The Academic Minute

How can we use technology to clean the air around us?

Petra Fromme, Regents Professor of Chemistry and Biochemistry and director of the Biodesign Center for Applied Structural Discovery at Arizona State University, examines how humidity can play a role in doing so.

Faculty Bio:

Petra Fromme, Regents’ Professor of Chemistry and Biochemistry and Director of the Biodesign Center for Applied Structural Discovery at Arizona State University, investigates the structure and function of biological and synthetic systems to advance solutions in energy, health, and sustainability.Petra Fromme, Ph.D., received her doctoral degree and habilitation in physical chemistry from the Technical University of Berlin in Germany, following her studies in biochemistry at the Free University of Berlin. She began her academic career at the Max Volmer Institute in Berlin before joining Arizona State University in 2002, where she has since become a leader in interdisciplinary research and innovation in structural biology and biophysics.Her research focuses on uncovering the molecular mechanisms of energy conversion and biological function, particularly through studies of membrane proteins such as photosystems. She has pioneered the use of X-ray free-electron lasers for serial femtosecond crystallography, enabling high-resolution structural determination of biomolecules and capturing dynamic processes. Her work has resulted in many impactful peer-reviewed publications and has significantly advanced the fields of structural biology and renewable energy research.Her current research also explores the development of advanced materials for direct air capture of carbon dioxide, focusing on moisture-swing systems that operate using changes in humidity. By studying nanoscale to macroscale structures, using different X-ray scattering and imaging techniques her team is working to design energy-efficient technologies to mitigate climate change.Professor Fromme has received numerous honors for her contributions to science, including the prestigious Anfinsen Award from the Protein Society. Through her leadership at the Biodesign Institute and her mentorship of students and researchers, she continues to drive innovation at the intersection of structural biology, energy, and environmental sustainability.

Transcript:

Fighting climate change often means building bigger machines—industrial systems that pull carbon dioxide from the air using heat, pressure or a lot of energy.But what if the air itself could do the work?My research is looking to step a big step forward in the fight against climate change; developed together with my graduate student and the first author of this published study, Gayathri Yogaganeshan and our team.Our work is on materials that can remove carbon dioxide directly from the air, using something as simple as changes in humidity. When the air is dry, the material captures CO₂. When the air becomes humid, it releases that CO₂ again. This process is called a moisture swing, and it allows us to capture carbon without using large amounts of energy, heat, or pressure.What makes this research special is that we didn’t just test whether the materials work, we looked deep inside them, all the way down to the nanoscale. Using advanced imaging techniques, we discovered that tiny structural features, thousands of times smaller than a human hair, control how water and CO₂ move through the material.We found that even small changes in humidity can subtly rearrange these structures, which in turn affects how efficiently carbon is captured and released. One of the materials we studied, a porous resin, showed particularly strong performance because its internal structure allows gases to move more easily.This work helps us understand how to design better materials, ones that are more efficient, more durable, and capable of operating on a large scale.Ultimately, our goal is to create technologies that can clean the air around us, helping to reduce atmospheric CO₂ and mitigate climate change.

Read More:

[ASU News] - Moisture-powered materials could make cleaning CO2 from air more efficient

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What motivates runners?

Emily Balcetis, associate professor of psychology at New York University, examines this question.

Faculty Bio:

Dr. Emily Balcetis is an Associate Professor of Psychology at New York University and author of Clearer, Closer, Better: How Successful People See the World. She is an award-winning behavioral scientist and author. She directs a research lab that pioneers the scientific investigation of motivation, uncovering previously unknown strategies that sustain people’s efforts to meet their goals. Dr. Balcetis focuses on issues including diversity in leadership, disparities in health, bias in the legal system, polarization in public opinion, and safety in cyber behavior, among others. She has appeared as a host for National Geographic and has served as a consultant for General Electric, Nestle, Nike, Pixar, the New York Times, Prudential Financial, Sandoz Pharmaceutical, and other industry leaders. Her research has been supported by grants from the National Science Foundation and National Institutes of Health. She has delivered two TEDx talks on motivation science and polarization in public opinion and earned her PhD from Cornell University.

