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  • Foundation AI model predicts postoperative risks from clinical notes

    Foundation AI model predicts postoperative risks from clinical notes

    Millions of Americans undergo surgery each year. After surgery, preventing complications like pneumonia, blood clots and infections can be the difference between a successful recovery and a prolonged, painful hospital stay — or worse. More than 10% of surgical patients experience such complications, which can lead to longer stays in the intensive care unit (ICU), higher mortality rates and increased health care costs. Early identification of at-risk patients is crucial, but predicting these risks accurately remains a challenge.

    New advancements in artificial intelligence (AI), particularly large language models (LLMs), now offer a promising solution. A recent study led by Chenyang Lu, the Fullgraf Professor in computer science & engineering in the McKelvey School of Engineering and director of the AI for Health Institute (AIHealth) at Washington University in St. Louis, explores the potential of LLMs to predict postoperative complications by analyzing preoperative assessments and clinical notes. The work, published online Feb. 11 in npj Digital Medicine, shows that specialized LLMs can significantly outperform traditional machine learning methods in forecasting postoperative risks.

    “Surgery carries significant risks and costs, yet clinical notes hold a wealth of valuable insights from the surgical team,” Lu said. “Our large language model, tailored specifically for surgical notes, enables early and accurate prediction of postoperative complications. By identifying risks proactively, clinicians can intervene sooner, improving patient safety and outcomes.”

    Traditional risk prediction models have primarily relied on structured data, such as lab test results, patient demographics, and surgical details like procedure duration or the surgeon’s experience. While this information is undoubtedly valuable, it often lacks the nuance of a patient’s unique clinical narrative, which is captured in the detailed text of clinical notes. These notes contain personalized accounts of the patient’s medical history, current condition, and other factors that influence the likelihood of complications.

    Lu and co-first authors Charles Alba and Bing Xue, both graduate students working with Lu at the time the study was conducted, employed specialized LLMs trained on publicly available medical literature and electronic health records. They then fine-tuned the pretrained model on surgical notes to make better predictions about surgical outcomes. The resulting method — the first of its kind to process surgical notes and use them to make predictions about postoperative outcomes — can go beyond structured data to recognize patterns in the patient’s condition that might otherwise be overlooked.

    Based on nearly 85,000 surgical notes and associated patient outcomes from an academic medical center in the Midwest collected between 2018 and 2021, the team reported that their model performed far better than traditional methods in predicting complications. For every 100 patients who experienced a postoperative complication, the team’s new model correctly predicted 39 more patients who had complications than traditional natural language processing models.

    Beyond the number of patients who could potentially have surgical complications caught early and mitigated, the study also showcases the power of foundation AI models, which are designed to multitask and can be applied to a wide range of problems.

    “Foundation models can be diversified, so they’re generally more useful than specialized models. In this case, where lots of complications are possible, the model needs to be versatile enough to predict many different outcomes,” said Alba, who is also a graduate student in WashU’s Division of Computational & Data Sciences. “We fine-tuned our model for multiple tasks at same time and found that it predicts complications more accurately than models trained specifically to detect individual complications. This makes sense because complications are often correlated, so a unified foundational model benefits from shared knowledge about different outcomes and doesn’t have to be painstakingly tuned for each one.”

    “This versatile model has the potential to be deployed across various clinical settings to predict a wide range of complications,” said Joanna Abraham, associate professor of anesthesiology at WashU Medicine and a member of the Institute for Informatics (I2) at WashU Medicine. “By identifying risks early, it could become an invaluable tool for clinicians, enabling them to take proactive measures and tailor interventions to improve patient outcomes.”

    This study is supported by the Agency for Healthcare Research and Quality within the U.S. Department of Health and Human Services (R01 HS029324-02).

  • Breakthrough CRISPR-based test offers faster, more accurate diagnosis for fungal pneumonia

    Breakthrough CRISPR-based test offers faster, more accurate diagnosis for fungal pneumonia

    Tulane University researchers have developed a CRISPR-based test that diagnoses Pneumocystis jirovecii pneumonia (PJP) — a life-threatening fungal infection primarily affecting children and immunocompromised patients — more quickly and less invasively, according to a new study published in the Journal of Clinical Investigation.

    The highly accurate test detects RNA from live fungi in blood samples and throat swabs, eliminating the need for invasive bronchoscopy procedures currently used for diagnosis.

    “Current diagnostic methods haven’t evolved in decades, leaving many patients without timely or definitive answers,” said study co-author Dr. Jay Kolls, the John W. Deming Endowed Chair in Internal Medicine at Tulane University School of Medicine. “Having a non-invasive throat swab test or a blood test really allows us to get a faster, but also more specific, diagnostic than we’re able to get right now.”

