a fee.

After submitting applications, students began the long wait for interview invitations. Acquaye said she felt the real stress of the process during this period, when she constantly checked her phone hoping for an interview email. The uncertainty and competitiveness of the invitation stage, she said, made it the most emotionally draining part of the process. 

Students then submitted a ranked list of programs, which the match algorithm uses to determine placements. Programs also submitted rankings of applicants. Acquaye found ranking programs more difficult than waiting for the final match result. She described the ranking decision as a process of intense self-reflection, weighing priorities like location, institutional culture and professional development.

“I felt that making my rank list was more stressful than waiting to see where I was going to end up, because it’s just so much, there’s just so many details,” Acquaye said. “At the end of the day, programs start to [look] kind of similar, and then you start to think, ‘Oh, my goodness, this is my life? What if I like this program more, but I like this city better?’” 

Acquaye was drawn to Brigham and Women’s for its strong academic psychiatry offerings and emphasis on nurturing scholarly interests. She saw a clear alignment between her goals and the mission of the program, which made her confident in her decision, even though she had initially hoped to be in New York.

For Brian Fleischer MED ’25, the preparation for Match Day began well before fourth year. In the summer before his final year, he completed research and away rotations that helped him shape his specialty decision. By the time interviews rolled around, he had already finished the required Step One and Step Two licensing exams and finalized his letters of recommendation.

Fleischer applied to primary care programs and completed many of his interviews remotely while conducting research in Ghana. He noted that virtual interviews allowed him to avoid travel costs and maintain his research schedule, a flexibility that had not always been possible in previous years.

He explained that the match algorithm uses the rank lists submitted by both students and programs to optimize mutual preferences. If a student ranks a program highly and is also ranked highly by that program, the likelihood of a match is strong. This process, he said, helps ensure that both sides end up with a good fit.

Like many applicants, Fleischer experienced a mix of anticipation and anxiety leading up to Match Day. He had ranked Yale’s Primary Care program highly, in part due to his longstanding connections at the institution and the strength of its global health resources.

Originally from Ghana, Fleischer has pursued research focused on primary care access in both the United States and abroad. During his time at Yale, he completed global health rotations and worked on public health interventions, building strong relationships with mentors in the Primary Care program.

He said that Yale’s institutional support and global reach ultimately made it a natural choice. The alignment between his professional goals and the program’s offerings confirmed for him that it was the right place to continue his training.

Match Day, he noted, brought a mix of emotions. 

“I was surrounded by lots of close friends. They all knew where I really wanted so much,” Fleischer said. “When I saw the match, I knew it was a match made in heaven.”

YSM students matched into 21 specialties. Internal Medicine, including primary care and combined programs, was the most popular, drawing 23 students. Nine students matched into Anesthesiology, seven into Psychiatry and six into Emergency Medicine. A total of 24 students matched into surgical specialties, including Ophthalmology, Orthopaedic Surgery, Plastic Surgery and others.

According to Abigail Roth, associate director of communications at the School of Medicine, 21 students matched at Yale New Haven Health programs and 19 matched at Harvard-affiliated institutions. Other matches included Johns Hopkins Hospital, Stanford Health Care, New York Presbyterian, Duke University Medical Center and the University of Washington. 

Dean Nancy Brown called Match Day a reminder of the school’s mission. 

“I remain proud of our students and grateful to our faculty and community for helping them reach their goals,” Brown said.

Acquaye reflected on how far she and her peers had come. 

“We started as med students, you know, being on Harkness Lawn, [seeing upperclassmen] opening those envelopes and seeing their peers and where they’re going to all be going and imagining, ‘Oh, that’s gonna be me,’” she said. “And just like the full circle nature of it, all of us finally being in that position … seeing the completion of our journey.”

The 2025 National Match Day was March 21.

The post Yale School of Medicine achieves 100 percent match rate appeared first on Yale Daily News.

The cancer vaccine provides a personalized treatment option for high-risk kidney cancer patients. The treatment centers around immunotherapy, a form of cancer therapy that focuses on getting the immune system to recognize and suppress the cancer. The team’s vaccine represents a recent shift to more targeted cancer therapies than general treatments like surgery, radiation and chemotherapy that tend to prolong life in advanced cancer patients but do not usually elicit long-term responses. 

“People have been trying to do [cancer treatment] for over 100 years at this point, but what was really the success of the last 15 to 20 years was that [scientists] realized that the immune system has certain brakes on it that can prevent it from doing its job,” David A. Braun, a professor and researcher, told the News. 

In a typical immune system response to a pathogen like the flu virus, the immune system recognizes the foreign invaders, attacks them and ramps back down once they have been cleared. However, the immune system does not typically recognize and attack cancer in the same way. Dr. Braun explained that cancer keeps the brakes on the immune system pressed, effectively hiding itself and preventing an immune response. 

