A tailored silicon nanopattern coupled with a semi-transparent gold mirror can solve a complex mathematical equation using light. (Image credit: Ella Maru studio)
Researchers at the University of Pennsylvania, AMOLF, and the City University of New York (CUNY) have created a surface with a nanostructure capable of solving mathematical equations.
Powered by light and free of electronics, this discovery introduces exciting new prospects for the future of computing.
Engheta is the founder of the influential field of “optical metatronics.” He creates materials that interact with photons to manipulate data at the speed of light. Engheta’s contribution to this study marks an important advance in his quest to use light-matter interactions to surpass the speed and energy limitations of digital electronics, bringing analog computing out of the past and into the future.
“I began the work on optical metatronics in 2005,” says Engheta, “wondering if it were possible to recreate the elements of a standard electronic circuit at nanoscale. At this tiny size, it would be possible to manipulate the circuit with light, rather than electricity. After achieving this, we became more ambitious, envisioning collections of these nanocircuits as processors. In 2014, we were designing materials that used these optical nanostructures to perform mathematical operations, and in 2019, we anted up to entire mathematical equations using microwaves. Now, my collaborators and I have created a surface that can solve equations using light waves, a significant step closer to our larger goals for computing materials.”
The study, recently published in Nature Nanotechnology, demonstrates the possibility of solving complex mathematical problems and a generic matrix inversion at speeds far beyond those of typical digital computing methods.
The solution converges in about 349 femtoseconds (less than one trillionth of a second), orders of magnitude faster than the clock speed of a conventional processor.
Nader Engheta is the H. Nedwill Ramsey Professor in the Departments of Electrical and Systems Engineering and in Bioengineering in the School of Engineering and Applied Science and Professor in Physics and Astronomy in the School of Arts & Sciences at the University of Pennsylvania.
Gregory Bowman, the Louis Heyman University Professor, has joint appointments in the Department of Biochemistry and Biophysics in the Perelman School of Medicine and the Department of Bioengineering in the School of Engineering and Applied Science. (Image: Courtesy of School of Engineering and Applied Sciences)
His research aims to combat global health threats such as COVID-19 and Alzheimer’s disease by better understanding how proteins function and malfunction, especially through new computational and experimental methods that map protein structures. This understanding of protein dynamics can lead to effective new treatments for even the most seemingly resistant diseases.
“Delivering the right treatment to the right person at the right time is vital to sustaining—and saving—lives,” Magill said. “Greg Bowman’s novel work holds enormous promise and potential to advance new forms of personalized medicine, an area of considerable strength for Penn. A gifted researcher and consummate collaborator, we are delighted to count him among our distinguished PIK University Professors.”
Bowman came to Penn from the Washington University School of Medicine’s Department of Biochemistry and Molecular Biophysics, where he served on the faculty since 2014. He previously completed a three-year postdoctoral fellowship at the University of California, Berkeley.
Bowman’s research utilizes high-performance supercomputers for simulations that can better explain how mutations and disease change a protein’s functions. These simulations are enabled in part through the innovative Folding@home project, which Bowman directs. Folding@home empowers anyone with a computer to run simulations alongside a consortium of universities, with more than 200,000 participants worldwide.
His research has been supported by the National Science Foundation, National Institutes of Health, National Institute on Aging, and Packard Foundation, among others, and he has received a CAREER Award from the NSF, Career Award at the Scientific Interface from the Burroughs Wellcome Fund, and Thomas Kuhn Paradigm Shift Award from the American Chemical Society. He received a Ph.D. in biophysics from Stanford University and a B.S. (summa cum laude) in computer science, with a minor in biomedical engineering, from Cornell University.
“Greg Bowman’s highly innovative work,” Winkelstein said, “exemplifies the power of our interdisciplinary mission at Penn. He brings together supercomputers, biophysics, and biochemistry to make a vital impact on public health. This brilliant fusion of methods—in the service of improving people’s lives around the world—will be a tremendous model for the research of our faculty, students, and postdocs in the years ahead.”
The Penn Integrates Knowledge program is a University-wide initiative to recruit exceptional faculty members whose research and teaching exemplify the integration of knowledge across disciplines and who are appointed in at least two schools at Penn.
