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Good morning. You know that one Emily Dickinson poem? “Because I could not stop for Death – / He kindly stopped for me – / The Carriage held but just Ourselves – / And Immortality.” I’d love to hear a longevity enthusiast’s close reading. In the meantime, scroll down for a great story from Sarah Todd on the movement’s latest death-defying initiatives.
Researchers at the University of Ottawa has found that a heart attack may trigger neurological disorders and cognitive decline, and have identified the reactive molecule methylglyoxal as a potential driver of neuroinflammation after myocardial infarction. The research, published in the journal Advanced Science, adds to a growing body of evidence that injury to the heart can directly alter brain function through the “heart-brain axis.”
“Methylglyoxal has been widely studied for its role in metabolic diseases, including diabetes, but much less is known about its function in other diseases. In a previous study, we discovered that methylglyoxal was produced by dying heart tissue after a heart attack. Based on this evidence, we predicted that methylglyoxal in the blood would target other organs and tissues, including the brain—and this is what we did indeed observe,” said senior author Erik Suuronen, PhD, a professor in the department of surgery and director of the BEaTs research program at the University of Ottawa Heart Institute.
The researchers found that methylglyoxal-derived advanced glycation end products (MG-AGEs) accumulated in the brains of mice within hours after myocardial infarction and remained elevated seven days later. This accumulation resulted in neuroinflammation, activation of microglia and macrophages, increased inflammatory signaling, and disruption of the blood-brain barrier.
The study examined five brain regions in female and male mice after they induced myocardial infarction. Brain tissue was collected at six hours and seven days after the cardiac event. Researchers measured levels of MG-H1, the major MG-derived advanced glycation end product, along with glyoxalase-1, inflammatory cytokines, receptors for AGEs, activated microglia, and macrophages.
The work builds on a multiple earlier research studies linking cardiovascular disease and neurological disorders. Prior clinical studies have found that myocardial infarction and heart failure are associated with increased risks of dementia, depression, anxiety, and cognitive impairment. Patients with depression after myocardial infarction also face a greater risk of future cardiovascular events.
The heart-brain axis is the bidirectional relationship between cardiac and neurological function similar to the gut-brain axis. Changes in heart function can influence the brain, while neurological disease can also affect cardiovascular health. While the biological factors relating to this interaction are still emerging, the new research indicates that methylglyoxal may be one factor contributing to this relationship.
MG is produced as a byproduct of glycolysis and can react with proteins, lipids, and DNA, contributing to cellular dysfunction. Conditions associated with myocardial infarction, such as ischemia, inflammation, oxidative stress, and metabolic shifts toward anaerobic glycolysis, promote MG production while limiting its detoxification.
The mouse studies showed that the brainstem had the highest accumulation of MG-H1 after myocardial infarction, followed by the cortex. These same regions also showed elevated expression of inflammatory markers and receptors associated with MG-mediated signaling. Male mice exhibited higher MG-AGE accumulation and greater neuroinflammation than females in several brain regions.
The team also documented increases in inflammatory markers including NF-κB, TNF-α, and activated microglia across multiple brain regions. The cortex and brainstem demonstrated the highest inflammatory responses. According to the authors, the findings suggest a “region-specific vulnerability to glycation-induced stress post-MI.”
The findings could have implications for better understanding how heart attacks contribute to long-term neurological disease risk, including dementia and Alzheimer’s disease. The researchers noted that MG-AGEs already have recognized roles in neurodegenerative disease and mental health disorders.
The researchers have already used the information from the study to develop a peptide designed to trap methylglyoxal and prevent cellular damage, as a potential new therapy. The peptide will be tested for its ability to protect the brain after myocardial infarction.
“Given the increased risk of subsequent heart attacks or death in heart attack patients who experience depression or anxiety, being able to alleviate these conditions could reduce subsequent major cardiac events and improve the lives of countless patients, filling an urgent unmet clinical need,” Suuronen says.