Transcript:

Physical training shapes running over months and years. But what allows runners to adapt in real time? The answer is not just in their legs and lungs, it’s in their eyes. My research shows that visual attention gives runners a flexible way to support motivation and improve their performance while they’re on the move.Visual attention works like a spotlight. It can be wide, taking in what’s on the left, right, above and below us. Or attention can be narrowly focused tightly on a single point ahead. Across multiple studies, I find that runners systematically shift the scope of this attentional spotlight as they progress through a run. And when they do, they can run farther and faster.At the start of a run, people tend to use a wider scope of attention. They scan their environment, monitor others around them, and orient themselves to the course. But as physical demands increase and fatigue sets in, runners increasingly narrow their visual focus—often locking their gaze onto a small, goal-relevant target, like a sign, a bend in the road, or the finish line.That shift matters. When people are experimentally instructed to narrow their attention rather than keep it wide, they move faster, investing more effort, and experience less physical pain—even though the distance and physical demands remain unchanged. Narrowing attention appears to make the goal appear closer and more attainable, increasing perceptions of their own ability to surmount the challenges that lie ahead, and helping runners persist when effort becomes costly.Notably, more experienced and faster runners utilize this attentional narrowing strategy more than do slower runners. Faster runners focus their attention narrowly more intensely earlier on and scale up their narrowing as they progress through a run. The advantage faster runners have is not just physical conditioning—it’s also how they strategically deploy attention over time.Together, these findings suggest that visual attention is more than a passive perceptual process. It can function as a tool runners can strategically leverage. By learning when to broaden focus and when to narrow it, runners can better match their strategies to the changing demands of a goal—and sometimes, run farther and faster simply by changing where they look.

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What are the barriers to recruitment for women in law enforcement?

Amber Kerwin, professor of the practice of criminal justice at Hartwick College, looks into this question.

Faculty Bio:

Amber M. Kerwin is a professor of practice of criminal justice in the Sociology, Criminology & Social Services Department in the Division of Social and Behavioral Sciences at Hartwick College in Oneonta, New York. A former police officer and investigator, Kerwin’s areas of expertise include criminal justice, corrections, juvenile delinquency, policing, and police investigations. Among her recent courses taught: Introduction to Criminal Justice; Corrections & Punishment; Policing in a Democratic Society; and Introduction to Law. Her latest research, supported in part through the Hartwick College Faculty Research Grant Program, focuses on the barriers to recruitment and hiring of women in law enforcement.

Transcript:

Women police officers bring many positive benefits to their jobs in law enforcement. Studies have shown that women are better communicators, that they’re less likely to use physical force, and that victims of certain crimes feel more comfortable talking to them. Still, women make up a small percentage of law enforcement. Only 12 to 13 percent of total law enforcement officers in the United States currently are women. As a former police officer and investigator, I am interested in understanding why this is the case.My research focuses on the barriers to recruitment and hiring of women in law enforcement. It is important to me to identify, explore and better understand what prevents women from joining the police department or pursuing law-enforcement careers--and also understanding what makes it so hard to retain them. Through interviews with women who currently or previously worked in law enforcement, my research looks at the challenges they encounter in what remains an historically male-dominated profession. Lack of maternity- and family-related leave policies, uniforms not specifically designed for women, lack of acceptance, and harassment are all among the issues women have expressed in these conversations. Taking a closer look at the situation to gain more perspective will provide information to develop potential policy recommendations for police departments. This could help them create a more welcoming environment for women so they’re more comfortable working there. Right now, young girls and women don’t see a lot of other women in law enforcement so it’s not something that they have on their radar. A solution for that would be having more women officers do outreach to high schoolers or college students to do a more targeted recruitment effort for women.As communities across the country consider new options, this research will shed new light on the barriers to recruitment of women in law enforcement.