    Kolls and study co-author Tony Hu, PhD, the Weatherhead Presidential Chair in Biotechnology Innovation and director of the Tulane Center for Cellular & Molecular Diagnostics, led a multidisciplinary team to develop the new tool, combining engineering innovation with clinical and biomedical expertise.

    The fungus that causes PCP rarely harms healthy people but can trigger dangerous lung infections in those with compromised immune systems, including cancer patients, transplant recipients, people with HIV/AIDS and patients on immune-suppressing drugs.

    Traditional diagnostic methods for PJP rely on a bronchoscopy in which a tube is inserted into the patient’s airways to collect samples, an expensive and invasive procedure.

    “It can take until the next day, sometimes two days, to receive bronchoscopy results,” Hu said. “With the CRISPR-based assay, we can go from samples to results in 45 minutes.”

    PCR swab tests, like those used for rapid COVID testing, often fail to detect active P. jirovecii infections. Using the CRISPR-based detection tool alongside PCR testing significantly boosted diagnostic accuracy, correctly identifying 96% of infected infants (compared to 66% with PCR alone) and 93% of infected adults (compared to just 26% with PCR alone).

    CRISPR, an acronym for “clustered regularly interspaced short palindromic repeats,” is a gene editing technology that allows scientists to precisely modify DNA. For the PJP diagnostic test, CRISPR is used to detect RNA of the fungus causing PJP.

    “That’s novel,” Kolls said. “And we have data that shows PJP may be more prevalent than thought. Better diagnosis can help us prove that.”

    In addition to diagnosing patients, the test can be used on throat swabs already gathered in clinics to improve epidemiology and mapping of pneumocystis in the United States.

    This study also shows the growing use of CRISPR as a way to streamline disease detection. Looking ahead, the research team sees potential for the CRISPR diagnostic platform to detect other respiratory infections.

    “This tool is an example of the collaborative spirit at Tulane University, where cutting edge engineering can work alongside extensive clinical practice to address a very critical issue,” Hu said. “Hopefully we can soon deploy this as a clinical test and improve outcomes for patients.”

    The tool developed at Tulane was tested in collaboration with samples from University Health Network/University of Toronto, the Pneumonia Etiology Research for Child Health (PERCH) study, and the International Mycoses Prevention, Research, Implementation, Networks and Training (IMPRINT) consortium at the University of Cape Town. The research was supported by the National Institutes of Health, the Gates Foundation, and the National Institute for Health and Care Research.

  • Researchers develop enhanced method for wastewater surveillance of antibiotic resistance

    Researchers develop enhanced method for wastewater surveillance of antibiotic resistance

    Antibiotic resistance is a global concern that threatens our ability to prevent and treat bacterial infections in humans and animals. To better monitor the emergence and spread of resistance, researchers at the Carl R. Woese Institute for Genomic Biology developed a CRISPR-enriched metagenomics method for the enhanced surveillance of antibiotic resistance genes, ARGs, in wastewater.

    While antibiotics are powerful modern tools for combatting infections, bacteria change and adapt over time in response to antibiotic exposure, therefore decreasing effectiveness. Widespread overuse and misuse of antibiotics in the healthcare and food industries further accelerate this problem.

    Beyond direct exposure to antibiotics, resistance is also passed between different bacteria through the transfer of small pieces of bacterial DNA called antibiotic resistance genes. There are over 5000 identified ARGs, and these genes can be found in clinical samples, as well as bodies of water, originating from hospitals, farms, and sewage systems.

    “ARGs can reduce the life-saving power of drugs used to treat bacterial infections,” said Helen Nguyen (IGOH), a professor of civil and environmental engineering at the University of Illinois Urbana-Champaign. “Wastewater detection of ARGs with clinical significance allows public health authorities and physicians to anticipate what is circulating in communities.”

    Wastewater contains numerous different ARGs mixed together with genetic material from various sources including humans, viruses, and bacteria. Because ARGs only make up a tiny percentage of the total DNA content, uncovering them in wastewater samples requires sensitive detection methods. The most common technique is quantitative polymerase chain reaction, qPCR. This method uses RNA guides called primers to identify the specific DNA sequences of known ARGs, which are then amplified for detection.

    “qPCR is a sensitive method that many people in public health are well trained to do, but it requires primer design and validation which is very time consuming,” said Yuqing Mao, a doctoral student in civil and environmental engineering who was first author on the paper. “Since qPCR is used to pull out targeted gene sequences, all the other genetic material in the sample remains completely unknown.”

    The second method, metagenomics, is not as sensitive as qPCR, but captures a more complete story of the genetic information contained in a sample. Metagenomics involves breaking all the sample DNA into millions of smaller fragments which are simultaneously sequenced using next generation sequencing technologies. Computational algorithms piece together the full DNA sequences for comparison against databases to determine their identities.

    “ARGs make up less than one percent — probably even closer to 0.1 % — of DNA in the sample. Using standard metagenomics methods, 99.9 % of the DNA detected is not associated with ARGs,” Mao said.