Targeted immunotherapy

The current era of immunotherapies centers around the principle of taking off the brakes from the immune system to create an immune response to treat cancer. But, as Braun emphasized, simply activating the immune system is not enough to consistently target the cancer. 

“When you take your foot off the brake of the immune system, it’s actually not telling it where to go. It’s not telling it to attack the cancer. It’s turning on the immune system and hoping that that’s going to be the outcome,” Braun said. “And that’s been effective for some patients, but as we know, for so many patients, the current immunotherapies still are ineffective at either treating cancer, or preventing it from coming back.” 

While most vaccines today are preventative, the team’s cancer vaccine is therapeutic. According to Dr. Braun, rather than just taking off the brakes, the vaccine provides a steering wheel to guide the immune system to locate and attack the cancer. 

The vaccine is able to steer the immune system by targeting specific parts of the cancer unique to each patient. To find these discrepancies, the researchers performed DNA sequencing, RNA sequencing and a host of other tests on patients’ tumors. 

The research process

The trial included patients who had recently had their kidney tumors surgically removed, but who were at high risk for recurrence. The team created a personalized vaccine for each patient, with each vaccine taking three months to make on average. Braun mentioned that kidney tumor surgery patients typically need two to three months for recovery, so the timeframe for making the vaccine gave the team the perfect window to work on the vaccine. 

“I think if the question sometimes comes up, do you think that [timeframe] could be a lot shorter than three months? I think the answer is absolutely once this is done at a bigger scale and is automated in a way, I think that certainly that timeline is going to come down,” Braun said. 

The initial research began when Braun was working at the Dana-Farber Cancer Institute, and he noted that the cancer vaccine had interested him both clinically and scientifically. 

While drugs introduced in the last few years have helped treat high risk kidney cancer, there was no effective drug when Braun and his team began their research. 

“You would have a patient who just had surgery, with a third of a chance, or maybe even a 50-50 chance the cancer could come back. And there wasn’t anything you would do except just watch it,” Braun said. 

Braun added that despite new drugs for high risk cancer patients, clinic need is still very high for better treatment options. 

On the scientific side, Braun highlighted the importance of immunotherapy as a paradigm shift in cancer treatment. Historically, patients with advanced kidney cancer like metastatic or stage four kidney cancer live around a year from diagnosis to death. But, new immunotherapies show potential of extending patient lifespan by four to even nine years after diagnosis. 

While immunotherapy presents a promising way to treat cancer and have a significant long-term impact on patients, Braun acknowledged that there are limitations regarding the population that experiences its benefits. 

“Immunotherapy is this wonderful proof of concept that you have this tremendous control of cancer, but there’s a long way to go. It’s helping a certain number of people. That’s really important, but there’s many more people out there who are not right now getting benefit from it,” Braun said. 

Research planning and funding

The project required a great deal of coordination and team effort to ensure all the proper procedures were taken. This involved getting team members together and contracting a location for the genomic sequencing. The scientists designing the vaccine had to make it clinically appropriate, which required a great degree of specificity and planning by a great number of scientists, pharmacists and doctors to ensure its safety and efficacy. These were all important factors that the Braun team considered when designing the science. 

Braun worked with the patients in the clinical trial individually, analyzing their tumors to be able to fully understand their immune responses. 

The funding for this project came from the Gateway for Cancer Research, known for furthering innovative cancer research projects. The Department of Defense was also an important source of funding, as their research program helped fund a portion of the science involved in the study. 

The project had five major points of focus: feasibility, safety, evaluation of immune response, quality of the immune response and capability of the immune responses. The team had to evaluate if the project was feasible because it was something that had never been done before in kidney cancer patients. 

The researchers had to critically evaluate the trial to see if it is safe for humans. Then they evaluated if the vaccine would relieve an immune response, and thought about how they could utilize the immunotherapeutics in a way that would steer the immune system to attack cancer specific targets. The immune responses then had to be evaluated to see if they are lasting and capable of attacking the tumor directly. 

The trial also has its limitations, as this study in particular was only nine patients. To truly evaluate the efficacy of vaccines like this one, studies typically analyze data from hundreds or thousands of patients. However, in this trial, Braun and his team were looking more for an immune response and activity signal, rather than a definitive answer about the vaccine’s pharmaceutical efficacy. 

Effects of the vaccine

In the end, the team was able to manufacture a personalized vaccine for all the patients in the study, and no one experienced major side effects. Braun reported most patients presented with a little redness around the site of the injection and slight flu-like symptoms, including muscle aches and low grade temperatures. This was viewed as a positive sign by researchers because it indicated immune system activity. 

“The work was highly innovative. This type of personalized neoantigen vaccine approach has not previously been used in renal cell carcinoma,” Harriet Kluger, professor of medicine and dermatology, told the News. “Moreover, the scientific rigor in applying state of the art technologies to verify immunologic response to the vaccine is commendable.” 