The Louis Heyman University Professorship is a gift of Stephen J. Heyman, a 1959 graduate of the Wharton School, and his wife, Barbara Heyman, in honor of Stephen Heyman’s uncle. Stephen Heyman is a University Emeritus Trustee and member of the School of Nursing Board of Advisors. He is Managing Partner at Nadel and Gussman LLC in Tulsa, Oklahoma.
nucleus and membrane of pathogen micro organisms in blue background
Up to 50 percent of cancer-signaling proteins once believed to be immune to drug treatments due to a lack of targetable protein regions may actually be treatable, according to a new study from the Perelman School of Medicine at the University of Pennsylvania. The findings, published this month in Nature Communications, suggest there may be new opportunities to treat cancer with new or existing drugs.
Researchers, clinicians, and pharmacologists looking to identify new ways to treat medical conditions—from cancer to autoimmune diseases—often focus on protein pockets, areas within protein structures to which certain proteins or molecules can bind. While some pockets are easily identifiable within a protein structure, others are not. Those hidden pockets, referred to as cryptic pockets, can provide new opportunities for drugs to bind to. The more pockets scientists and clinicians have to target with drugs, the more opportunities they have to control disease.
The research team identified new pockets using a Penn-designed neural network, called PocketMiner, which is artificial intelligence that predicts where cryptic pockets are likely to form from a single protein structure and learns from itself. Using PocketMiner—which was trained on simulations run on the world’s largest super computer—researchers simulated single protein structures and successfully predicted the locations of cryptic pockets in 35 cancer-related protein structures in thousands of areas of the body. These once-hidden targets, now identified, open up new approaches for potentially treating existing cancer.
What’s more, while successfully predicting the cryptic pockets, the method scientists used in this study was much faster than previous simulation or machine-learning methods. The network allows researchers to nearly instantaneously decide if a protein is likely to have cryptic pockets before investing in more expensive simulations or experiments to pursue a predicted pocket further.
“More than half of human proteins are considered undruggable due to an apparent lack of binding proteins in the snapshots we have,” said Gregory R. Bowman, PhD, a professor of Biochemistry and Biophysics and Bioengineering at Penn and the lead author of the study. “This PocketMiner research and other research like it not only predict druggable pockets in critical protein structures related to cancer but suggest most human proteins likely have druggable pockets, too. It’s a finding that offers hope to those with currently untreatable diseases.”
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.
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.
Penn Integrates Knowledge Professor Kevin Johnson takes the stage at 24th Engaging Minds. (Image: Ben Asen)
This past weekend in New York City, the University of Pennsylvania showcased its 24th Engaging Minds event, the first in person since 2019. It was hosted by Penn Alumni.
Three Penn Integrates Knowledge University Professors — Kevin Johnson, Lance Freeman and Dolores Albarracín, — each discussed their research. The audience, at least 600 in person and remote, heard about using city planning to promote racial equity, about how conspiracy theories come to life and propagate, and about the need for physicians to communicate effectively with patients and families.
Following brief remarks from Penn Alumni President Ann Reese, University President Liz Magill introduced the event. “As many of you know, I’ve been thinking a lot and speaking often about what makes Penn Penn,” she said. “What are our distinctive strengths? What are the unique contributions to society that we have made in the past and can make in the future? And where do we go from the extraordinary position we are in now?”
Magill went on to express gratitude for the speakers and invited the audience to think about how the researchers’ work and expertise furthered what she described as the “twin principles of truth and opportunity.”
He took the audience through his family history, education and training, pausing at a point on the timeline when he was a young physician-scientist who had just explained a new medical topic to a journalist. “I felt really good about the conversation — and then the article came out,” Johnson said.
In the piece, he had been cast as saying that the medical community was over-treating this condition, “which is not what I said.” He realized in that moment that as a physician, he had been taught to communicate what a study finds, not how to act based on those findings. That experience shifted his thinking on how to communicate scientific topics, and he has spent decades trying to move the needle on how others in his field perceive this.
“As scientists we face obstacles. We face the obstacle of scale, so, small projects that we’re asked to generalize. We face the issue of trust. And then we face the issue of values,” Johnson said. “I’ll add a fourth, which is format; the way we choose to reach specific audiences will be different.”