    To enrich the amount of ARG associated fragments in the samples, Mao, Nguyen, and their collaborator, Joanna Shisler, who is affiliated with the Department of Microbiology at Illinois, leveraged the CRISPR-Cas9 system — a highly effective tool for gene editing. The DNA is fragmented in random locations when using standard metagenomics methods, but incorporation of CRIPSR-Cas9 allows for targeted fragmentation within ARGs. By designing a pool of 6010 different guide RNAs that could specifically bind to DNA at different sites found in ARGs, the Cas9 protein could be directed to cut at these locations.

    “Our new CRISPR method increases the abundance of ARG fragments in the sample, which increases their chances to be read and detected. CRISPR also has better potential for multiplexed assays than something like PCR because the molecular interaction is simple and straightforward for CRISPR,” Mao said.

    Their new method lowered the detection limit of ARGs by an order of magnitude, from 10-4 to 10-5, compared to standard metagenomics and found 1189 more ARGs and 61 more ARG families which are low in abundance in wastewater samples.

    As a sixth-year graduate student, Mao built this project from the ground up — overcoming scientific obstacles and learning many new techniques along the way. She said, “The first time I got the sequencing results, I never expected how much more sensitive it would be compared to the regular method — it detected many more ARGs than we thought it would. After finishing the project, I feel like I grew up.”

    But while this work is wrapped up, Mao and Nguyen are already pursuing multiple new directions, including expanding the applications of their CRISPR-Cas9 metagenomic method to a broader range of environmental samples and using their results to guide the design of new qPCR primers.

  • Controlling conformational changes in protein aromatic side chains

    Controlling conformational changes in protein aromatic side chains

    Novel protein cage system can control and visualize orientational changes in aromatic side chains upon ligand binding, as reported by researchers at Institute of Science Tokyo. By inducing coordinated molecular changes, this approach enables precise control over protein dynamics while also enhancing fluorescence properties. Their breakthrough could lead to applications in biomolecular robotics, drug delivery, and advancing the development of responsive biomaterials.

    The dynamic nature of proteins — their ability to bend, fold, and change shape in response to their environment — underlies most aspects of cellular function. Conformational changes in proteins, which occur at the nanoscale, are fundamental to processes ranging from enzyme catalysis and chemical signaling to cell division and differentiation.

    Aromatic interactions are particularly important in protein dynamics, playing roles in protein folding, molecular recognition, and the stabilization of protein complexes. However, designing systems that can control protein dynamics at an atomic level, such as the orientation of specific aromatic side chains, has remained a significant challenge in applied chemistry and biomaterials. In turn, this has limited our ability to develop sophisticated protein-based technologies.

    Fortunately, a research team led by Professor Takafumi Ueno from Institute of Science Tokyo(Science Tokyo), Japan, has been working on innovative solutions towards this goal. In their latest study, published in Advanced Scienceon 20 February 2025, the researchers designed a protein cage system that can control and visualize the movement of multiple aromatic side chains through the strategic binding of fluorescent ligands. Their approach represents a significant breakthrough in protein engineering, offering unprecedented control over certain protein dynamics.

    The team engineered special “pockets” within a protein cage structure called ferritin, surrounding these pockets with carefully arranged clusters of aromatic amino acids. When specific fluorescent ligands bind to these pockets, they trigger what could be described as a molecular domino effect — a coordinated series of changes in the orientation of the surrounding aromatic side chains.

    Through detailed X-ray crystallography analysis, the researchers demonstrated that different ligand structures could produce different orientational changes in the aromatic side chain. The team systematically investigated this phenomenon by designing variants with different numbers and types of phenylalanine group substitutions. This approach allowed them to fine-tune the system’s response to different molecular binding events. Interestingly, when certain fluorescent molecules were isolated within these aromatic pockets, they also exhibited markedly enhanced fluorescence properties, including improved quantum yield and longer fluorescence lifetime. “Together, these findings provide an understanding of the unique molecular behavior and fluorescence properties of ligands due to the assembly of aromatic residues, and a guideline for developing dynamically controlled supramolecular biomaterials,” explains Ueno.

    The success of this approach hinged on the team’s innovative strategy of using ligand binding as a driving force to induce and propagate changes in aromatic side chains. Manipulating protein side-chain dynamics could be used to propagate molecular behavior and information across nanoscale distances, which could prove useful in a number of ways. “The expected applications of this technology include the creation of biomolecular robots responsive to external stimuli and the use of such protein systems for drug delivery, especially for compounds with poor solubility in water,” notes Ueno. The system’s ability to enhance fluorescence properties while providing controlled molecular movement could also prove particularly valuable for biosensors.

    Overall, this study opens new possibilities in medicine, biotechnology, and materials science, paving the way to responsive biomaterials that can be precisely controlled at the molecular level.