The vaccine successfully generated an immune response, and researchers collected blood samples before, during and after the vaccine for some patients, up to months and years after they had finished the vaccine. They found that even years after they had the vaccine,patients still showed evidence of the vaccine-induced changes in their immune system, resulting in lasting immune memory against cancer targets. Researchers were able to collect the immune cells after vaccination and test them against the tumor to demonstrate the immune cells had become capable of recognizing the tumors. 

The researchers expected about one-third to one-half of the nine patients to have their kidney cancer come back, however none of those nine patients had their kidney cancer return. The first patient was treated in 2019, the next patient months after that and the rest staggered after that. 

Future research and expansion

Now, a larger, phase two trial is open in collaboration with larger companies like Merck and Moderna, which is now focused on studying the effectiveness of a personalized cancer vaccine in hundreds of patients. Yale remains a big part of that effort. 

“Dr. Braun and his team have produced a very important study. It shows that personalized vaccines are feasible, safe and potentially effective for Renal cell carcinoma. Further, Dr. Braun and his team showed that targeting driver mutations—the mutations that are most important in making cancer into cancer—is an effective vaccination strategy,” Jeffery Ishizuka, assistant professor of medical oncology, told the News. 

The phase two trial will continue to evaluate if the approach of a personalized cancer vaccine really works at a larger scale to decrease the chance of the kidney cancer coming back, but the longer term question focuses on how these cancer vaccines, particularly personalized vaccines, play a role in other types of cancers and other clinical settings. 

The Braun lab will continue to focus on these next generation of vaccine targets and explore their therapeutic potential in both clinical and scientific settings. 

“With continued development success, this approach and others like it could lead to a new standard of care in Renal cell carcinoma and save the lives of thousands of patients diagnosed with cancer,” Ishizuka said.

The Gateway for Cancer Research is located at 20 North Martingale Rd. in Schaumburg, Ill.

The post Yale scientists design personalized cancer vaccine appeared first on Yale Daily News.

Founded in 2020, the CarDS Lab has been at the forefront of creating AI-based applications to improve medical diagnoses of heart muscle diseases for the past five years.

“We want to design AI tools that we can use with tests that are easy to perform and that can be easily available in the community,” Dr. Evangelos Oikonomou told the News. “We don’t necessarily want to build AI tools for technologies that might be very hard to come across or find and are only restricted to very specific, high resource settings.”

The team of researchers began working on the grant proposal almost two years ago and spent around a year and a half compiling data from different hospitals to train the AI model. 

With an interest in developing AI models that rely on data provided by accessible diagnostic tools, the team focused on point-of-care ultrasound — a portable ultrasound test that can be performed by plugging an ultrasound rod into a smartphone to obtain a detailed image of the heart. The test is widely available to medical providers but is mainly used for crude assessments of how the heart works and if there are any obvious abnormalities.

According to Oikonomou, even with the use of point-of-care ultrasound, many abnormal heart conditions go undetected. Recognizing patterns that indicate cardiomyopathies requires extensive training and expertise, and it is difficult for expert operators to carefully examine every ultrasound image. 

“We see that the model is actually able to pick up on patterns that are visible to the human eye, but probably not detectable by an untrained operator,” Oikonomou said. “But it also goes a bit beyond that. It even seems to detect those conditions way before clinicians actually suspect what’s going on.”

The study focused on two main forms of cardiomyopathies — hypertrophic cardiomyopathy and amyloid cardiomyopathy, both diseases that make it difficult for the heart to pump blood. 

Oikonomou noted that medical professionals have realized that the condition is much more common than previously thought and that the lack of expert operators and effective diagnosis tools made the diseases difficult to detect in the past. 

To train their AI model, the team fed real-world data collected from more than 30,000 patients across the Yale Health System over a decade.

After the team created a functional training model, they started testing videos from patients who were not seen by the AI model beforehand and who were screened with point-of-care ultrasound devices across the emergency rooms of the Yale Health System, the Yale New Haven Health System and the Mount Sinai Health System.

While hypertrophic cardiomyopathy is inborn and amyloid cardiomyopathy is acquired during one’s lifetime, both diseases are progressive, meaning that the model can actually pick up on earlier stages of the disease that cannot be detected by the human or untrained eye.

“We found that the algorithm could pick up the disease at an average of two years before the disease was eventually diagnosed in real-world practice,” said Oikonomou. “We also found that there were a lot of patients that were never actually tested for any of those conditions but were flagged as high risk by our models, and these patients went on to have worse outcomes.”

Oikonomou noted that the model stratifies risk in patients, with those flagged positively being much more likely to have the disease progress faster than those flagged negatively. 

