Perelman School of Medicine (PSOM) professors and Penn Bioengineering Graduate Group members Carl June and Avery Posey are leading the charge in T cell therapy and the fight against cancer.
Avery Posey, PhDCarl June, MD
Advances in genome editing through processes such as CRISPR, and the ability to rewire cells through synthetic biology, have led to increasingly elaborate approaches for modifying and supercharging T cells for therapy. Avery Posey, Assistant Professor of Pharmacology, and Carl June, the Richard W. Vague Professor in Immunotherapy, explain how new techniques are providing tools to counter some of the limitations of current CAR T cell therapies in a recent Nature feature.
The pair were also part of a team of researchers from PSOM, the Children’s Hospital of Philadelphia (CHOP), and the Corporal Michael J. Crescenz VA Medical Center to receive an inaugural $8 million Therapy ACceleration To Intercept CAncer Lethality (TACTICAL) Award from the Prostate Cancer Foundation. Their project will develop new clinic-ready CAR T cell therapies for Metastatic Castrate-Resistant Prostate Cancer (mCRPC).
Sevile Mannickarottu, Director of Educational Labs, Penn Bioengineering
Sevile Mannickarottu, Director of Educational Laboratories in the Department of Bioengineering (BE), was interviewed in a recent episode of Shifting Schools, a weekly podcast that hosts educators and thought-leaders in conversations about the latest trends in education and EdTech. Mannickarottu, a Penn Engineering alumnus, runs the George H. Stephenson Foundation Educational Laboratory & Bio-MakerSpace, also known as the Penn BE Labs. In addition to being the primary teaching lab for Penn Bioengineering, the Penn BE Labs has grown into “the world’s only interdisciplinary Bio-MakerSpace.”
MakerSpaces–collaborative, educational work environments–have recently grown in popularity. Penn BE Labs distinguishes itself as a Bio-MakerSpace, embracing the interdisciplinary character of bioengineering by offering itself freely as a space for both academic and personal projects. It is stocked with tools ranging from 3D printers, laser cutters, and electrical equipment, including supplies to support work in molecular biology, physiology, chemistry, and microfluidics.
In the episode, hosts Tricia Friedman and Jeff Utecht talk with Mannickarottu about the organic process by which the Penn BE Labs evolved from a standard teaching space for undergraduate engineering laboratory courses into a student-driven hub of creativity and entrepreneurial spirit that is open to the entire Penn community regardless of discipline or major.
Mannickarottu and his team have found that “creativity needs to let go of control – that’s when fun things happen.” As the lab staff and faculty started to allow more creative freedom in the undergraduate bioengineers’ education, the requests for more supplies started pouring in and the lab’s activities and resources grew. “Honestly, we’re driven almost entirely by student requests and student demands,” says Mannickarottu. So when a student requested a sewing machine for a project? They went out and bought one, adding to their ever-growing stockpile of tools. Over time, more and more diverse projects have emerged from the BE Labs, many of them going on to win awards and grow beyond Penn’s campus as independent startups.
In case this sounds out of reach for smaller institutions, Mannickarottu shares words of encouragement. “The biggest thing,” he says, “is to allow for creativity on the part of the students.” A lab or program can start their own MakerSpace surprisingly inexpensively and build their inventory over time. His number one recommendation for those looking to replicate the success of Penn BE Labs is to allow students freedom to innovate, and administrators will be drawn to invest in the MakerSpace to allow for even more opportunities for them to create and thrive.
To help others get started, the Penn BE Labs staff have put a wide range of resources online, including extensive video and photo archives, FAQ’s, tutorials, information about student projects and startups, and equipment inventories. A 2019 post written for the BE Blog by BE alumna Sophie Burkholder (BSE ‘20 & MSE ‘21) gives the reader tips on “how to build your own MakerSpace for under $1500.”
Though it may currently be “the world’s only interdisciplinary Bio-MakerSpace,” the greatest legacy of the Penn BE Labs would be to be known as the first of many.