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Can artificial intelligence help us detect brain cancer early?

Dimitrios Mathios, assistant professor of neurosurgery at Washington University in St. Louis, takes a non-invasive look.

Faculty Bio:

Dimitrios Mathios, MD, is an assistant professor of neurosurgery and director of the Molecular Neuro-oncology Lab at WashU’s School of Medicine. His research focuses on using cancer immunology, genetics, and epigenetics to develop non-invasive early detection of brain tumors, when they are easily removable or curable with other treatments.Dr. Mathios has published more than 50 peer-reviewed articles in journals like Nature Genetics, Nature Communications, Science Translational Medicine, Cancer Discovery, the Journal of Neurosurgery, the Journal of Neuro-Oncology, and Neurosurgery. His work has resulted in numerous awards, including the Young Investigator Physician Scientist Research Award at John Hopkins University School of Medicine, where he completed his residency, in 2022, and the American Association for Cancer Research-Conquer Cancer Foundation’s ASCO Young Investigator Award for Translational Cancer Research in 2020.

Transcript:

Diagnosing brain cancer at its earliest stages, like any cancer, is critical. But there are currently no screening methods available to detect brain cancer before a patient becomes symptomatic, or when the symptoms are as commonplace as a headache or slight personality change. As a result, brain cancer often goes undetected when treatment is the easiest, most effective, and least risky.That’s why, in 2019 I began researching a way to develop a noninvasive, blood-based test for early detection of brain cancer.Medical researchers have been looking for and finding ways to detect different kinds of cancer through blood-based tests for more than 15 years. They do this by identifying markers or other abnormalities in the blood that signal the presence of cancerous cells.But brain cancer is one of the most challenging cancers to detect in the blood. This is because the blood-brain barrier, a layer of cells that protects the brain from toxins and pathogens in the blood, prevents cancer markers from entering the bloodstream in as high concentrations as in other cancers. For this reason, detection of brain tumors requires developing more sensitive approaches for biomarker detection.This year, we reached a significant milestone in our research. We found that we could use artificial intelligence to analyze DNA fragments in the bloodstream for evidence of brain cancer. With this approach, we successfully detected brain cancer in about 75% of a cohort of more than 500 patients.The next step is to conduct a larger trial to confirm our findings in a broader population. Our end goal? To create a reliable test that emergency room doctors and primary care physicians can use when patients come to them with subtle, non-specific neurologic symptoms, giving those patients a quicker diagnosis and a better chance of survival.

Read More:

[Cancer Discovery] - Detection of Brain Cancer Using Genome-wide Cell-free DNA Fragmentomes

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On University at Buffalo Week: Speeding up drug discovery will have many benefits.

Tom Grant, assistant professor in the Department of Structural Biology in the Jacobs School of Medicine and Biomedical Sciences, looks into doing so.

Faculty Bio:

Thomas Grant is a structural biologist whose research focuses on developing innovative analytical methods to uncover the structure and dynamics of biological macromolecules.

Trained at the University at Buffalo, where he earned a BS in Mathematical Physics (2007) and a PhD in Structural and Computational Biology (2013), Grant’s research combines rigorous quantitative training with experimental expertise in X-ray crystallography, small-angle scattering, and X-ray free electron laser methodologies.

His work integrates advanced computational algorithm development with cutting-edge experimental tools to generate high-resolution structural models and time-resolved “molecular movies,” illuminating how proteins and nucleic acids function within the cell and informing rational drug design.

In 2025, he was awarded a $2.18 million grant from the National Institute of General Medical Sciences, part of the National Institutes of Health, to create a new artificial intelligence-powered tool that improves scientists’ understanding of how proteins move and change shape within the human body.