AI models accurately detected two types of cardiomyopathy with 0.95 and 0.98 AUROC, a performance metric used to evaluate binary classification models where achieving a 1 is considered a “perfect model,” and were received well in testing at Mount Sinai Hospital System located in New York.

The CarDS lab published this AI application for anyone to access for research purposes. 

“We need to make use of AI. We need to leverage AI to make those technologies more accessible, more scalable,” Oikonomou told the News.

AI has become increasingly integrated into medical practices. However, the successful widespread implementation of such technologies depends on their affordability and availability.

The CarDS lab used point-of-care ultrasound because it is easily applicable for community-based screenings of abnormal heart disorders, costs less than $2,000 and can easily be plugged into a smartphone.   

Oikonomou emphasized the importance of the team’s work with external collaborators at Mount Sinai, who independently ran the AI model without any transfer of their data. 

“This multi-site validation confirms that the model will work reliably when exposed to new settings and patient distributions beyond what was seen during model development,” Gregory Holste, a doctoral student in the CarDS Lab, said.

Oikonomou shared that they are in the process of designing a clinical trial where some providers are going to have access to these AI tools and others will not. Over the years, the results from this trial should give insight into the clinical value of using AI technologies to detect cases of abnormal heart muscle disorders.

More information regarding the CarDS Laboratory’s research can be found here.

The post Yale researchers developed AI tool to predict heart muscle disease appeared first on Yale Daily News.

In the pre-clerkship phase, students take lecture courses and participate in interactive clinical workshops and extracurricular activities, blending numerous ways of learning and interacting with medicine. The News talked to first years at the School of Medicine about their lives inside and outside the classroom. 

“What I like about Yale is that we have so much flexibility built within our curriculum and in our scheduling that gives people a chance to kind of explore the way that they learn, and also explore things outside the classroom,” Dariana Gil-Hernández MED ’28 said. 

“Having this flexible system gives everyone the chance to get involved in different things, like research, volunteering, shadowing, while also learning,” she added.

Master courses

The School of Medicine operates on blocks rather than semesters. 

In each block, all students take a specific master course together — a lecture that covers a variety of science topics. Each master course lasts between four and 12 weeks. 

Since the fall, students have already taken “Intro to Profession,” “Scientific Foundations,” “Genes and Development” and “Attacks and Defenses” and are currently in the “Homeostasis” master course.

One aspect of the School of Medicine’s pre-clerkship curriculum that has helped first-year students feel less stressed throughout the adjustment to the fast-paced curriculum has been the lack of grades.

For many students, such as Gil-Hernández, the absence of grades or class rank at the School of Medicine was a key aspect of their decision to attend and has been a highlight of their learning experience.

“I feel like the most important thing for me was the lack of feeling competition and feeling stressed out about having a grade,” Gil-Hernández said. “I think that taking all of that out and just making sure that we’re focusing on what matters, which is learning and completing our milestones, it’s definitely been life-changing for me.”

Although students learn without grades, each master course has a self-assessment midway through the block and a qualifier, or final, at the end. Self-assessments are online and take-home, and only students know their results as scores are anonymous to professors.

Elaine Yang MED ’28 emphasized that these self-assessments give students an opportunity to check their progress in master courses and receive any necessary help before qualifiers. 

“If you do fail, we have a support system,” Yang said. “Yale calls it our longitudinal coaches, who are our academic coaches. So if you fail a self-assessment, they’ll check in with you before you take the qualifier to see if you need ideas or help planning out a study schedule.”

Gil-Hernández echoed the importance of the robust academic support system for students at the School of Medicine.

She noted that the whole class is also assigned a learning specialist — an advisor who discusses studying habits, future goals and learning preferences while working with students to create a personalized schedule that suits their skills and interests.

Since most of the lectures for master courses are not mandatory, students have more flexibility when it comes to finding a learning style that works best for them. While some students enjoy the more traditional in-class learning experience, others prefer watching recorded lectures at their own pace at home and utilizing third-party resources to supplement their learning.

According to Yang, the experience of learning and trying multiple study strategies has been both rewarding and stressful.

“It’s fun to learn how my classmates study because there’s a lot to be learned from them,” Yang said. “But at times it can feel overwhelming and stressful with how much we need to learn and worry about. Am I doing this right? Is there a more efficient way to do this? That’s a big challenge that I am facing, and I think that a lot of people are, too.”

Longitudinal courses

Apart from master courses, first-year students also take longitudinal courses that extend through multiple blocks and complement their learning in master courses.

From September to December, students focused on the applications of ethics in medical practice in “Professional Responsibility.” From January through their second year, students learn about epidemiology and biostatistics in “Population and Methods.”

Between October and May, students also concurrently take a longitudinal course called “Human Anatomy,” which incorporates interactive small-group sessions to cover cases and anatomical structures through dissection.

Alongside lecture courses, first-year students gain clinical knowledge and experience through a “Clinical Skills” course and Interprofessional Longitudinal Clinical Experience — ILCE.