Listen to “The legacy of your lab” in Shifting Schools to learn more about the Penn BE Labs and for tips on starting your own MakerSpace.
Artist-in-residence and visiting scholar Rebecca Kamen has blended AI and art to produce animated illustrations representing how a dyslexic brain interprets information.
A work that Penn artist-in-residence Rebecca Kamen produced for the show, “Dyslexic Dictionary” at Arion Press in San Francisco. Here, she reinterprets Ph.D. candidate Dale Zhou’s network visualization. (Image: Cat Fennell)
Communicating thoughts with words is considered a uniquely human evolutionary adaptation known as language processing. Fundamentally, it is an information exchange, a lot like data transfer between devices, but one riddled with discrete layers of complexity, as the ways in which our brains interpret and express ideas differ from person to person.
Learning challenges such as dyslexia are underpinned by these differences in language processing and can be characterized by difficulty learning and decoding information from written text.
Artist-in-residence in Penn’s Department of Physics and Astronomy Rebecca Kamen has explored her personal relationship with dyslexia and information exchange to produce works that reflect elements of both her creative process and understanding of language. Kamen unveiled her latest exhibit at Arion Press Gallery in San Francisco, where nine artists with dyslexia were invited to produce imaginative interpretations of learning and experiencing language.
The artists were presented with several prompts in varying formats, including books, words, poems, quotes, articles, and even a single letter, and tasked with creating a dyslexic dictionary: an exploration of the ways in which their dyslexia empowered them to engage in information exchange in unique ways.
Undiagnosed dyslexia
“[For the exhibit], each artist selected a word representing the way they learn, and mine was ‘lens,’” explains Kamen. “It’s a word that captures how being dyslexic provides me with a unique perspective for viewing and interacting with the world.”
From an early age, Kamen enjoyed learning about the natural sciences and was excited about the process of discovery. She struggled, however, with reading at school, which initially presented an obstacle to achieving her dreams of becoming a teacher. “I had a difficult time getting into college,” says Kamen. “When I graduated high school, the word ‘dyslexia’ didn’t really exist, so I assumed everyone struggled with reading.”
Kamen was diagnosed with dyslexia well into her tenure as a professor. “Most dyslexic people face challenges that may go unnoticed by others,” she says, “but they usually find creative ways to overcome them.”
This perspective on seeing and experiencing the world through the lens of dyslexia not only informed Kamen’s latest work for the exhibition “Dyslexic Dictionary,” but also showcased her background in merging art and science. For decades, Kamen’s work has investigated the intersection of the two, creating distinct ways of exploring new relationships and similarities.
“Artists and scientists are curious creatures always looking for patterns,” explains Kamen. “And that’s because patterns communicate larger insights about the world around us.”
The researchers studied different information-seeking approaches by monitoring how participants explore Wikipedia pages and categorically related these to two ideas rooted in philosophical understandings of learning: a “busybody,” who typically jumps between diverse ideas and collects loosely connected information; and a more purpose-driven “hunter,” who systematically ties in closely related concepts to fill their knowledge gaps.
They used these classifications to inform their computational model, the knowledge network. This uses text and context to determine the degree of relatedness between the Wikipedia pages and their content—represented by dots connected with lines of varying thickness to illustrate the strength of association.
In an adaption of the knowledge network, Kamen was classified as a dancer, an archetype elaborated on in an accompanying review paper by Dale Zhou, a Ph.D. candidate in Bassett’s Complex Systems Lab, who had also collaborated with Kamen on “Reveal.”
“The dancer can be described as an individual that breaks away from the traditional pathways of investigation,” says Zhou. “Someone who takes leaps of creative imagination and in the process, produces new concepts and radically remodels knowledge networks.”
Dani Smith Bassett is J. Peter Skirkanich Professor in Bioengineering with secondary appointments in the Departments of Physics & Astronomy, Electrical & Systems Engineering, Neurology, and Psychiatry.
David Lydon-Staley is an Assistant Professor in the Annenberg School for Communications and Bioengineering and is an alumnus of the Bassett Lab.