  • Genomics approach to metabolism reveals how reactions flow

    Genomics approach to metabolism reveals how reactions flow

    Two papers published today in the journal Nature describe a significant advance in understanding the complex functions of the metabolic network. The research is from the lab of Marian Walhout, PhD, the Maroun Semaan Chair in Biomedical Research and chair and professor of systems biology, which has been engaged with fundamental questions of metabolism for more than a decade.

    According to Dr. Walhout, organisms constantly monitor their nutrient intake and adjust their metabolism to generate biomass and energy; their metabolism takes place through a series of chemical reactions that together make up the metabolic network. The Walhout lab has sought to understand how these reactions work together and what happens when the normal flow of information within the network is disrupted. Do cells have the capacity to find alternate routes through the network? And how do they do this? The answers to these questions have broad implications as many diseases and health-related issues such as cancer, diabetes and obesity have root causes that stem from altered metabolism.

    While single reactions and metabolic pathways have been studied in detail, it has been painstaking to determine which are active or ‘carry flux’ in a given cell, at a given moment. The first paper published today, “Systems-level design principles of metabolic rewiring in an animal” employs a systems approach to understanding the principles whereby the animal changes the flow of metabolism when metabolic reactions are disrupted. The second paper, “A systems-level, semi-quantitative landscape of metabolic flux in C. elegans,” uses data obtained from the first paper to infer the ‘natural’ wiring of the metabolic circuit in an animal, describing which reactions are active and which are turned off in a normal state.

    Both studies were enabled by “Worm Perturb-Seq” (WPS), a novel high-throughput method used to deplete the expression of around 900 metabolic genes individually in C. elegans. WPS uses RNA sequencing to unveil how changes in different parts of the metabolic network (flux) affect gene expression and shows that gene expression can be used to learn how an organism deals with alterations in its metabolism. The data accrued from this systematic study revealed a high-level model where ‘core’ metabolic functions, when depleted, are compensated for by genes with the same core metabolic function while other core metabolic functions are repressed. What Walhout and colleagues have called a “compensation-repression,” or CR model, represents a strategy used by, in this case, the worm, to monitor the state of its metabolism and adjust it at the gene regulatory level. Fascinatingly, preliminary analysis of human data indicates that the CR model may also explain perturbations in human metabolism.

    “When we analyzed the WPS data, it became clear that it was not only instrumental to understand how the animal rewires its metabolism through the compensation-repression model, but also that we could use the data to infer, by computational modeling, how the metabolic flux runs through the network, or is ‘wired,’ in the worm in its normal state,” said Walhout. “By transforming questions about metabolism into a genomics challenge, we were able to draw a ‘wiring map’ of adult worm metabolism that revealed numerous novel insights.”

    “The most exciting part of the study for me was seeing that the majority of our predictions are validated through isotope tracing experiments,” said Hefei Zhang, PhD, a postdoctoral fellow and co-first author on both papers. “Those results indicated that our concept of using molecular phenotypes to predict metabolic network wiring at systems level is really reliable.”

    Among these insights are the use of RNA as a carbon source by the organism; the use of amino acids as an energy source to fuel the tricarboxylic acid cycle; and surprisingly, that carbohydrates such as glucose, which had previously been assumed to be the major source for energy production, have a relatively minor contribution in the worm.

    “WPS and the results presented in the two studies should provide a powerful framework for similar studies in other organisms, including humans, to provide insights into healthy metabolism, as well as different diseases where metabolism runs awry,” said Walhout.

    Xuhang Li, a PhD candidate in the Walhout lab who recently completed his doctoral work and is co-first author on both papers, added, “Together, these studies establish a new paradigm for studying metabolism through the lens of genomics, enabling a systems-level understanding of metabolic dynamics.”

  • A new model accurately predicts the movement of elite athletes to catch the ball in parabolic flight

    A new model accurately predicts the movement of elite athletes to catch the ball in parabolic flight

    How does a tennis player like Carlos Alcaraz decide where to run to return Novak Djokovic’s ball by just looking at the ball’s initial position? These behaviours, so common in elite athletes, are difficult to explain with current computational models, which assume that the players must continuously follow the ball with their eyes. Now, researchers of the University of Barcelona have developed a model that, by combining optical variables with environmental factors such as gravity, accurately predicts how a person will move to catch a moving object just from an initial glance. These results, published in the journal Royal Society Open Science, could have potential applications in fields such as robotics, sports training or even space exploration.

    The paper addresses the outfielder problem, which refers to the baseball player who stands in the outfield to catch the ball after it is hit. It is a classic challenge in physics and the neuroscience of movement, used to explore how humans and animals predict movements in a dynamic environment and how automated systems can be designed to mimic them.