Clinical Skills consists of both a lecture and small group workshops. Josh Brenne MED ’28 noted that students recently began learning how to complete a physical exam in the course, first learning the specific skills required in the lecture and then transitioning into groups of four students led by two faculty members who guide students in practice.

According to Yang, students practice clinical skills in many ways — on fellow medical students, standardized participants trained to act like patients and real patients.

“These small group workshops really give that advantage where not only do I know the content, but I also feel confident enough that I can understand it and interact with it and teach it to other people,” Gil-Hernández added.

In ILCE, students go to the hospital once a week to practice interviewing real patients and performing physical examination skills. At the hospital, a group of three or four medical students and a physician assistant student pair off to spend time talking with patients about their medical history.

Brenne emphasized that opportunities to spend time with patients in the hospital leave him feeling fulfilled and better prepared for the transition to the clerkship phase next January, where students begin their clinical rotations.

“Sitting in a classroom, it’s more like you’re being a student, but when you actually get to talk to someone face to face, when you try to examine them, it feels a bit more like being a real doctor,” Yang said.

Yang added that a crucial part of clinical assessment and preparation at the School of Medicine is the formative feedback that students receive during their clinical experiences. Preceptors help students sharpen their clinical skills and provide feedback about how students can better communicate with patients.

According to Brenne, last school year the School of Medicine also began piloting high-yield workshops, which allow students to self-select themselves for small group workshops with an interactive spin.

“The consistency of people going to this high-yield session really helps us create a class community, a community within that workshop where we know more about each other and learn the content in a more active way,” said Brenne.

Extracurriculars and community

Yale heavily encourages students to participate in research, with many students already in a lab or currently meeting with PIs, according to Brenne.

While undergraduate students are tasked with deciding on a major to study, students at the medical school must find a specialty that they want to pursue in residency and beyond. To help them with the decision, students shadow a variety of physicians. 

For Yang, joining the Yale Journal of Biology and Medicine podcast team helped her find a new career interest in radiation oncology. Through the club, she interviewed a radiation oncologist and became inspired by their conversation to learn more about the specialty and shadow doctors working in the radiation oncology department at Yale.

“I really felt like I could see myself among them. So that gave me a direction to pursue,” Yang said. 

Outside of classes and extracurriculars, first-year students have many opportunities to engage with the community.

Similar to Yale College’s residential college system, the School of Medicine has six advisory houses, formerly known as advisory colleges. 

Students are grouped at random into each house, and the head of the house meets regularly with them to discuss their progress and ensure they are meeting their guidelines. Within each house, coaches are also assigned to six students and meet throughout each master block to help students academically and socially.

Students in each house bond over house dinners and other fun social events throughout the year. 

The class of 2028 has a size of 104 students, which makes it easy for first-year students to build strong connections with their peers.

“I think about when I was an undergrad, and there was no way I was going to get to know everyone,” Gil-Hernández noted. “Now it feels like you have your own community. You get to know people.”

More information about the pre-clerkship curriculum at the School of Medicine can be found online.

The post Inside Yale’s medical school: First years share their experiences appeared first on Yale Daily News.

Journavx, developed by Vertex Pharmaceuticals, provides an alternative to addictive opioid painkillers prescribed to millions of Americans every year. The biological target of the drug was first discovered in the late 1990s in the lab of Professor Stephen Waxman at the School of Medicine. 

“The approval is so important because Journavx is the first and only nonopioid oral pain signal inhibitor, which means that it is a medicine that combines effective pain relief with a favorable safety profile, and it is non-addictive,” Vertex spokesperson wrote to the News. “Every time a physician can prescribe a nonopioid medicine instead of an opioid, it’s an opportunity to avoid their potential liabilities and bend the curve in the right direction.”

According to the most recent Centers for Disease Control and Prevention data, approximately 125 million people were prescribed opioids in 2023, which while effective, have significant safety and tolerability concerns and addictive potential. In fact, around 85,000 people annually develop opioid use disorder within the first year of being prescribed an opioid for acute pain.

Although Journavx is a step forward, some scientists the News spoke to argued that the drug has considerable limitations.

“We shouldn’t also be expecting magic bullets. Some people might be disappointed that the drug won’t cure all pain,” said Sulayman Dib-Hajj, professor of neurology at the School of Medicine, who participated in the original research at Yale.

Yale roots of Journavx

Action potentials, brief electrical signals that muscles and nerves use to communicate, cause pain sensations. These wave-like transmissions move along neurons like an electrical signal through a wire. 

“All neurons communicate with each other by producing nerve impulses,” Waxman told the News. “They’re called action potentials, and they’re due to the opening of sodium channels, and that causes a tiny rush of sodium that becomes explosive, and you get this all or none, little explosion of depolarization.”