Transcript:

In the United States, it often takes 10 to 15 years to develop a new drug from its initial discovery to market approval. In my lab, we’re working on an artificial intelligence-driven project called SWAXSFold that we hope will dramatically speed up this process.

So let me explain what SWAXSFold is.

A few years ago, Google DeepMind released an algorithm called AlphaFold. It has had a major impact in biomedical research, winning the 2024 Nobel Prize in Chemistry. AlphaFold predicts 3D protein structures. By identifying these structures, scientists can target them with drugs that treat disease.

But the reality is that many proteins are dynamic. They move around and can have many different shapes – that’s how they function and operate. This limits AlphaFold’s usefulness for drug discovery.

That’s where our expertise in “SWAXS” comes in. SWAXS stands for small- and wide-angle X-ray scattering.

SWAXS uses X-rays to take snapshots of proteins as they move around and change shape. It can see the different conformations a protein adopts, which is exactly what AlphaFold can’t do. So we’re developing SWAXSFold to integrate SWAXS data directly into this structure prediction process.

As you can imagine, this requires intense computational resources.

Our team is utilizing New York State’s supercomputing facility, Empire AI, to train SWAXSFold with our database of nearly a half million protein structures.

As SWAXSFold learns from AI, it will give us unparalleled insight on how proteins change their shape, function and operate. In turn, this will improve our ability to predict protein structures and, thus, make it much easier and quicker to identify drug targets that treat everything from cancer to Alzheimer’s disease.

Read More:

[UBuffalo] - Researcher’s AI-powered drug discovery tool supported by Empire AI

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On University at Buffalo Week: Can we use AI to help give those with motor neuron diseases their voices back?

Rohini Srihari, professor of computer science and engineering at the School of Engineering and Applied Science, looks to do so.

Faculty Bios:

Rohini Srihari is a scientist, educator and entrepreneur.

A professor in the Department of Computer Science and Engineering at the University at Buffalo, Srihari’s research and teaching focuses on artificial intelligence for social good, conversational AI, deep learning in natural language processing and other fields.

Her work has been funded by the National Science Foundation, the Defense Advanced Research Projects Agency, the Intelligence Advanced Research Projects Activity and other agencies. She has worked extensively with the U.S. government in developing innovative multilingual text mining solutions.

Srihari’s recent research focuses on advancing the state-of-the-art in socialbots capable of engaging in empathetic, interesting and purposeful conversations. This also involves building trustworthy socialbots for combating disinformation, assisting the disabled and other purposes.

She has published over 100 research papers in computer science journals and conference proceedings. Her Google Scholar h-index is 40. She is also the author of two U.S. patents, one on multilingual text mining.

She has served as Chief Data Scientist at PeaceTech Lab, a non-profit incubated within the United States Institute of Peace in Washington, DC. She has also founded and directed technology start-ups, focusing on “big data” analytics solutions for various markets. Two of these companies were subsequently acquired by large media analytics companies.

Srihari received her B. Math degree from the University of Waterloo (Canada) and her PhD in Computer Science from the University at Buffalo.

Transcript:

People with motor neuron diseases such as ALS and cerebral palsy often lose the ability to speak. When this happens, they use modified keyboards or eyegaze tracking systems to communicate. But to speak, they rely upon augmentative and alternative communication devices. The late physicist Stephen Hawking is an example. Typically, people can generate about five words per minute with these devices due to their reduced motor abilities.

This limits them to simple exchanges such as: “I need a glass of water.” But what these people really want is to engage in meaningful conversation.

We can help with artificial intelligence, specifically an area of AI research known as personalization of large language models.

Large language models are the same technology powering ChatGPT and other popular chatbots. To personalize them, the chatbot needs to know a lot about the user. It needs to know their own experiences. It needs to understand their personality. It possibly needs to recognize the gestures that they use when communicating.