    Joan López-Moliner, professor at the UB’s Faculty of Psychology and member of the Institute of Neurosciences (UBneuro), has led the research and affirms that “faced with this problem, current models are based on guiding locomotion by continuously looking at the ball, while normally the elite athlete can run towards the ball without looking at it.” “Moreover, — he adds — these models do not allow predictions of where the ball will go regarding the observer.” The initial study was part of the doctoral thesis conducted by Borja Aguado, co-author and former member of the group, who, after a stay in Darmstadt (Germany), is now a researcher at the University of Vic.

    The model integrates prior knowledge of the ball’s gravity and physical size into the visual information received in real time. “The model provides live signals that indicate the predicted position of the ball’s fall and the time remaining until it arrives, considering different gravity conditions. This makes it possible to predict precisely how a player will move to catch it, from the very beginning of the flight,” describes López-Moliner, who also coordinates the Vision and Control of Action research group.

    Despite the importance of gravity in anticipating trajectories, this is the first time this factor has been included in such a model. “This omission has overlooked the substantial influence that gravity exerts on the trajectory, which reflects a gap in the way existing models take into account environmental constants,” says the UB professor.

    Moreover, the previous models cannot explain why humans perceive whether a ball is within reach or not to decide whether to start running. “Our model does account for this, as it indicates where the object will go regarding the player,” says the researcher.

    Experiments with virtual reality

    To validate the model, the researchers conducted experiments in an immersive virtual reality environment, in which each participant — wearing goggles and holding a virtual reality device in their hand — had to move to the position where they thought a virtual ball would land. The controlled environment allowed a variety of gravity and ball size conditions to be simulated, showing that the empirical trajectories, movement patterns and temporal responses matched the model’s predictions. “Our model accurately predicts the trajectories observed in the different conditions by the participants. The results underline the importance of integrating environmental constants, such as gravity, to better understand how humans interact with the world around us,” says the researcher.

    Virtual training for athletes and astronauts

    The new model could be a basis for the future development of various practical applications, for example in sports training. “On the one hand, since the model includes several components — such as visual information or gravity — it could be applied in training or virtual simulation platforms. In this way, the degree of sensitivity of a person — such as an elite athlete — to the different components could be seen, or they could be trained to process and use the visually relevant information to optimize performance,” says López-Moliner.

    Moreover, the fact that the model can consider various types of gravity could also have applications in the aerospace sector. “The model can be applied in environments with different gravities and can potentially predict the performance with which a person — for example, the astronaut on the space station — would interact with moving objects,” says the professor.

    Research with artificial neural networks

    The researchers are already working on the next phase: implementing the model in artificial neural networks, computational systems that mimic the functioning of neurons in the human brain. Their goal is to compare the performance of humans and artificial networks. “This would allow us to have a clearer idea of how the computations are implemented at the neural level, as we now have a model at computational scale, but not the neural implementation in an artificial neural network. This knowledge could have clear applications in the field of robotics,” concludes López-Moliner.

  • New AI model measures how fast the brain ages

    New AI model measures how fast the brain ages

    A new artificial intelligence model measures how fast a patient’s brain is aging and could be a powerful new tool for understanding, preventing and treating cognitive decline and dementia, according to USC researchers.

    The first-of-its-kind tool can non-invasively track the pace of brain changes by analyzing magnetic resonance imaging (MRI) scans. Faster brain aging closely correlates with a higher risk of cognitive impairment, said Andrei Irimia, associate professor of gerontology, biomedical engineering, quantitative & computational biology and neuroscience at the USC Leonard Davis School of Gerontology and visiting associate professor of psychological medicine at King’s College London.

    “This is a novel measurement that could change the way we track brain health both in the research lab and in the clinic,” he said. “Knowing how fast one’s brain is aging can be powerful.”

    Irimia is the senior author of the study that describes the new model and its predictive power; the study was published February 24, 2025 in Proceedings of the National Academy of Sciences.

    Biological brain age versus chronological age

    Biological age is distinct from an individual’s chronological age, Irimia said. Two people who are the same age based on their birthdate can have very different biological ages due to how well their body is functioning and how “old” the body’s tissues appear to be at a cellular level.

    Some common measures of biological age use blood samples to measure epigenetic aging and DNA methylation, which influences the roles of genes in the cell. However, measuring biological age from blood samples is a poor strategy for measuring the brain’s age, Irimia explained. The barrier between the brain and the bloodstream prevents blood cells from crossing into the brain, such that a blood sample from one’s arm does not directly reflect methylation and other aging-related processes in the brain. Conversely, taking a sample directly from a patient’s brain is a much more invasive procedure, making it unfeasible to measure DNA methylation and other aspects of brain aging directly from living human brain cells.

    Previous research by Irimia and colleagues highlighted the potential of MRI scans to non-invasively measure the biological age of the brain. The earlier model used AI analysis to compare a patient’s brain anatomy to data compiled from the MRI scans of thousands of people of various ages and cognitive health outcomes.