During the molecular revolution of the 1980s, scientists discovered an entire family of sodium channels. 

Some of these channels specifically allow for pain signaling. Professor Dib-Hajj explains that local anesthetics, like Novocaine, which dentists use, block all sodium channels in the injection site, preventing pain signals from reaching the brain.

“The problem with using non-selective sodium channel blockers like local anesthetics or drugs is that they have a lot of side effects,” Dib-Hajj explained. “Just think about getting out of the dentist chair. Your cheek feels a lot bigger. You’re slurring your speech.”

Waxman added that if these medications were put into a pill, sodium channels in other areas like the heart and the brain would also be affected, leading to numerous health complications.

The goal is to find a sodium channel uniquely involved in pain transmission, so that blocking it would not affect other important functions, such as heart or cognitive functions, Dib-Hajj said.

“Once it was clear that there were more than one sodium channel, the question came up, might there be one or more types of sodium channels that are present only in peripheral nerves and are important for pain signaling,” Dr. Waxman said.

So, in the late 1990s, Waxman’s lab at Yale began investigating the peripheral sodium channels that propel pain signals.

They eventually deduced that three of these channels — Nav 1.7, 1.8, and 1.9 — work together to produce the nerve impulses in pain signaling.

Nav 1.8, the target for the Journavx drug, is a channel primarily involved in pain transmission that was discovered in 1995 at University College in London, said Dib-Hajj. Waxman and Dib-Hajj immediately started working on studying and targeting the Nav1.8 sodium channel as a potential non-opioid treatment for pain.

At Yale, scientists investigated how this channel behaves at the cellular level through observing it in environments such as human embryonic cell lines, neurons and in its native environment.

However, studying Nav 1.8 in lab settings presented a challenge because the channel does not express well in models. Waxman, Dib-Hajj and their team worked on developing experimental methods to properly express and analyze Nav1.8.

Journavx’s limitations and future research

Three decades later, the FDA has approved Journavx to treat acute pain. However, Dib-Hajj said, more work needs to be done, and Journavx has limitations, especially in its efficacy in treating chronic pain.

“Don’t get me wrong, it’s very exciting that we have something new, especially it’s not addictive, but the effect is not very big, and we still don’t know how effective it’s going to be for chronic pain,” said Dib-Hajj. “That is still a work in progress.”

Dib-Hajj explained that different approaches still need to be researched in order to find drugs that can treat severe pain, such as that caused by chemotherapy. He is also interested in Journavx’s effects on chronic pain.

He explained that he is still looking for “a bigger clinical effect than what this currently improved drug is producing.”

Waxman and Dib-Hajj’s lab plans to move in a different direction from Vertex. Instead of focusing on developing drugs that block Nav1.8 channels, they aim to decrease the total number of these channels on neurons. Fewer Nav1.8 channels mean less pain signal transmission, potentially offering a more long-term solution for chronic pain.

This approach is closer to gene therapy, where a biological agent, such as a virus or RNA-based therapy, reduces the expression of Nav1.8 in neurons. 

Biologic reagents, a type of drug that affects gene expression rather than just blocking the channel at the cell surface, is another possibility. Designing antibodies is another approach Dib-Hajj is investigating to regulate Nav 1.8, as antibody-based therapies are already used in some diseases and could be a non-opioid, long-term pain treatment.

Dib-Hajj explains that drugs like Vertex’s work quickly and are better suited for acute pain, whereas gene therapy or biologics might take longer to show effects, making them more suitable for chronic pain. The goal is to create a “menu” of pain treatments — allowing doctors to choose the best treatment for each patient.

Waxman and Dib-Hajj both emphasized that Vertex’s development underscores the importance of basic science research in academia.

“This channel was discovered in 1996. It’s almost now, 30 years later, and it’s only now that we potentially have something to go to the patients with,” Dib-Hajj said. “Drug development takes time. There are many unsung heroes who are still working at the lab bench.”

Vertex Pharmaceuticals was founded in 1989.

The post First FDA-approved non-addictive painkiller has Yale roots appeared first on Yale Daily News.

The team’s research, driven by a commitment to enhancing diagnostics accessibility across the world, initially focused on a computer vision model that processed the visual data from an electrocardiogram, or ECG. The team detected the presence of left ventricular systolic dysfunction or LVSD, a heart disorder typically not visible on any ECG. 

“A lot of research in the last few years has shown that ECGs are a richer tool than we give them credit for,” noted Dr. Lovedeep Singh Dhingra, the first author of the study and a postdoctoral fellow at the CarDS Lab.

The only other way of screening patients for this condition is through a transthoracic echocardiogram, an ultrasound of the heart that captures any abnormalities in its structure or function.

The imaging technique is not widely available and requires special expertise to read. 