Here’s how it works. The user is talking to someone, the other person says something, the chatbot listens to what they say, and the speech is transcribed into text that’s fed into our chatbot. The chatbot then generates three possible responses for the user. The user either selects one of those responses as, “this is what I want to say,” or they can override that and say, “no, this isn’t what I meant.” So the goal is, with minimal input from the user, to be able to generate much longer responses, but also allowing the user to steer the conversation.

We’re already testing this technology with people whose communication is limited by motor neuron diseases. And we’re running the AI models through powerful supercomputers. We’re hopeful this technology will soon be ready for widespread use, improving the lives of countless people.

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On University at Buffalo Week: Finding ways to reduce AI’s energy consumption can be crucial.

Sambandamurthy Ganapathy, professor of physics, explores how the human brain may have an answer.

Faculty Bio:

Sambandamurthy Ganapathy is a physicist who studies nanoscale materials and devices and how they relate to cutting-edge phenomena like superconductivity and resistive switching.

His experimental research group, which uses advanced nanofabrication techniques, designs and develops devices smaller than one micrometer that can explore the fundamental, microscopic mechanisms that dictate physical properties.

Ganapathy’s research examines electron transport in semiconductors and other atomic layers under ultra-low temperatures, high magnetic fields and other extreme physical conditions. It also investigates metal-insulator transitions, neuromorphic computing and superconductor-insulator transitions.

This work strives to unlock hidden quantum phenomena in novel states of matter which manifest when subatomic particles interact.

Transcript:

There’s nothing in the world as efficient as the human brain. It can store and process enormous amounts of information while using very little energy. That’s why my team and I are working on computing systems designed to mimic how the brain works.

This approach is called neuromorphic computing. It’s been around since the 1980s, but it’s gaining new attention as artificial intelligence makes computing more complex — and far more energy-hungry.

Computers and brains are already surprisingly similar. Computers have billions of transistors that switch on and off. Brains have billions of neurons that either fire signals or stay silent.

But there’s a major difference. In the brain, memory and processing happen in the same place. In traditional computers, they’re separated, so data has to constantly move back and forth. That takes a lot of energy. Neuromorphic chips aim to solve this by placing memory and processing much closer together, like the brain.

That’s where my research comes in. My team studies materials with unique quantum properties that could form the foundation of these new chips.

Neuromorphic computers won’t recreate consciousness, but they may solve problems in more human-like ways — especially when information is incomplete or uncertain.

In the near future, they’ll likely be used for specialized tasks, like self-driving cars. So don’t expect neuromorphic chips to power your smartphone anytime soon.

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On University at Buffalo Week: Inclusive design works to eliminate friction for everyone, but how do we achieve it?

Jordana Maisel, Director of Research for the Center for Inclusive Design and Environmental Access (IDEA Center) at the School of Architecture and Planning, looks into this.

Faculty Bio:

Jordana Maisel is director of the Research for the Center for Inclusive Design and Environmental Access (IDEA Center) at the University at Buffalo School of Architecture and Planning.

She is an expert in universal design and issues related to the impact of the built environment on individuals’ health, particularly for older adults and people who have disabilities.

An associate professor of urban and regional planning, Maisel also has led research in areas such as public transportation, street infrastructure, building design and accessible housing policy. A frequent theme of Maisel’s work is evaluating the impact of inclusive design implementation.

Transcript:

Ever tried to read a sign you couldn’t understand?Find a restroom that didn’t meet your needs?Navigate a space that just wasn’t built for you?

That moment of friction — that feeling of being left out — is what inclusive design works to eliminate.

Because design is power. It determines who feels welcome, who can participate, and who gets left out. Inclusive design (also known as universal design) uses that power to create spaces that work better for more people.

Many people think inclusive design is just grab bars or ramps. Those are important, but they’re actually accessibility features. Inclusive design goes further. It’s a design process that improves human performance, health and wellness, and social participation across age, ability, language, and culture.