The researchers said future work will examine how MG-driven inflammation contributes to neuron death and behavioral changes after myocardial infarction. They also plan further research to better understand the glyoxalase detoxification system, including glyoxalase-1 activity and glutathione levels.
The post Heart Attack Byproduct Linked to Brain Inflammation and Cognitive Decline appeared first on Inside Precision Medicine.
Researchers at University of California, San Diego have developed a new artificial intelligence (AI) model that can translate a tumor’s complex genetic profile into predictions about how that cancer may respond to treatment. The foundation model, called MutationProjector, was trained on genomic data from more than 30,000 tumors across 10 solid cancer types, and validated through testing across multiple independent patient cohorts. Led by Trey Ideker, PhD, professor of medicine at UC San Diego School of Medicine and director of the Big Data Institute at the University of Oxford, the researchers say the model offers a new framework for connecting cancer mutations to the biological pathways that drive treatment response.
“Genetic sequencing is already routine in cancer care, but we still struggle to fully interpret the many mutations found in a patient’s tumor,” said Ideker, who also holds a second appointment at UC San Diego Jacobs School of Engineering and is a member of UC San Diego Moores Cancer Center. “Our goal with MutationProjector was to build a general-purpose model that can learn from tens of thousands of tumor genomes and turn those mutation patterns into more precise predictions about treatment response.”
Ideker is co-senior and co-corresponding author of the team’s published paper in Cancer Discovery, titled “A foundation model of cancer genotype enables precise predictions of therapeutic response,” in which the authors stated, “These results establish a unifying framework for connecting tumor genotypes to biological mechanisms and therapeutic outcomes.”
Following a cancer diagnosis, one of the next steps is often genetic testing, which helps doctors classify the tumor and decide which treatments to pursue. “DNA sequencing panels—and in particular those that broadly identify alterations in cancer-associated genes—have been widely adopted in the clinic due to their relatively low cost, rapid turnaround, and established relevance to treatment outcomes,” the authors explained.
![Trey Ideker is a professor of medicine at UC San Diego School of Medicine and director of the Big Data Institute at the University of Oxford. [Erik Jepsen / UC San Diego]](https://www.genengnews.com/wp-content/uploads/2026/05/Low-Res_TreyIdekerLab-ErikJepsen-800px-300x218.jpg)
However, while genetic testing is relatively low cost, fast, and has a strong track record in cases where validated genetic biomarkers are available, those cases remain limited, because this type of treatment stratification is currently based on only a small number of known biomarkers. Today, only about 8% of cases are successfully matched to an FDA-approved therapy on the basis of genetics and usually on the basis of a single gene, the team continued. “While this situation may reflect the incomplete scope of genes covered by current sequencing panels, it clearly also reflects a fundamental lack of knowledge about how gene mutations should be interpreted.”
They suggest that an “average” tumor has approximately 11 distinct genetic alterations identified by clinical sequencing, representing a potentially rich source of molecular information, if this information could be used to help select treatment. One of the challenges to matching cancer mutations with treatment outcomes is that most mutations are rare, the investigators pointed out. Another is that individual biomarkers do not function in isolation, but act together to influence drug response.
Unlike existing approaches that rely on a small number of biomarkers, MutationProjector analyzes the broader combination of genetic alterations present in a tumor. The model then uses this information to generate a compact representation of the tumor’s biological state, helping researchers interpret which molecular pathways may be disrupted and, by extension, which treatments may be most effective. “Foundation models, which are pre-trained on large datasets and then applied to solve diverse new challenges with relatively few samples, are especially well positioned to advance precision oncology,” Ideker and team noted.
The investigators trained their foundational model, MutationProjector, using genetic profile data from more than 30,000 tumors samples across different cancer types. They then showed that across several independent cohorts of cancer patients, including those with bladder cancer, lung cancer and melanoma, MutationProjector matched or exceeded existing methods for predicting response to common immunotherapy and chemotherapy treatments. The model also identified both known and unexpected biomarkers associated with treatment outcomes, which could help improve current approaches to genetic testing and patient stratification.