Read more about the 24th Engaging Minds at Penn Today.
Kevin Johnson is the David L. Cohen University of Pennsylvania Professor in the Departments of Biostatistics, Epidemiology and Informatics and Computer and Information Science. As a Penn Integrates Knowlegde (PIK) University Professor, Johnson also holds appointments in the Departments of Bioengineering and Pediatrics, as well as in the Annenberg School of Communication.
Each year, the the Department of Bioengineering seeks exceptional candidates to conduct summer research in bioengineering with the support of two scholarships: the Abraham Noordergraaf Student Summer Bioengineering Research Fund and the Blair Undergraduate Research Fund in the Department of Bioengineering. These scholarships provide a living stipend for students to conduct research on campus in a Penn research lab under the mentorship of a faculty member. The Abraham Noordergraaf Student Summer Bioengineering Research Fund provides financial support for undergraduate or graduate summer research opportunities in bioengineering with a preference for study in the area of cardiovascular systems. Dr. Noordergraaf, who died in 2014, was a founding member and first chair of Penn Bioengineering. The Blair Undergraduate Research Fund in the Department of Bioengineering supports three to five undergraduate research scholars each year with the support of Dr. James C. Blair II. After a competitive round of proposals, the following six scholars were chosen for the Summer 2022 semester. Keep reading below for the research abstracts and bios of the awardees.
The Blair Undergraduate Research Fund in the Department of Bioengineering (Blair Scholars)
Ella Atsavapranee
Student: Ella Atsavapranee (BE Class of 2023)
PI: Michael J. Mitchell, J. Peter and Geri Skirkanich Assistant Professor of Innovation, Bioengineering
“Lipid nanoparticle-mediated delivery of RAS protease to inhibit cancer cell growth”
Mutations in RAS, a family of proteins found in all human cells, drive a third of cancers, including many pancreatic, colorectal, and lung cancers. However, there are still no therapies that can effectively prevent RAS from causing tumor growth. Recently, a protease was engineered to specifically degrade active RAS, offering a promising new tool for treating these cancers. However, many protein-based therapies still cannot be effectively delivered to patients. Lipid nanoparticles (LNPs), which were used in the Pfizer-BioNTech and Moderna COVID-19 vaccines, have emerged as a promising platform for safe and effective delivery of both nucleic acids and proteins. We formulated a library of LNPs using different cationic lipids. We characterized the LNPs by size, charge, and pKa, and tested their ability to deliver fluorescently labeled protease. The LNPs were able to encapsulate and deliver a RAS protease, successfully reducing proliferation of colon cancer cells.
Ella is a senior from Maryland studying bioengineering and chemistry. She works in Dr. Michael Mitchell’s lab, developing lipid nanoparticles to deliver proteins that reduce cancer cell proliferation. She has also conducted research on early-stage cancer detection and therapy monitoring (at Stanford University) and drug delivery across the blood-brain barrier for neurodegenerative diseases (at University of Maryland). She is passionate about translational research, science communication, and promoting diversity in STEM.
Chiadika Eleh
Student: Chiadika Eleh (BE and CIS Class of 2024)
PI: Eric J. Brown, Associate Professor of Cancer Biology, Perelman School of Medicine
“Investigating Viability in ATR and WEE1 Inhibitor Treated Ovarian Cancer Cells”
High-grade serous ovarian cancers (HGSOCs) are an aggressive subtype of ovarian cancer, accounting for up to 80% of all ovarian cancer-related deaths. More than half of HGSOCs are homologous recombination deficient; thus, they lack a favorable response when treated with common chemotherapeutic trials. Therefore, new treatment strategies must be developed to increase the life expectancy and quality of life of HGSOC patients. To address the lack of effective treatment options, the Brown Lab is interested in combining ATR and WEE1 inhibition (ATRi/WEE1i) to target HGSOC cells. It has previously been shown that low-dose ATRi/WEE1i is an effective treatment strategy for CCNE1-amplified ovarian cancer-derived PDX tumors (Xu et al., 2021, Cell Reports Medicine). Therefore, the next step is to characterize the HGSOC-specific response to ATRi/WEE1i treatment. This project aims to characterize the viability phenotype of ovarian cancer (OVCAR3) cells in the presence of ATRi/WEE1i in both single and combination treatments. With further research, Eleh hopes to prove the hypothesis low-dose combination ATRi/WEE1i treatment will result in the synergistic loss of viability in OVCAR3 cells. This goal will be achieved through the treatment of OVCAR3 cells with ranging doses of ATRi and Wee1i over 24 and 48 hour time intervals. We hope that this data will help set a treatment baseline that can be used for all OVCAR30-based viability experiments in the future.