Take, for example, the Pittsburgh International Airport, the first transportation facility in the U.S. to earn innovative solutions for universal design, or isUD, certification. Think of isUD as the inclusive design equivalent of the LEED program for sustainable buildings. Developed by our Center, isUD helps projects go beyond minimal code requirements and toward meaningful usability for everyone.

At the Pittsburgh airport that includes adult changing tables in restrooms, intuitive digital signage, and a play area designed for children of all ages and abilities.

For more than four decades, our multidisciplinary team has advanced research and real-world projects that make environments more responsive, resilient, and inclusive. Most recently, we’re investigating ways the built environment can better respond to a changing climate and how AI advances can mitigate access challenges.

The future is more diverse, more dynamic, and more complex. As our communities evolve, our spaces must evolve with them. That’s the work of inclusive design.

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On University at Buffalo Week: What can we learn from embracing historically marginalized students on campuses?

Stephen Santa Ramirez, associate professor of higher education at the Graduate School of Education, explores this.

Faculty Bio:

Stephen Santa-Ramirez is an associate professor of higher education at the University at Buffalo Graduate School of Education.

His experiences in U.S. higher education and student affairs include work in multicultural and LGBT+ affairs, residential life and housing services, and migrant student services. In addition to UB, Santa-Ramirez has taught for the Philadelphia Freedom Schools, Michigan State University, the University of Texas at Arlington, and Arizona State University.Santa-Ramirez’s personal and professional experiences in higher education – and identity as a scholar-practitioner-advocate – have played formative roles in the development of his research agenda, which centers on the lives and knowledge of historically marginalized and economically neglected students. Broadly, he investigates the historical, ideological, and structural inequities that impact Black, Latinx, Indigenous, migrant and other marginalized communities.

By employing critical, student-centered, and asset-based frameworks, he investigates campus racial climate, transitions and belongingness of first-generation students of color, college student activism and resistance, and the various ways race, ethnicity, im/migration status and policy inform the educational experiences of collegians who are undocumented.In addition to authoring a host of book chapters, some of his published peer-reviewed articles can be located in The Review of Higher Education, The Journal of Higher Education, Journal of First-generation Student Success, Journal of Diversity in Higher Education, Journal of College Student Development, among others. Additionally, he was the 2023 special issue editor for New Directions for Higher Education titled “Equitable and Humanizing Research, Policy, and Practice With and For Undocumented Collegians in the United States.”Santa-Ramirez is a 2024-2026 co-chair of the Latinx Network via ACPA-College Student Educators International, an associate editor for the College Student Affairs Journal (CSAJ), and is on the ASHE 2026 conference leadership team as a co-chair for local and community engagement.

Transcript:

Growing up in a mixed-status family, I saw early on how immigration policy creates very real inequities. Those experiences shape my work today, which centers on the lives, knowledge and resistance of historically marginalized and economically neglected students.

As an educator, I’ve worked with students who are citizens, international students and undocumented students. Over time, I began to notice how often higher education relies on one-size-fits-all approaches — and how those approaches miss the realities undocumented students navigate every day. Seeing that pushed me toward advocacy and a central idea in my research called UndocuJoy as Resistance. It’s about highlighting joy, community and brilliance as powerful responses to systems that too often deny hope. At a time when immigration policy remains uncertain, uplifting these stories really matters.

My research looks at how structural inequities shape campus climate, first-generation students’ transitions, and their sense of belonging.

Educators and institutional actors should consider the whole student – beyond the classroom – and design culturally affirming peer mentorship programs, implement policies addressing racial profiling, and ensure campus environments are inclusive of students’ cultural identities.

Ultimately, my scholarship aims to push institutions toward accountability while remaining grounded in the voices and needs of marginalized communities.

Training faculty and staff in culturally sustaining advising practices and supporting Latinx research agendas, as well as those of other marginalized groups, is crucial for promoting retention, and for fostering a sense of belonging.

Undocumented students are already part of our campuses and communities. When we take time to learn from their experiences — the hard parts and the joyful ones — we share the responsibility of support, instead of leaving students to carry it alone.