Through an AI tool that trained the model to pick up on previously undetectable signals on a scannable ECG, the team became the first to find a mechanism to pick up LVSD from an ECG image alone and published their first study in 2023. The breakthrough allowed anyone with a smartphone to simply take a photo of an ECG and put it into the model to predict low ejection fraction, a key marker of LVSD.

The team’s next step was to determine if the model for detecting current heart failure in patients could detect signals that predicted future heart failure risk as well. Dr. Arya Aminorroaya, co-author of the study, noted that while risk stratification models that assess heart failure risk do exist, they require a multitude of markers that are difficult to obtain.

“If we can predict the risk of heart failure using this simple ECG, then we could transform the way that we risk stratify patients for heart failure, and we may be able to start patients on therapies sooner rather than later,” Aminorroaya said. 

In their research, the team discovered an interesting phenomenon. Patients who tested false positives with the AI model — meaning that the model detected low ejection fraction when the echocardiogram did not — tended to develop heart failure in the years that followed more often than patients who screened negative.

This discovery proved the team’s hypothesis that the AI tool was capturing a signal that electrocardiograms could not confirm but that was successfully predicting the risk of heart failure.

After this milestone, the researchers continued their work by testing their model in both clinical and population settings. 

The team used data from more than 200,000 patients at Yale New Haven Hospital but frequently ran into the issue of patients switching between hospitals and creating gaps in the long-term clinical data. On the other hand, the team collected data from 40,000 patients at UK Biobank and 13,000 patients from ELSA-Brazil who were systematically followed up for heart failure.

“We had different types of data adjudication across different sources, and the model performed consistently across those different definitions, which is why the model did even better compared to a typical score that would be used for heart failure prediction,” Dhingra said.

The working model is currently hosted on the CarDS lab website, where people can freely use it for research purposes.

Looking to the future, Dhingra sees the project going in three possible directions. 

The first is targeting other heart diseases like valve disorders or hypertrophy through a model that functions as a single, broad screen for structural heart disease. The second is not just inputting clinical ECGs into their model but also testing variable ECGs produced by wearable portable devices like an Apple Watch, allowing people without established health care to screen for future heart failure. The third is running randomized trials to test the clinical effectiveness of the prediction model.

​​“The work of the CarDS Lab is focused on using AI to specifically change the landscape of care in low-resource settings where both equipment and expertise are limited,” Dr. Rohan Khera, senior author of the study and director of the CarDS Lab, wrote to the News.

To learn more about ongoing research at the CarDS lab, see here. 

Update, Feb. 5: The article has been updated to clarify that Dhingra described the team’s research mission, not just his own interest.

The post Yale researchers develop AI tool that predicts risk of heart failure appeared first on Yale Daily News.

The BATMAN — Balloon-Assisted Translocation of the Mitral ANterior leaflet — procedure is an innovative form of transcatheter mitral valve replacement, or TMVR, a minimally invasive operation that replaces the mitral valve in patients by threading a thin catheter through a blood vessel to get to the heart. 

“It’s only been in the last year or two that people have done what’s called a transseptal procedure, where basically we come up from the vein in the leg, and we start out on the right side of the heart,” Vora told the News. “We use a needle to cross over to the left side of the heart, and then that allows us to do everything from sort of a fully percutaneous approach, where at the end of the day, the patient only has a couple of needle sticks coming from the groin.”

Vora, one of the two interventional cardiologists on the team, led this transcatheter-based approach by carefully directing the catheter throughout the operation. 

Even though TMVR has been widely performed worldwide over the past decade, the procedure’s narrow set of anatomical requirements has inspired the creation of new variations that allow mitral valve replacement in a larger group of patients with mitral valve abnormalities. One of these variations — known as the BATMAN procedure — treats patients with leaking of the mitral valve, known as mitral regurgitation, or MR, specifically in cases where challenging patient anatomy prevents the implementation of standard TMVR. 

In these patients, the heart valve replacement could cause the leaflets of the mitral valve to obstruct blood flow through the left ventricle of the heart, making operating highly risky. In the BATMAN procedure, a small hole is created in the leaflet to insert the valve and prevent obstruction of blood flow. 

One of the keys to safely and effectively mitigating the risks presented by patients undergoing the BATMAN procedure is extensive preparation. Patients undergo numerous preoperative tests to create a three-dimensional map of the heart that allows those performing the procedure to meticulously plan each step in advance. 

During the procedure, a combination of X-ray and ultrasound imaging techniques are used to monitor the heart. A continuous X-ray known as a fluoroscopy helps provide constant images of the heart. But, since not all valve structures show up on an X-ray, a transesophageal echocardiogram is also conducted by placing an ultrasound probe in the esophagus to fill in the gaps on the X-ray. 

Reinhardt, the team’s cardiac imaging specialist, analyzed the data from these real-time pictures to guide Vora’s maneuvers. 