Engineers in the Center for Precision Engineering for Health (CPE4H) are focusing on innovations in diagnostics and delivery, cellular and tissue engineering, and the development of new devices that integrate novel materials with human tissues. Below is an excerpt from “Going Small to Win Big: Engineering Personalized Medicine,” featuring the research from the laboratory of Michael Mitchell, J. Peter and Geri Skirkanich Assistant Professor of Innovation in Bioengineering.
The Challenge
Solid tumors evade the immune system’s ability to attack them in part due to the tumors’ tough, fibrous biological barriers that circulating immune cells can’t cross. Researchers need to identify ways to deliver individualized treatments that can better target these tumors without causing damage to healthy tissues or affecting overall quality of life.
The Status Quo
Current cancer treatments typically involve surgery, radiation or chemo- therapy to eliminate solid tumors. These treatments are invasive and can cause numerous negative downstream effects. Newer treatments involve engineering a patient’s immune system to recognize and fight cancerous cells, but are so far only effective against certain “liquid” cancers, where the mutated cells circulate freely in the blood and bone marrow and are small enough to be picked off by the patient’s upgraded T cells. Additionally, existing methods can also require that the cell engineering take place in a lab rather than directly inside the body.
The Mitchell Lab’s Fix
Members of the lab of Michael Mitchell, J. Peter and Geri Skirkanich Assistant Professor of Innovation in Bioengineering, are looking to utilize nanoparticle delivery technology developed by their lab to engineer a different type of immune cell, the macrophage, in order to fight solid- tumor cancers from the inside.
The Mitchell lab is using lipid nanoparticles (LNPs) to carry mRNA and DNA sequences inside of macrophages, a type of immune cell that can consume tumor cells if engineered correctly. In theory, a patient would receive an injection carrying the LNP payload, and the macrophages, whose name literally means “big eaters,” would take up the genetic sequence, alter their function and be able to recognize a patient’s own unique tumor cells in the body.
Because of the way macrophages operate, they could cross the tumor’s biological barrier and attack the cells, destroying the tumor from the inside. An added benefit of the Mitchell Lab’s technology is that the destroyed tumor cells would then also allow other immune cells to present their antigens to circulating T cells, which could then learn to fight those same cancer cells in the future.
“One of the longstanding challenges that we face in the context of cancer and immunotherapies is that every tumor has unique antigens that are specific to patients,” says Mitchell. “This is why we’ve had a lot of trouble developing targeted therapies. Personalizing an approach by harnessing an individual’s immune system gives each patient a greater chance of a positive outcome.”
The model of tubule packing developed by the Hughes Lab shows the tubules repelling each other and shifting around.
A recent study by Penn Bioengineering researchers sheds new light on the role of physics in kidney development. The kidney uses structures called nephrons and tubules to filter blood and pass urine to the bladder. Nephron number is set at birth and can vary over an order of magnitude (anywhere from 100,000 to over a million nephrons in an individual kidney). While the reasons for this variability remain unclear, low numbers of nephrons predispose patients to hypertension and chronic kidney disease.
Now, research published in Developmental Cell led by Alex J. Hughes, Assistant Professor in the Department of Bioengineering, demonstrates a new physics-driven approach to better visualize and understand how a healthy kidney develops to avoid organizational defects that would impair its function. While previous efforts have typically approached this problem using molecular genetics and mouse models, the Hughes Lab’s physics-based approach could link particular types of defects to this genetic information and possibly highlight new treatments to prevent or fix congenital defects.
Alex J. Hughes, Assistant Professor in Bioengineering
Louis Prahl, NIH F32 Postodctoral Fellow
During embryonic development, kidney tubules grow and the tips divide to make a branched tree with clusters of nephron stem cells surrounding each branch tip. In order to build more nephrons, the tree needs to grow more branches. To keep the branches from overlapping, the kidney’s surface grows more crowded as the number of branches increase. “At this point, it’s like adding more people to a crowded elevator,” says Louis Prahl, first author of the paper and Postdoctoral Fellow in the Hughes Lab. “The branches need to keep rearranging to accommodate more until organ growth stops.”