    However, the cross-sectional nature of analyzing one MRI scan to estimate brain age had major limitations, he said. While the previous model could, for instance, tell if a patient’s brain was ten years “older” than their calendar age, it couldn’t provide info on whether that additional aging occurred earlier or later in their life, nor could it indicate whether brain aging was speeding up.

    A more accurate picture of brain aging

    A newly developed three-dimensional convolutional neural network (3D-CNN) offers a more precise way to measure how the brain ages over time. Created in collaboration with Paul Bogdan, associate professor of electrical and computer engineering and holder of the Jack Munushian Early Career Chair at the USC Viterbi School of Engineering, the model was trained and validated on more than 3,000 MRI scans of cognitively normal adults.

    Unlike traditional cross-sectional approaches, which estimate brain age from one scan at a single time point, this longitudinal method compares baseline and follow-up MRI scans from the same individual. As a result, it more accurately pinpoints neuroanatomic changes tied to accelerated or decelerated aging. The 3D-CNN also generates interpretable “saliency maps,” which indicate the specific brain regions that are most important for determining the pace of aging, Bogdan said.

    When applied to a group of 104 cognitively healthy adults and 140 Alzheimer’s disease patients, the new model’s calculations of brain aging speed closely correlated with changes in cognitive function tests given at both time points.

    “The alignment of these measures with cognitive test results indicates that the framework may serve as an early biomarker of neurocognitive decline,” Bogdan said. “Moreover, it demonstrates its applicability in both cognitively normal individuals and those with cognitive impairment.”

    He added that the model has the potential to better characterize both healthy aging and disease trajectories, and its predictive power could one day be applied to assessing which treatments would be more effective based on individual characteristics.

    “Rates of brain aging are correlated significantly with changes in cognitive function,” Irimia said. “So, if you have a high rate of brain aging, you’re more likely to have a high rate of degradation in cognitive function, including memory, executive speed, executive function, and processing speed. It’s not only an anatomic measure; the changes we see in the anatomy are associated with changes we see in the cognition of these individuals.”

    Looking ahead

    In the study, Irimia and coauthors also note how the new model was able to distinguish different rates of aging across various regions of the brain. Delving into these differences -including how they vary based on genetics, environment, and lifestyle factors — could provide insight into how different pathologies develop in the brain, Irimia said.

    The study also demonstrated that the pace of brain aging in certain regions differed between the sexes, which might shed light onto why men and women face different risks for neurodegenerative disorders, including Alzheimer’s, he added.

    Irimia said he is also excited about the potential for the new model to identify people with faster-than-normal brain aging before they show any symptoms of cognitive impairment. While new drugs targeting Alzheimer’s have been introduced, their efficacy has been less than researchers and doctors have hoped for, potentially because patients might not be starting the drug until there is already a great deal of Alzheimer’s pathology present in the brain, he explained.

    “One thing that my lab is very interested in is estimating risk for Alzheimer’s; we’d like to one day be able to say, ‘Right now, it looks like this person has a 30% risk for Alzheimer’s.’ We’re not there yet, but we’re working on it,” Irimia said. “I think this kind of measure will be very helpful to produce variables that are prognostic and can help to forecast Alzheimer’s risk. That would be really powerful, especially as we start developing potential drugs for prevention.”

  • Cell connects: Breaking barriers in stem cell communication through mRNA transfer

    Cell connects: Breaking barriers in stem cell communication through mRNA transfer

    Messenger RNA can travel between different types of stem cells through tunnel-like structures, as revealed by a new study. By studying interactions between mouse and human stem cells, they discovered that this RNA transfer can reprogram human cells to an earlier developmental state. This groundbreaking finding not only sheds light on an underexplored form of cellular communication but also suggests promising applications in regenerative medicine without using artificial genetic modifications or external chemicals.

    Cell-to-cell communication is essential throughout all forms and stages of life, and many communication mechanisms are well studied. However, over the past few years, scientists have found increasing evidence that RNA, which carries genetic information and regulates gene expression, is also involved in intercellular communication.

    One way in which messenger RNA (mRNA) transmission happens is through extracellular vesicles. Cells secrete small-sized, membranous vesicles containing biomolecules, including RNA, and these are taken up by nearby cells. A less understood mechanism of mRNA transfer involves tubular structures that form when cells come into contact. However, very few reports on this type of mRNA movement have been made, and the biological significance of this type of communication in stem cells remains mostly a mystery.

    Against this backdrop, a research team led by Professor Takanori Takebe from Institute of Science Tokyo, Japan, investigated the mechanisms and roles of mRNA transfer between different types of stem cells. Their findings were published in the journal Proceedings of the National Academy of Sciences on January 22, 2025, in Volume 122, Issue 4.