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What can protein shape tell us about Alzheimer’s disease?

John Yates, John Lytton Young Professor in the Department of Integrative Structural and Computational Biology at The Scripps Research Institute, discusses this question.

Faculty Bio:

John R. Yates III is the John Lytton Young Professor in the Department of Integrative Structural and Computational Biology at The Scripps Research Institute. He received a B.A in Zoology and an M.S. in Chemistry from the University of Maine at Orono. He obtained his Ph.D. in Chemistry at the University of Virginia in the laboratory of Donald F. Hunt with a dissertation entitled Protein Sequencing by Tandem Mass Spectrometry. He performed postdoctoral research in the laboratory of Leroy E. Hood at California Institute of Technology. At the University of Washington, he obtained the rank of Associate Professor with tenure before moving to The Scripps Research Institute in LaJolla, CA. His research interests include development of integrated methods for tandem mass spectrometry analysis of protein mixtures, bioinformatics using mass spectrometry data, and biological studies involving proteomics. He is the lead inventor of the SEQUEST software for correlating tandem mass spectrometry data to sequences in the database and developer of the shotgun proteomics technique for the analysis of protein mixtures. His laboratory has developed the use of proteomic techniques to analyze protein complexes, posttranslational modifications, organelles and quantitative analysis of protein expression for the discovery of new biology. He has received numerous awards such as the American Society for Mass Spectrometry (ASMS) research award, the Pehr Edman Award in Protein Chemistry, the ASMS Biemann Medal, the HUPO Distinguished Achievement Award in Proteomics, Herbert Sober Award from the ASBMB, and the Christian Anfinsen Award from The Protein Society, the 2015 ACS’s Analytical Chemistry award, 2015 The Ralph N. Adams Award in Bioanalytical Chemistry, the 2018 Thomson Medal from the International Mass Spectrometry Society, the 2019 John B. Fenn Distinguished Contribution to Mass Spectrometry award from the ASMS, the 2019 HUPO Award in Discovery, and the 2024 Pittsburgh Society Award in Analytical Chemistry. Dr. Yates served as an Associate Editor at Analytical Chemistry for 15 years and is currently the Editor in Chief at the Journal of Proteome Research.

Transcript:

Alzheimer’s disease is one of the most challenging medical problems today. By the time symptoms such as memory loss appear, damage in the brain has often been developing for years. That makes early detection one of the most important goals in Alzheimer’s research.A key feature of the disease is protein misfolding. Proteins are the molecular machines that carry out most of the work in our cells. For them to function properly, they must fold into precise three-dimensional shapes. In Alzheimer’s disease, some proteins lose their correct shape—a problem called proteostasis dysregulation.Our research asked a simple question: Could these structural changes in proteins be detected in the blood? If so, might they serve as early warning signs of Alzheimer’s disease?To investigate this, we analyzed blood plasma samples from more than 500 individuals, including healthy volunteers, people with mild cognitive impairment, and patients diagnosed with Alzheimer’s disease. Using mass spectrometry, we measured thousands of proteins at once and examined subtle structural changes in proteins circulating in the bloodstream.We then applied machine learning to identify patterns in these protein structures associated with Alzheimer’s disease. From this analysis, we discovered a small group of proteins whose structural changes appear to signal disease progression.In particular, peptides derived from three proteins—C1QA, clusterin, and apolipoprotein B—formed a diagnostic panel capable of distinguishing healthy individuals, people with early cognitive impairment, and those with Alzheimer’s disease with more than 80% accuracy.This work suggests that structural changes in proteins circulating in the blood may serve as conformational biomarkers for Alzheimer’s disease. Such tests could eventually help doctors detect the disease earlier, monitor its progression, and identify patients who may benefit most from emerging therapies.Detecting Alzheimer’s earlier could mean treating it earlier—and improving outcomes for millions of people worldwide.

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