“There’s a lot of constant communication between the imager and the interventionists to decide where we need to go, what moves need to be done to get the catheters where they need to be, and when it’s safe to cut,” Reinhardt said. 

On top of constant communication, the success of the BATMAN procedure heavily relies on an integrated multidisciplinary heart team approach. Vora emphasized that interventional cardiologists, cardiac surgeons, imaging specialists, anesthesiologists, radiologists and nurse coordinators all worked together throughout the procedure to ensure personalized treatment. 

Looking to the future, the Yale team’s success opens the doors for a broader group of patients in the greater Connecticut area affected by MR to receive minimally invasive treatment options that avoid the risks of open heart surgery.

“To have a technique that can offer a transcatheter solution to the type of patient with mitral valve disease where open operations are too high risk is a huge advantage,” Vallabhajosyula, the team’s cardiac surgeon partner, said. 

The transcatheter heart valve system used in TMVR was initially approved by the FDA in 2015.

The post Yale multidisciplinary heart team first in Connecticut to perform BATMAN procedure appeared first on Yale Daily News.

The team placed second in the Rainforest competition, organized by XPRIZE, a non-profit organization that organizes public competitions for technological developments that will benefit humanity. Map of Life developed a database to help scientists, researchers and conservation groups to better understand biodiversity trends and protect endangered species. 

The heart of Map of Life is actively assessing the status of different species in specific areas of the world. Over the past years, to go beyond model-based predictions, the team has added a novel Rapid Assessments, or MOLRA, technology. The team then leveraged the combination of models and local sampling for the XPRIZE Rainforest competition.

“Our survey technology involves sending a fleet of semi-autonomous drones into a site to collect visual, audio, and environmental DNA (eDNA) samples which are then processed through our state-of-the-art machine learning workflows to retrieve species identifications, site characteristics, and other important insights,” Tamara Rudic, team member, wrote to the News.

According to Walter Jetz, director of the Yale Center for Biodiversity and Global Change, the Map of Life went live around 12 years ago.

While the use of drones for wildlife data collection is not entirely new in biodiversity research, MOLRA sets itself apart from other technologies by blending species distribution knowledge with cutting-edge sample processing methods. 

The XPRIZE Rainforest competition launched in 2019, and the MOLRA team, in collaboration with the YBGC Center and the Field Museum of Natural History, spent the next two years developing their proposal. 

The team officially qualified for the competition after submitting their plan to XPRIZE in May of 2021, where they detailed the feasibility of their drone-based data collection solution. 

The next big milestone for the team came in the summer of 2022 when it was chosen as a semifinalist for the competition.

“For the following year, our team was hard at work testing and tweaking our solution – initially in our cold Connecticut backyards, and finally in real rainforest locations,” Jetz wrote. 

The team then traveled to Singapore to compete in the semifinals alongside fourteen other teams at the Central Catchment Nature Reserve, the first formal field test of their solution.

For Jetz, Rudic and the rest of the team, this competition stage was a success. However, the team also realized that they needed to incorporate environmental DNA — collected from environmental samples like soil, air or water — into their data collection alongside audiovisual information.

“We quickly got in contact with collaborators at Rutgers University, the Federal University of Amazonas and others to build a robust eDNA component to our sampling plan,” Jetz. 

With global accessibility a top priority for the MOLRA team, another critical challenge during the competition was designing the drone missions in a cost-effective and easily reproducible way. 

Apart from utilizing free and open-source software, the team had to work around the design limitations of affordable unmanned aerial vehicle platforms. 

“We developed a lot of really clever hardware hacks and reverse-engineered a lot of the control systems for these drones and ended up with a system that could be deployed by someone with minimal additional equipment virtually anywhere in the world with minimal to no direct assistance needed from our small team,” Kevin Winner, modeling lead at the YBGC Center, noted.

XPRIZE announced the MOLRA team as one of six finalists in the fall of 2023. The team members then continued testing their solution in the Amazon rainforest or polishing the software and machine learning algorithms behind their biodiversity assessment technology.

Now, with $2 million in prize money, the MOLRA team hopes to grow its innovation.

The prize money will go toward making the MOLRA solution more accessible and globally deployed, thanks to a Yale Ventures-backed university spinoff that will help grow staff, increase involvement with high-impact projects, and continue refinement of the MOLRA technology. 

The team also aims to build on the YBGC Center’s work with global biodiversity management partners to guide future conservation efforts and decisions and to aid efforts such as the Half-Earth Project. 

“It’s the combination of these scientific products on how species are distributed around the world with the on-the-ground monitoring of MOLRA that will make large scale species-based monitoring more efficient and effective,” Alex Killion, managing director at the YBGC Center, wrote. 

For more information on Map of Life Rapid Assessments, see here.

The post Yale team ranked second in biodiversity monitoring competition, earns $2 million prize appeared first on Yale Daily News.