To understand this process, Hughes, Prahl and their team investigated branch organization in mouse kidneys as well as using computer models and a 3D printed model of tubules. Their results show that tubules have to actively restructure – essentially divide at narrower angles – to accommodate more tubules. Computer simulations also identified ‘defective’ packing, in which the simulation parameters caused tubules to either overlap or be forced beneath the kidney surface. The team’s experimentation and analysis of published studies of genetic mouse models of kidney disease confirmed that these defects do occur.
This study represents a unique synthesis of different fields to understand congenital kidney disease. Mathematicians have studied geometric packing problems for decades in other contexts, but the structural features of the kidney present new applications for these models. Previous models of kidney branching have approached these problems from the perspective of individual branches or using purely geometric models that don’t account for tissue mechanics. By contrast, The Hughes Lab’s computer model demonstrates the physics of how tubule families interact with each other, allowing them to identify ‘phases’ of kidney organization that either relate to normal kidney development or organizational defects. Their 3D printed model of tubules shows that these effects can occur even when one sets the biology aside.
Hughes has been widely recognized for his research in the understanding of kidney development. This new publication is the first fruit of his 2021 CAREER Award from the National Science Foundation (NSF) and he was recently named a 2023 Rising Star by the Cellular and Molecular Bioengineering (CMBE) Special Interest Group. In 2020 he became the first Penn Engineering faculty member to receive the Maximizing Investigators’ Research Award (MIRA) from the National Institutes of Health (NIH) for his forward-thinking work in the creation of new tools for tissue engineering.
Pediatric nephrologists have long worked to understand the cause of these childhood kidney defects. These efforts are often confounded by a lack of evidence for a single causative mutation. The Hughes Lab’s approach presents a new and different application of the packing problem and could help answer some of these unsolved questions and open doors to prevention of these diseases. Following this study, Hughes and his lab members will continue to explore the physics of kidney tubule packing, looking for interesting connections between packing organization, mechanical stresses between neighboring tubule tips, and nephron formation while attempting to copy these principles to build stem cell derived tissues to replace damaged or diseased kidney tissue. Mechanical forces play an important role in developmental biology and there is much scope for Hughes, Prahl and their colleagues to learn about these properties in relation to the kidney.
Other authors include Bioengineering Ph.D. students and Hughes Lab members John Viola and Jiageng Liu.
This work was supported by NSF CAREER 2047271, NIH MIRA R35GM133380, Predoctoral Training Program in Developmental Biology T32HD083185, and NIH F32 fellowship DK126385.
Engineers in the Center for Precision Engineering for Health (CPE4H) are focusing on innovations in diagnostics and delivery, cellular and tissue engineering, and the development of new devices that integrate novel materials with human tissues. Below is an excerpt from “Going Small to Win Big: Engineering Personalized Medicine,” featuring the research from the laboratory of Jenny Jiang, J. Peter and Geri Skirkanich Associate Professor of Innovation in Bioengineering.
The Challenge
In order to create personalized immune therapies, researchers need to untangle what is happening between an individual patient’s immune cells and the antigens that they interact with on a molecular level. Immune cell-antigen interactions need to be understood in four different areas in order to create a full picture: the unique genetic sequence of the T cell’s antigen receptors, the antigen specificity of that cell, and both the gene and protein expression of the same cell.
The Status Quo
Prior methods of understanding interactions between T cells and antigens could only get a picture of one or two of these four elements because of technology constraints. Other roadblocks included that cells cultured or engineered in a laboratory setting are not in a natural environment so they won’t express genes or proteins in the way T cells would in the body, and technologies that assess the antigen specificity of T cells were not cost-effective for looking at large numbers of antigens.
The Jiang Lab’s Fix
The lab of Jenny Jiang, J. Peter and Geri Skirkanich Associate Professor of Innovation in Bioengineering, developed a technology called TetTCR-SeqHD, which solves these problems. Using this technology, scientists can now simultaneously profile samples of large numbers of single T cells in the four dimensions using high- throughput screening.