    To more easily detect mRNA trafficking from one cell to another, the researchers employed a coculture experimental system. Simply put, they cultured mouse embryonic stem cells (mESCs) alongside human primed pluripotent stem cells (hPSCs). “When we started this coculture for a different purpose, we almost serendipitously found this unexpected mRNA transfer phenomenon as we could distinguish endogenously expressed genes and laterally transferred mRNAs based on genetic sequence differences between mice and humans,” explains Takebe.

    Using this experimental system, coupled with RNA imaging analysis and mouse-specific gene expression analysis, the team revealed that mRNA from mESCs moved into hPSCs during co-culture. An in-depth analysis of this transferred mRNA revealed that mRNAs coding for molecules related to transcription, translation, and stress response were transferred from mouse cells to human cells. They also proved that this mRNA transfer occurred through tunnel-like membrane extension structures, called “tunneling nanotubes,” formed between mouse and human cells.

    Next, the researchers investigated the biological effects that this transferred mRNA had on the receiving cells. Notably, they found that primed hPSCs could be reverted to a so-called “naïve” state. In other words, the human cells reverted to an earlier embryonic stage in their differentiation. This suggests that mRNA moving between different mammalian stem cells has biologically significant effects beyond mere movement, going as far as cell fate conversion. The team also identified several key transcription factors involved in this process, with essential functions for pluripotent state maintenance.

    Taken together, the results of this study shed light on the importance of intercellular mRNA transfer. First author Yoneyama says,”This study provides insights into a novel mechanism of intercellular communication, illustrating how cell populations coordinate and coexist with their surrounding environment, thereby advancing our understanding of biological phenomena.” Takebe concludes, “We expect our findings to contribute to the development of new cell-fate control technology that does not rely on artificial gene introduction or chemical compounds.” Such technologies could lead to new therapeutic strategies in regenerative medicine and novel drugs.

    Further efforts will be needed to fully grasp the intricacies of cell-to-cell communication, and Takebe’s group is already looking forward to pursuing this exciting line of research.

  • Scientists optimize biohybrid ray development with machine learning

    Scientists optimize biohybrid ray development with machine learning

    The Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and NTT Research, Inc., a division of NTT, announced the publication of research showing an application of machine-learning directed optimization (ML-DO) that efficiently searches for high-performance design configurations in the context of biohybrid robots. Applying a machine learning approach, the researchers created mini biohybrid rays made of cardiomyocytes (heart muscle cells) and rubber with a wingspan of about 10 mm that are approximately two times more efficient at swimming than those recently developed under a conventional biomimetic approach.

    A team led by Harvard SEAS Postdoctoral Fellow John Zimmerman and including NTT Research Medical and Health Informatics Scientist Ryoma Ishii, Harvard SEAS Tarr Family Professor of Bioengineering and Applied Physics Kevin Kit Parker, and members of the Harvard SEAS Disease Biophysics Group led by Parker demonstrated this research in a new paper published in Science Robotics titled, “Bioinspired Design of a Tissue Engineered Ray with Machine Learning.”

    “This research seeks to answer a fundamental question in the development of biohybrid robots, in this case the marine ray: How do we select fin geometries to operate under novel working environments while preserving natural scaling laws in terms of swimming speed and efficiency,” said Ishii, who also works as a visiting scientist for Harvard University. “Our research indicates the application of ML-DO, inspired by protein engineering, offers a more efficient and less computationally intensive path forward in automating the creation of muscular structure-function relationships.”

    Limitations of the Biomimetic Approach

    In biomimetic design, the conventional approach to biohybrids, engineers form functional devices by recreating existing biological structures. That approach, however, has limits. For biohybrid lifeforms that resemble batoid fishes (skates and rays), for example, there is a wide range of natural aspect ratios and fin morphologies. Which ones do you mimic? Also, biomimetics may neglect the natural biomechanical and hydrodynamic forces that govern how fast an organism can swim based on its size and body kinematics, leading to inefficient muscle mass and limited swimming speeds.

    In that light, the motivating question in this study became: How to select fin geometries that operate under novel working environments while preserving natural scaling laws in terms of swimming speed and efficiency?

    The Design Breakthroughs of Machine Learning

    The multi-disciplinary and iterative nature of the problem required computationally intensive modeling, but the team believed that directed optimization by machine learning (ML-DO) would enable an efficient search for fin designs that maximized their relative swimming speeds. They based their hypothesis in part on a trial function that demonstrated an approximately 40 percent improvement of ML-DO over other leading methods in recognizing known high-rank sequences. Testing the assumption involved three steps: 1) developing an algorithm for expressing a multitude of different fin geometries; 2) describing a generalized ML-DO approach for searching within a large discontinuous configuration space; and 3) using this methodology to identify biohybrid fin geometries for high-performance swimming with smooth and orderly flow.