Chiadika Eleh is a Bioengineering and Computer Science junior and a member of Penn Engineering’s Rachleff Scholar program. As a Blair Scholar, she worked in Dr. Eric Brown’s cancer biology lab, where she studied cell cycle checkpoint inhibitors as a form of cancer treatment.
“Tbc1d2b regulates vascular formation during development and tissue repair after ischemia”
The mechanisms behind endothelial cells forming blood vessels remains unknown. We have identified Tbc1d2b as a protein that is integral to the regulation of vascular formation. In order to investigate the role of Tbc1d2b in tubule formation, fibrin gel bead assays will be conducted to evaluate how the presence of Tbc1d2b is required for angiogenesis. Fibrin gel bead assays simulate the extracellular matrix environment to support the in vitro development of vessels from human umbilical vein endothelial cells (HUVEC) coated on cytodex beads. In order to confirm the success of angiogenesis, immunostaining for Phalloidin and CD31 will be conducted. After confirmation that fibrin gel bead assays can produce in vitro tubules, sgRNA CRISPR knockout of Tbc1d2b will be performed on HUVEC cells which will then be used to conduct more fibrin gel bead assays. We hypothesize that HUVEC with the Tbc1d2b knockout phenotype will be unable to form tubules while wild type HUVEC will be able to.
Gloria Lee is a rising senior studying Bioengineering and Physics in the VIPER program from Denver, Colorado. Her research in Dr. Yi Fan’s lab focuses on the role that proteins play in cardiovascular tubule formation.
Abraham Noordergraaf Student Summer Bioengineering Research Fund (Noordergraaf Fellows)
Gary Lin
Student: Gary Lin (Master’s in MEAM Class of 2023)
PI: Michelle J. Johnson, Associate Professor in Physical Medicine and Rehabilitation, Perelman School of Medicine, and in Bioengineering
“Development and Integration of Dynamically Modulating Control Systems in the Rehabilitation Using Community-Based Affordable Robotic Exercise System (Rehab CARES)”
As the number of stroke patients requiring rehabilitative care continues to increase, strain is being put onto the US health infrastructure which already has a shortage of rehabilitation practitioners. To help alleviate this pressure, a cost-effective robotic rehabilitative platform was developed to increase access to rehabilitative care. The haptic TheraDrive, a one-degree of freedom actuated hand crank that can apply assistive and resistive forces, was modified to train pronation and supination at the elbow and pinching of the fingers in addition to flexion and extension of the elbow and shoulder. Two controllers were created including an open-loop force controller and a closed-loop proportional-integral (PI) with adaptive control gains based on subject performance in therapy-game tasks as well as galvanic skin response. Stroke subjects (n=11) with a range of cognitive and motor impairment completed 4 therapy games in both adaptive and non-adaptive versions of the controllers (n=8) while measuring force applied on the TheraDrive handle. Resulting normalized average power versus Upper Extremity Fugl-Meyer (UE-FM) and Montreal Cognitive Assessment (MoCA) correlation analyses showed that power was strongly correlated with UE-FM in 2 of the conditions and moderately correlated with the other 6 while MoCA was moderate correlated to 2 of the conditions and weakly correlated to the rest. Mann-Whitney U-tests between adaptive and non-adaptive versions of each therapy game showed no significant differences with regards to power between controller types (p<0.05).
Gary is a master’s student in the School of Engineering studying Mechanical Engineering and Applied Mechanics with a concentration in Robotic and Mechatronic systems. His research primarily focuses on developing affordable rehabilitation robotics for use in assessment and game-based therapies post neural injury. Many of his interests revolve around the design of mechatronic systems and the algorithms used to control them for use in healthcare spaces.