“When applied to predict immunotherapy or chemotherapy resistance across multiple cancer types and cohorts, MutationProjector achieves or exceeds state-of-the-art performance in all contexts,” the team wrote. “It identifies unexpected biomarkers, including KMT2D mutation in immunotherapy sensitivity and joint alteration of SMARCA4 and STK11 in immunotherapy resistance.”
JungHo Kong, PhD, first author of the study and a postdoctoral researcher in the department of medicine at UC San Diego School of Medicine, said, “Many cancer mutations are individually rare, which makes them difficult to study one at a time. By pretraining on a large collection of tumors and integrating molecular network knowledge, MutationProjector can detect patterns that would be easy to miss with conventional biomarker approaches. That gives us a way to move from long lists of mutations toward a more functional understanding of the tumor.”
![First study author JungHo Kong, shown here, is a postdoctoral researcher at UC San Diego School of Medicine. [UC San Diego Health Sciences]](https://www.genengnews.com/wp-content/uploads/2026/05/Low-Res_IMG_8731-1024x768-1-300x225.jpg)
The researchers emphasize that the model was designed not only to make predictions, but also to provide insight into why those predictions are made, which could help when refining biomarkers and treatment strategies. This interpretability is especially important in precision oncology, where clinicians need to understand how tumor genotypes relate to treatment decisions.
The team also hopes to expand the model to additional cancer types and data sources, including international cancer genome datasets and other forms of clinical information, such as imaging, transcriptomics, and electronic health records. “While 30,000+ genomes representing 10 solid tumor types were considered in our study, numerous additional tumor samples are available for expansion of MutationProjector to tumor types such as pancreatic cancer, prostate cancer or sarcomas,” the authors said. “Other future studies should explore the extent to which the MutationProject concept can be applied to further clinical tasks of interest, including application to liquid biopsies of circulating tumor DNAs for early cancer detection.”
Ideker added, “Our results suggest that tumor genome foundation models may help extend the clinical value of sequencing beyond a handful of well-known genes. This could support a more comprehensive and biologically grounded approach to precision oncology.”
The post AI Model Predicts Cancer Treatment Response from Tumor Genotype appeared first on GEN – Genetic Engineering and Biotechnology News.
Research findings help explain why symptoms present so differently from one child to the next, and why individualized supports and interventions are essential.
Autism can look very different from person to person. One child might differ from another in how they learn, process sensory information, and experience social and communication challenges. Scientists have long suspected these differences stem from distinct biology, but proving it has been challenging — until now.
A recent study published in Nature Neuroscience has identified two biological subtypes of autism linked to different pathways in the brain.
Researchers from the Child Mind Institute, the Istituto Italiano di Tecnologia, and other international partners analyzed brain connection patterns in nearly 2,000 individuals, including 940 autistic people from the Autism Brain Imaging Data Exchange (ABIDE). By combining human brain-imaging datasets with complementary biological data, they identified two consistent patterns in how different brain regions communicate.
One subtype showed reduced communication, or hypoconnectivity, among brain regions linked to pathways that help brain cells send signals to one another. The other showed increased communication, or hyperconnectivity, among brain regions linked to pathways associated with the immune system. The two subtypes exhibited differences in functional brain structure and modest differences on standardized autism assessments, with the hyperconnectivity subtype scoring moderately higher on autism severity measures.
These findings give scientists the first empirically biology-based framework for understanding autism’s complexities over time. This type of work could move the field closer to more precise, personalized approaches to medicine and care. However, this does not mean autism can now be divided into just two categories, nor does it create a new diagnostic framework. Autism is complex, and these two subtypes are likely part of a much larger picture.