The Jiang Lab’s technology is essentially a method for getting a “full-body scan” of an individual’s T cells and creates a catalog of the different types of T cells and the antigens they respond (or don’t respond) to, paving the way for the ability to better target immune therapies to an individual patient.
“Individual T cells are unique, and that’s the challenge of using one treatment to fit all,” says Jiang. “Identifying antigen specificity and creating therapies that target that specificity in an individual’s T cells will be key to truly personalizing immune therapies in the future.”
University of Pennsylvania scientist Nader Engheta has been selected as a 2023 recipient of the Benjamin Franklin Medal, one of the world’s oldest science and technology awards. The laureates will be honored on April 27 at a ceremony at the Franklin Institute in Philadelphia.
Engheta, H. Nedwill Ramsey Professor in Electrical and Systems Engineering, is among nine outstanding individuals recognized with Benjamin Franklin Medals this year for their achievements in extraordinary scientific, engineering and business leadership.
“As a scientist and a Philadelphian, I am deeply honored and humbled to receive the Franklin Medal. It is the highest compliment to receive an award whose past recipients include some of my scientific heroes such as Albert Einstein, Nikola Tesla, Alexander Graham Bell, and Max Planck. I am very thankful to the Franklin Institute for bestowing this honor upon me.”
Larry Dubinski, President and CEO of The Franklin Institute, says, “We are proud to continue The Franklin Institute’s longtime legacy of recognizing individuals for their contributions to humanity. These extraordinary advancements in areas of such importance as social equity, sustainability, and safety are significantly moving the needle in the direction of positive change and therefore laying the groundwork for a remarkable future.”
The 2023 Benjamin Franklin Medal in Electrical Engineering goes to Engheta for his transformative innovations in engineering novel materials that interact with electromagnetic waves in unprecedented ways, with broad applications in ultrafast computing and communication technologies.
“Professor Engheta’s pioneering work in metamaterials and nano-optics points the way to new and truly revolutionary computing capabilities in the future,” says University of Pennsylvania President Liz Magill. “Penn inaugurated the age of computers by creating the world’s first programmable digital computer in 1945. Professor Engheta’s work continues this tradition of groundbreaking research and discovery that will transform tomorrow. We are thrilled to see him receive the recognition of the Benjamin Franklin Medal.”
Engheta founded the field of optical nanocircuits (“optical metatronics”), which merges nanoelectronics and nanophotonics. He is also known for establishing and& developing the field of near-zero-index optics and epsilon-near-zero (ENZ) materials with near-zero electric permittivity. Through his work he has opened many new frontiers, including optical computation at the nanoscale and scattering control for cloaking and transparency. His work has far-reaching implications in various branches of electrical engineering, materials science, optics, microwaves, and quantum electrodynamics.
“This award recognizes Dr. Engheta’s trailblazing advances in engineering and physics,” says Vijay Kumar, Nemirovsky Family Dean of Penn Engineering.“ The swift and sustainable technologies his research in metamaterials and metatronics offers the world are the result of a lifelong commitment to scientific curiosity. For over 35 years, Nader Engheta has personified Penn Engineering’s mission of inventing the future.”
Nader Engheta is the H. Nedwill Ramsey Professor in the Departments of Electrical and Systems Engineering and Bioengineering in the School of Engineering and Applied Science and professor of physics and astronomy in the School of Arts & Sciences at the University of Pennsylvania.
Researchers at Penn and colleagues have developed a tool to analyze single cells that assesses both the patterns of gene activation within a cell and which sibling cells shared a common progenitor.
Arjun Raj of the School of Engineering and Applied Science and the Perelman School of Medicine, former postdoc Lee Richman, now of Brigham and Women’s Hospital, and colleagues have developed a new analysis tool that combines a cell’s unique gene expression data with information about the cell’s origins. The method can be applied to identify new cell subsets throughout development and better understand drug resistance.