Priya Shah
Student: Priya Shah (BE Class of 2024)
PI: Alex J. Hughes, Assistant Professor in Bioengineering
“Optogenetic Control of Developing Kidney Cells for Future Treatment of End-Stage Renal Disease”
This project sought to build from prior research in the Hughes Lab on the geometric and mechanical consequences of kidney form on cell and tissue-scale function. While the developmental trajectory of the kidney is well understood, little is currently known about many factors affecting nephron progenitor differentiation rate. Insufficient differentiation of nephron progenitor cells during kidney formation can result in lower nephron number and glomerular density, which is a risk factor for progression to end-stage renal disease later in life. Prior studies indicated that the amount of nephron differentiation – and thus function of the adult kidney – is correlated to the packing of ureteric tubule tips present at the surface of the kidney. Building off of research conducted in the Bugaj Lab, we found that inserting an optogenetic construct into the genome of human embryonic kidney (HEK) cells allowed us to manipulate the contraction of those cells through exposing them to blue light. Manipulating the contraction of the cells allows for the manipulation of the packing of ureteric tubule tips at the kidney surface. We used a lentiviral vector to transduce HEK293 cells with the optogenetic construct and witnessed visible contraction of the cells when they were exposed to blue light. Future work will include using CRISPR-Cas9 to introduce the optogenetic construct into IPS cells.
Priya is a junior studying bioengineering and had the opportunity to work on manipulating developing kidney cells using an optogenetic construct in the Hughes Lab this summer. She is thrilled to continue this research throughout the coming school year. Outside of the lab, Priya is involved with the PENNaach dance team and the Society of Women Engineers, as well as other mentorship roles.
Cosette Tomita
Student: Cosette Tomita (Master’s in MEAM Class of 2023)
“Expression and Characterization of an Anti-Aβ42 scFv”
Background: Amyloid Beta (Aβ42) fibrils contribute to the pathology of Alzheimer’s Disease. Numerous monoclonal antibodies have been developed against Aβ42. In this study we have designed and expressed a short chain variable fragment specific to Aβ42 (Anti-Aβ42 scFv). To characterize our anti-Aβ42 scFv we have performed structural analysis using transmission electron microscopy (TEM) and binding kinetics using microscale thermophoresis (MST) compared to commercially available antibodies 6E10, Aducanumab, and an IgG isotype control. The goal of this study is to determine if labeling densities and binding constants for Aducanumab and anti-Aβ42 scFv are not significantly different.
Method: To characterize Aβ42 fibril associated antibodies we used negative stain TEM. Aβ42 fibrils were stained on a glow discharged copper grid, and incubated with gold conjugated anti-Aβ42 scFv, 6E10—which binds all Aβ species, aducanumab, or IgG isotype control. Labeling densities were calculated as the number of fibril-associated gold particles per 1 μm2 for each image. Next, we used microscale thermophoresis determine the binding kinetics. Antibodies or anti-Aβ42 scFv were labeled with Alexa Fluor-647 and unlabeled Aβ42 was titrated in a serial dilution over 16 capillaries. The average fluorescence intensity was plotted against the antibody or scFv concentration and the curves were analyzed using the GraphPad Prism software to calculate the dissociation constant (KD) values.
Results: We found a significant difference, tested with a one-way ANOVA (P <0.0001), in gold particle associated Aβ fibrils per 1 μm2 between anti-Aβ42 scFv, 6E10, aducanumab, and IgG isotype control. Further analysis of aducanumab and 6CO3 with unpaired student t-test indicates significant differences in fibril associated gold particles between aducanumab vs. 6E10 (P=0.0003), Aducanumab vs. Isotype control (P <0.0001), anti-Aβ42 scFv vs 6E10 (p=0.0072), and anti-Aβ42 scFv vs Isotype Control (P=0.0029) with no significant difference in labeling densities between Aducanumab and anti-Aβ42 scFv. The expected KD values from MST were 1.8μM for Aducanumab and anti-Aβ42 scFv, 10.3nM for 6E10 and no expected binding for the isotype control. The experimental KD values for anti-Aβ42 scFv and 6E10 are 0.1132μM and 1.467μM respectively. The KD value for Isotype control was undetermined, as expected, however, the KD for Aducanumab was undetermined due to suboptimal assay conditions. Due to confounding variables in the experimental set up such as the use of Aβ1-16 compared to Aβ42 and the use of different fluorophores—5-TAMRA, Alexa Fluor 647 or FITC— the experimental KD values were off by several orders of magnitude.