The study also highlights the importance of open science. Through shared datasets like ABIDE, researchers can tackle questions too large for a single lab to answer alone.
The post New Study Identifies Different Biological Subtypes of Autism appeared first on Child Mind Institute.
Background: Adolescent asthma is a significant contributor to youth morbidity and is known to be best managed through consistent medication use and symptom management. However, adolescents often struggle to perceive their symptoms accurately and consistently use their medication at the recommended rate, risking worsened symptoms and impaired quality of life. The Responsive Asthma Care for Teens (ReACT) system is a project aimed at identifying and and providing supporting for several barriers adolescents may face in asthma management. By integrating both software and hardware to monitor medication adherence, ReACT provides a personalized support plan to improve asthma management and, subsequently, quality of life. Objective: The objective of this study is to conduct a proof-of-concept assessment of the ReACT system following an initial pilot study and adjusting for the feedback received. In addition to assessing the acceptability and usability of the current version, this study aims to assess whether the proposed ReACT system leads to indications of improvement in medication adherence because of the personalized support plans. Methods: Participants in the study were 5 adolescents aged 15 to 17 years recruited using a combination of consent-to-contact forms delivered via an in-person asthma clinic and Qualtrics panels. As a part of this study, participants met with the study team 3 times over 1 month. After completing initial surveys on stress, problem-solving, and asthma-related quality of life, we oriented the participants to the ReACT platform and asked them to interact with it as normal. After the month, the participants were interviewed, and they discussed the system and completed surveys assessing their opinions on acceptability and usability. Results: On a 4-point scale, participants reported high acceptability of ReACT (mean score 3, SD 0.32), willingness to use it again (mean score 4, SD 0.89), and willingness to recommend it to a friend (mean score 3.75, SD 0.55), and they considered it to be helpful (mean score 3.2, SD 0.84). Conclusions: Our findings suggest that ReACT is an acceptable and usable mobile health intervention to improve asthma self-management among adolescents, and it had promising results for improving self-regulation, problem-solving, and asthma control. The system continues improving based on feedback from a larger sample size of participants, and we hope that ReACT will aid adolescent development while delivering highly personalized support for each user.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/a599c5f704bb69fa9dc2f629b00493f3" />
In Uganda’s fishing villages along lakes and rivers, wearable GPS devices are offering a new, detailed picture of how schistosomiasis spreads, helping refine control strategies for a disease affecting about 250 million people globally, mostly in rural sub-Saharan Africa.
A Nature Health study by researchers at the University of Oxford’s Big Data Institute shows that simple models using GPS-tracked movement can accurately predict which open-water sites people use, how often they visit them, and which sites are most likely to drive transmission of Schistosoma mansoni, the parasite that causes schistosomiasis.
Schistosomiasis, or “snail fever,” is caused by Schistosoma mansoni flatworms that parasitize freshwater snails. Infection occurs when people contact contaminated water where larval forms of the flatworm penetrate the skin. Repeated exposure leads to reinfection and chronic disease, including liver damage, portal hypertension, bladder fibrosis, kidney damage, and increased cancer risk.
While praziquantel can cure infection, mass drug administration (MDA) with the antiparasitic medication has failed to interrupt transmission of the parasite. The World Health Organization (WHO) notes that MDA alone is insufficient, since transmission persists in localized hotspots. Focal interventions are needed, but researchers have limited them due to poor data on where water contact actually occurs.
Conventional assumptions have limited modeling of human contact with open-water sites. Though open-water contact is heterogeneous within villages and households, it is often assumed that people use only the site closest to their household or village. However, how mobility affects site usage patterns and whether assignment rules beyond nearest distance can be more realistic are unclear.
Along with praziquantel MDA, the 2022 WHO schistosomiasis control guidelines recommend safe water, sanitation, and hygiene (WASH) as the main intervention, but evidence is mixed. Lack of reliable data has made it difficult to determine why the intervention did not improve biannual MDA. Most water contact and WASH studies use self-reported data or household distance to sites and taps, which lacks objective, spatially granular data to characterize fine-scale water usage patterns and quantify WASH’s impact on water contact and (re)infection.