    The ML-DO-driven results included a quantitative exploration of fin structure-function relationships and reconstruction of general trends in open-sea batoid morphology, as well as a winning design: Fins with large aspect ratios and fine tapered tips, which preserved their utility across multiple length-scales of swimming. On that basis, the team built biohybrid mini-rays out of engineered cardiac muscle tissue, which were capable of self-propelled swimming at the millimeter length scale and demonstrated improved swimming efficiencies approximately two times greater than observed in previous biomimetic designs.

    Looking Ahead

    While promising, researchers note that additional work is needed to completely match natural scaling laws. While the devices presented in this study demonstrated greater efficiency than other recent biomimetic designs, they were still slightly less efficient on average than naturally occurring marine lifeforms.

    In the future, researchers expect to continue the development of biohybrid robotics for use cases including remote sensors, probes for dangerous working environments and as therapeutic delivery vehicles. Researchers believe that the ML-DO-informed approach better mimics the selective pressures of evolution, enabling them to better understand how biological tissues are shaped — both in a healthy physiology as well as the maladaptive pathophysiology of disease. Additionally, this research advances scientific understanding of 3D organ biofabrication, such as a biohybrid heart.

    “The Harvard Biophysics Group entered into a joint research agreement with NTT Research two years ago aiming to fundamentally interrogate our understanding of cardiac physiology, with an eye towards advancing the development of biohybrid devices, including biohybrid robotics and biohybrid human heart,” Parker said. “This paper indicates the positive progress our shared research has achieved so far, and I am excited to see what the future of our collaboration has in store.”

    In 2022, NTT Research and Harvard SEAS announced a three-year joint research agreement to engineer a model of the human heart, study fundamental laws of muscular pumps and apply joint findings to the development of a cardiovascular bio digital twin model.

  • How mitochondria drive autophagy

    How mitochondria drive autophagy

    Autophagy is the process by which cells digest and renew their internal structures, recycling damaged or malfunctioning proteins and organelles. By reusing these basic molecules, the cell gains the energy and resources needed to maintain balance. However, when autophagy is disrupted, it can lead to conditions associated with ageing, muscle loss (sarcopenia), and various metabolic diseases such as obesity and diabetes. In this context, the restoration of proper autophagy function could be a promising approach to improving health.

    A team led by Drs. Antonio Zorzano and Saska Ivanova at IRB Barcelona has published a new study in the journal Autophagy that sheds light on the connections between the MFN1 and MFN2 proteins and autophagy. The study reveals details about how the cells recycle their components to maintain optimal metabolism and provides insights into potential strategies to tackle cell ageing and diseases related to metabolic disorders.

    “The fact that these mitochondrial proteins regulate such crucial recycling processes helps us understand why their modification can lead to sarcopenia and other age-related issues,” explains Dr. Zorzano, who is also a Full Professor at the Faculty of Biology of the University of Barcelona and a member of CIBERDEM. “If we could design molecules that regulate these interactions, we would be able to prevent or reverse cell alterations related to the ageing process and metabolic disorders.”

    CRISPR-Cas9 gene editing

    One innovative aspect of this study is the methodology used: the team employed CRISPR-Cas9 gene editing to label the MFN1 and MFN2 proteins within the cell, without resorting to artificial overexpression. This approach allowed them to study mitofusins under conditions very similar to their natural state. “We labelled these proteins without altering them or overproducing them, which enabled us to better understand their true role in cellular recycling,” says Dr. Isabel Gordaliza-Alaguero, first author of the study.

    This approach allowed the researchers to collect more precise data about how MFN1 and MFN2 interactions change in response to different nutritional conditions, including extreme nutrient deprivation. Their findings provide a better understanding of the mechanisms that regulate cellular energy balance — a key pillar of healthy metabolism.

    Implications and future lines of research

    In addition to deepening our understanding of cellular metabolism, these findings pave the way for potential therapeutic applications. Regulating autophagy through specific drugs or compounds could have implications for the treatment of metabolic diseases such as obesity and type 2 diabetes, which are linked to imbalances in lipid and protein production and degradation. Furthermore, improving autophagy function has the potential to slow cellular ageing by preserving organelle quality and preventing sarcopenia and other age-related conditions.

    “The next step will be to determine the specific regions of the mitofusins that are essential for these new interactions, with the aim to design molecules that imitate or block them when necessary,” adds Dr. Ivanova. Using animal models, the team also plans to study how autophagy regulation influences long-term cellular energy balance.

    This study was performed in collaboration with the labs headed by Dr. Maria Macias (IRB Barcelona and ICREA) and Dr. Manuel Palacín (IRB Barcelona, UB and CIBERER) and was supported by funding from the ERDF, the EU’s Horizon 2020 programme, the BBVA Foundation, the Fundación Marató de TV3 and ‘la Caixa’ Foundation.