Conclusion: We have illustrated similar labeling densities between Aducanumab and our anti-Aβ42 scFv. In the future, we will further optimize the MST assay conditions and compare the KD values obtained by MST with other techniques such as surface plasma resonance.
Cosette was born and raised in Chicago land area. Go Sox! She attended University of Missouri where she majored in Chemistry and Biology. She synthesized sigma-2 radiotracers and developed advanced skills in biochemical techniques in Dr. Susan Lever’s lab. After graduation, she moved to NJ to work at Lantheus, a radiopharmaceutical company. She missed academia and the independence of program and project development, so she came to work at the Penn Cyclotron facility before entering the Bioengineering master’s program.
In 2005, John Ioannidis published a bombshell paper titled “Why Most Published Research Findings Are False.” In it, Ioannidis argued that a lack of scientific rigor in biomedical research — such as poor study design, small sample sizes and improper assessment of the significance of data— meant that a large percentage of experiments would not return the same results if they were conducted again.
Since then, researchers’ awareness of this “replication crisis” has grown, especially in fields that directly impact the health and wellbeing of people, where lapses in rigor can have life-or-death consequences. Despite this attention and motivation, however, little progress has been made in addressing the roots of the problem. Formal training in rigorous research practices remains rare; while mentors advise their students on how to properly construct and conduct experiments to produce the most reliable evidence, few educational resources exist to support them.
Konrad Kording, a Penn Integrates Knowledge Professor with appointments in the Departments of Bioengineering and Computer and Information Science in Penn Engineering and the Department of Neuroscience in Penn’s Perelman School of Medicine, has been awarded one of the initiative’s first five grants.
“The replication crisis is real,” says Kording. “I’ve tried to replicate the research of others and failed. I’ve reanalyzed my own data and found major mistakes that needed to be corrected. I was never properly taught how to do rigorous science, and I want to improve that for the next generation.”
Though the technology for brain-computer interfaces (or BCI’s) has existed for decades, recent strides have been made to create BCI devices which are safer, smaller, and more effective. Konrad Kording, Nathan Francis Mossell University Professor in Bioengineering, Neuroscience, and Computer and Information Science, helps to elucidate the potential future of this technology in a recent feature in Wired. In the article, he discusses the “invasive” aspects of previous BCI technology, in contrast to recent innovations, such as a new device by Synchron, which do not require surgery and are consequently much less risky:
“The device, called a Stentrode, has a mesh-like design and is about the length of a AAA battery. It is implanted endovascularly, meaning it’s placed into a blood vessel in the brain, in the region known as the motor cortex, which controls movement. Insertion involves cutting into the jugular vein in the neck, snaking a catheter in, and feeding the device through it all the way up into the brain, where, when the catheter is removed, it opens up like a flower and nestles itself into the blood vessel’s wall. Most neurosurgeons are already up to speed on the basic approach required to put it in, which reduces a high-risk surgery to a procedure that could send the patient home the very same day. ‘And that is the big innovation,” Kording says.
Congratulations to Kevin B. Johnson, David L. Cohen University Professor, on his recent appointed as a Senior Fellow in the Leonard Davis Institute of Health Economics at the University of Pennsylvania (Penn LDI). Johnson, an expert in health care innovation and health information technology, holds appointments in Biostatistics, Epidemiology and Informatics in the Perelman School of Medicine and Computer and Information Science in the School of Engineering and Applied Science. He also holds secondary appointments in Bioengineering, Pediatrics, and in the Annenberg School of Communication and is Vice President for Applied Informatics in the University of Pennsylvania Health System.
Penn LDI is Penn’s hub for health care delivery, health policy, and population health, we connect and amplify experts and thought-leaders and train the next generation of researchers. Johnson joins over 500 Fellows from across all of Penn’s schools, the University of Pennsylvania Health System, and the Children’s Hospital of Philadelphia. Johnson brings expertise in Health Care Innovation, Health Information Technology, Medication Adherence, and Social Media to his new fellowship and has extensively studied healthcare informatics with the goal of improving patient care.