Lead author Fabian Reitzug, PhD, and colleagues from Goylette F. Chami’s lab tracked 452 people using GPS loggers in three Ugandan districts for 10 days. A total of 8,200 water contact events occurred at 69 open-water sites and 32 improved sources like taps and boreholes—deep drilling to groundwater. Of the participants, 63.9% used open water and 33.2% improved sources.
Reitzug and colleagues found, unsurprisingly, that distance strongly predicted behavior: usage dropped sharply with distance. Open-water contact was ~70% at 20 meters from home and 11% at 500 meters. Nearly all tap and borehole use occurred within 1 km of home, and over 99.5% of open-water contact occurred within 3 km, showing highly localized exposure.
Adding mobility metrics (such as “radius of gyration”) did not improve predictions. This simple finding challenges the assumption that mobile phone tracking can reliably estimate infection risk. Schistosomiasis exposure appears to be caused by local, routine movements, not long-distance travel.
The study found little evidence that safe water infrastructure reduces risky water contact. Taps and boreholes rarely replaced open-water use; fewer than 2% of people fully substituted safe water for natural sources. Daily activities like bathing, fishing, and washing still require lake or river contact. Behavior varied by location. In the Western Ugandan district of Buliisa, nearly 90% visited open water, compared with 44% in the Eastern Ugandan district of Mayuge.
When incorporated into transmission models, GPS-informed movement patterns closely reproduced observed reinfection rates. Simple “nearest-water-source” assumptions overestimated risk. The improved model also identified high-risk water sites by combining human use with ecological suitability for snail habitats.
These findings suggest control programs could shift toward targeted interventions at key transmission sites, such as focal snail control, environmental modification, or localized treatment. The study also indicates that a 1 km intervention radius may be more realistic than the current 500 m guideline. Importantly, reliable spatial patterns emerged from as few as 15 participants over 10 days, suggesting the approach is scalable. Key limitations include using proximity as a proxy for water contact and limited seasonal coverage.
Overall, the study reframes schistosomiasis transmission as a highly local, measurable process, enabling more precise, data-driven control strategies. Future research should examine whether similar models apply to other waterborne diseases. Identifying pathogen-specific exposure pathways and collecting GPS logger data from various locations could test this approach’s generalizability.
The post GPS Tracking Upends Rural Uganda’s Schistosomiasis Transmission Assumptions appeared first on Inside Precision Medicine.
Nature Medicine, Published online: 26 May 2026; doi:10.1038/s41591-026-04401-x
Biobanks are introducing trusted research environments to enable secure data access, yet they can increase costs and complexity for biomedical researchers who use them. We discuss challenges and solutions and introduce the Python Phenofhy community tool for Our Future Health.
Nature Medicine, Published online: 26 May 2026; doi:10.1038/s41591-026-04376-9
In a multicenter phase 2 trial, the fibroblast growth factor receptor inhibitor rogaratinib showed encouraging clinical efficacy in patients with succinate dehydrogenase-deficient gastrointestinal stromal tumors, suggesting a potential new treatment option for this patient population and demonstrating that an epigenetic mechanism of oncogene activation can be successfully targeted with a tyrosine kinase inhibitor.
WASHINGTON — People in the food world didn’t know what to expect when the Trump administration appointed a little-known Florida attorney as the FDA’s top food official in 2025.
They knew Kyle Diamantas worked at Jones Day representing food, beverage, and tobacco-industry clients. They saw the picture of him and Donald Trump Jr. holding giant, dead wild turkeys after a hunt. He had no experience in public health, in medicine or science, or in government.
The credentials didn’t scream qualified. And Diamantas was stepping into a center rocked by DOGE layoffs and a defiant resignation by former leader Jim Jones.