Recent advances in analyzing data at the single-cell level have helped biologists make great strides in uncovering new information about cells and their behaviors. One commonly used approach, known as clustering, allows scientists to group cells based on characteristics such as the unique patterns of active or inactive genes or by the progeny of duplicating cells, known as clones, over several generations.
Although single-cell clustering has led to many significant findings, for example, new cancer cell subsets or the way immature stem cells mature into “specialized” cells, researchers to this point had not been able to marry what they knew about gene-activation patterns with what they knew about clone lineages.
Now, research published in Cell Genomics led by University of Pennsylvania professor of bioengineering Arjun Raj has resulted in the development of ClonoCluster, an open-source tool that combines unique patterns of gene activation with clonal information. This produces hybrid cluster data that can quickly identify new cellular traits; that can then be used to better understand resistance to some cancer therapies.
“Before, these were independent modalities, where you would cluster the cells that express the same genes in one lot and cluster the others that share a common ancestor in another,” says Lee Richman, first paper author and a former postdoc in the Raj lab who is now at Brigham and Women’s Hospital in Boston. “What’s exciting is that this tool allows you to draw new lines around your clusters and explore their properties, which could help us identify new cell types, functions, and molecular pathways.”
Researchers in the Raj Lab use a technique known as barcoding to assign labels to cells they are interested in studying, particularly useful for tracking cells, clustering data based on cells’ offspring, and following lineages over time. Believing they could parse more valuable information out of this data by incorporating the cell’s unique patterns of gene activation, the researchers applied ClonoCluster to six experimental datasets that used barcoding to track dividing cells’ offspring. Specifically, they looked at the development of chemotherapy resistance and of stem cells into specialized tissue types.
Members of the research team include (from left to right) Xuexiang Han, Michael J. Mitchell, Ningqiang Gong, Lulu Xue, Sarah J. Shepherd, and Rakan El-Mayta.
Since the success of the COVID-19 vaccine, RNA therapies have been the object of increasing interest in the biotech world. These therapies work with your body to target the genetic root of diseases and infections, a promising alternative treatment method to that of traditional pharmaceutical drugs.
Lipid nanoparticles (LNPs) have been successfully used in drug delivery for decades. FDA-approved therapies use them as vehicles for delivering messenger RNA (mRNA), which prompts the cell to make new proteins, and small interfering RNA (siRNA), which instruct the cell to silence or inhibit the expression of certain proteins.
The biggest challenge in developing a successful RNA therapy is its targeted delivery. Research is now confronting the current limitations of LNPs, which have left many diseases without an effective RNA therapy.
Liver fibrosis occurs when the liver is repeatedly damaged and the healing process results in the accumulation of scar tissue, impeding healthy liver function. It is a chronic disease characterized by the buildup of excessive collagen-rich extracellular matrix (ECM). Liver fibrosis has remained challenging to treat using RNA therapies due to a lack of delivery systems for targeting activated liver-resident fibroblasts. Both the solid fibroblast structure and the lack of specificity or affinity to target these fibroblasts has impeded current LNPs from entering activated liver-resident fibroblasts, and thus they are unable to deliver RNA therapeutics.
To tackle this issue and help provide a treatment for the millions of people who suffer from this chronic disease, Michael Mitchell, J. Peter and Geri Skirkanich Assistant Professor of Innovation in the Department of Bioengineering, and postdoctoral fellows Xuexiang Han and Ningqiang Gong, found a new way to synthesize ligand-tethered LNPs, increasing their selectivity and allowing them to target liver fibroblasts.
Lulu Xue, Margaret Billingsley, Rakan El-Mayta, Sarah J. Shepherd, Mohamad-Gabriel Alameh and Drew Weissman, Roberts Family Professor in Vaccine Research and Director of the Penn Institute for RNA Innovation at the Perelman School of Medicine, also contributed to this work.
Penn Engineering’s newly established ASSET Center aims to make AI-enabled systems more “safe, explainable and trustworthy” by studying the fundamentals of the artificial neural networks that organize and interpret data to solve problems.
ASSET’s first funding collaboration is with Penn’s Perelman School of Medicine (PSOM) and the Penn Institute for Biomedical Informatics (IBI). Together, they have launched a series of seed grants that will fund research at the intersection of AI and healthcare.
Teams featuring faculty members from Penn Engineering, Penn Medicine and the Wharton School applied for these grants, to be funded annually at $100,000. A committee consisting of faculty from both Penn Engineering and PSOM evaluated 18 applications and judged the proposals based on clinical relevance, AI foundations and potential for impact.
Artificial intelligence and machine learning promise to revolutionize nearly every field, sifting through massive amounts of data to find insights that humans would miss, making faster and more accurate decisions and predictions as a result.
Applying those insights to healthcare could yield life-saving benefits. For example, AI-enabled systems could analyze medical imaging for hard-to-spot tumors, collate multiple streams of disparate patient information for faster diagnoses or more accurately predict the course of disease.
Given the stakes, however, understanding exactly how these technologies arrive at their conclusions is critical. Doctors, nurses and other healthcare providers won’t use such technologies if they don’t trust that their internal logic is sound.
“We are developing techniques that will allow AI-based decision systems to provide both quantifiable guarantees and explanations of their predictions,” says Rajeev Alur, Zisman Family Professor in Computer and Information Science and Director of the ASSET Center. “Transparency and accuracy are key.”
“Development of explainable and trustworthy AI is critical for adoption in the practice of medicine,” adds Marylyn Ritchie, Professor of Genetics and Director of the Penn Institute for Biomedical Informatics. “We are thrilled about this partnership between ASSET and IBI to fund these innovative and exciting projects.”
Seven projects were selected in the inaugural class, including projects from Dani S. Bassett, J. Peter Skirkanich Professor in the Departments of Bioengineering, Electrical and Systems Engineering, Physics & Astronomy, Neurology, and Psychiatry, and several members of the Penn Bioengineering Graduate Group: Despina Kontos, Matthew J. Wilson Professor of Research Radiology II, Department of Radiology, Penn Medicine and Lyle Ungar, Professor, Department of Computer and Information Science, Penn Engineering; Spyridon Bakas, Assistant Professor, Departments of Pathology and Laboratory Medicine and Radiology, Penn Medicine; and Walter R. Witschey, Associate Professor, Department of Radiology, Penn Medicine.
Optimizing clinical monitoring for delivery room resuscitation using novel interpretable AI
Elizabeth Foglia, Associate Professor, Department of Pediatrics, Penn Medicine and the Children’s Hospital of Philadelphia
Dani S. Bassett, J. Peter Skirkanich Professor, Departments of Bioengineering and Electrical and Systems Engineering, Penn Engineering
This project will apply a novel interpretable machine learning approach, known as the Distributed Information Bottleneck, to solve pressing problems in identifying and displaying critical information during time-sensitive clinical encounters. This project will develop a framework for the optimal integration of information from multiple physiologic measures that are continuously monitored during delivery room resuscitation. The team’s immediate goal is to detect and display key target respiratory parameters during delivery room resuscitation to prevent acute and chronic lung injury for preterm infants. Because this approach is generalizable to any setting in which complex relations between information-rich variables are predictive of health outcomes, the project will lay the groundwork for future applications to other clinical scenarios.
Mannickarottu and his team have found that “creativity needs to let go of control – that’s when fun things happen.” As the lab staff and faculty started to allow more creative freedom in the undergraduate bioengineers’ education, the requests for more supplies started pouring in and the lab’s activities and resources grew. “Honestly, we’re driven almost entirely by student requests and student demands,” says Mannickarottu. So when a student requested a sewing machine for a project? They went out and bought one, adding to their ever-growing stockpile of tools. Over time, more and more diverse projects have emerged from the BE Labs, many of them going on to win awards and grow beyond Penn’s campus as independent 
To help others get started, the Penn BE Labs staff have put a wide range of resources 
