Background: Out-of-pocket (OOP) costs pose a significant barrier to participating in cancer clinical trials (CCTs). Financial reimbursement programs (FRPs) that reduce the burden of OOP costs can support participation in CCTs if the information is readily available to participants at the time of enrollment. Prior studies have shown the importance and impact of FRPs, but despite improvements, significant barriers still remain. Objective: This study was designed to explore the feasibility and acceptability of automated texts designed to offer, screen, and enroll CCT participants in an FRP for OOP travel and lodging-related clinical trial costs. Methods: This study used a mixed methods approach. Eligible participants were those who consented to participate in a breast, leukemia, or chimeric antigen receptor T cell (CAR-T) trial at the Abramson Cancer Center of the University of Pennsylvania, a National Cancer Institute comprehensive cancer center. Quantitative data were collected through engagement metrics, including text response rates and enrollment rates, as well as patient-reported satisfaction scores. Qualitative data were derived from semistructured interviews. Program enrollment rates were used to determine feasibility, whereas the engagement metrics were used to measure the acceptability of the program. Semistructured interviews were conducted with a subsample of patients who responded to at least one of the FRP texts and agreed to be interviewed to determine the barriers to and facilitators of enrolling in the Improving Patient Access to Cancer Clinical Trials (IMPACT) program via text, perceived advantages and disadvantages of the text messaging program compared to a phone call, and overall feedback on the acceptability of the automated text messaging program. Results: Quantitative data, including engagement with texts, FRP eligibility screening, and enrollment rates, were collected from all participants who successfully received a text (n=51), and qualitative data were collected from a subsample of participants who agreed to participate in a semistructured interview (n=28) about the text-based program. Participants’ mean age was 58 (SD 12) years, approximately 65% (n=33) of participants were female, 21% (n=11) of participants were Black, and 4% (n=2) of participants were Hispanic or Latino. There was high engagement with texts (n=49, 96.1%) and a high screening rate for FRP eligibility (n=33, 64.7%). Of those who successfully screened, 26 (51%) screened via text. We also saw high overall FRP enrollment rates of those who completed the texts (n=16 of 24 eligible, 66.7%) and high satisfaction (Net Promoter Score=51). The text-based platform streamlined the enrollment process, allowing one-third of patients to complete enrollment independently, without assistance from the FRP coordinator. Reported facilitators for completion of the text conversation included support from the coordinator and introduction of the FRP by CCT teams. Barriers were a lack of communication from CCT teams, patient skepticism about the legitimacy of the texts, and limited program information via text. Conclusions: Despite the small sample size and single study site, these findings suggest that automated text messaging can be an effective, low-cost, and scalable strategy to increase awareness and streamline enrollment in FRPs.
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Engineered AAVs Harness Glymphatic System to Reach Brain Targets in Mice
Crossing the blood-brain barrier and avoiding off-target effects are two important challenges for gene therapies designed to target diseases of the brain. Now, developers of a gene therapy delivery platform that pairs specially engineered adeno-associated viruses (AAVs) with a delivery strategy that harnesses the brain’s fluid transport pathways claim that their approach addresses both issues and could help pave the way to new treatments for neurological disorders like multiple sclerosis, Huntington’s disease, and rare pediatric white matter disorders.
Details of the work were published recently in a Nature Biotechnology paper titled “Efficient targeting of human glial progenitor cells in vivo with engineered AAV vectors and glymphatic delivery.” The research was done by scientists at University of Rochester Medicine and the University of Copenhagen.
While the platform can deliver therapeutic genes broadly throughout the brain, it preferentially targets human glial cells. Steve Goldman, MD, PhD, lead author of the study and co-director of the Center for Translational Neuromedicine at URochester, has spent his career studying glial cells and elucidating their role in disease progression and recovery. Previously, his lab developed human glial progenitor cell models and investigated the link between glial dysfunction and neurological disease. For example, in Huntington’s disease, his lab has shown that healthy human glial progenitor cells could outcompete and replace diseased cells in the brain.
“Over the last decade, we’ve learned that many neurological disorders involve glial dysfunction as a major driver of disease,” Goldman said. “That realization has created an urgent need for tools that can safely and efficiently deliver therapies to these cells throughout the brain.”
The current study gets scientists one step closer to that goal. Digging into the details, Goldman and his colleagues engineered a library of modified AAV5 viral vectors by making small changes to the vectors’ capsids. They then screened the vectors in mice whose brains were transplanted with human glial progenitor cells and tracked their movements to identify which ones most effectively infected the human glial progenitor cells and their descendants including astrocytes and oligodendrocytes.
“Human cells display different molecular signatures than mouse cells, and cells behave differently in the brain than they do in a dish,” Goldman explained. “By selecting vectors under biologically relevant conditions, we were able to identify candidates with a strong preference for human glia.”
Next, the team turned their efforts to studying how best to distribute the AAVs throughout the brain. For that, they turned to the glymphatic system, the brain’s network of fluid-filled pathways used to clear metabolic waste by circulating cerebrospinal fluid through the brain. They delivered the engineered AAVs into the cisterna magna, a fluid-filled compartment at the base of the brain, while using hypertonic treatment to enhance fluid uptake into the network. This approach spread the vectors broadly throughout the brain tissue while largely avoiding the blood-brain barrier, and reducing exposure to peripheral organs like the liver.
“The glymphatic system is changing the way we think about brain drug delivery,” Goldman said. “Rather than trying to force therapies across the blood-brain barrier from the bloodstream, we can use the brain’s own transport pathways to distribute them more effectively where they are needed.”
Immediate targets for this approach are pediatric lysosomal storage diseases and other inherited disorders in which glial cells lack critical enzymes. Essentially, diseases of the brain’s white matter with well-defined biological targets. Further down the road, the approach could support novel therapies for multiple sclerosis, age-related white matter loss, and Huntington’s, among other neurodegenerative disorders where glial dysfunction is involved.
“We envision a future in which vectors can be designed for specific diseases and specific cell populations,” Goldman said. His lab is already exploring whether they can use artificial intelligence to design viral capsids that have specific targeting characteristics. “This study shows that by combining targeted vector engineering with glymphatic delivery, we can begin to build that future.”
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AI Tool Speeds Biomedical Research Progress
A multi-skilled biomedical AI agent can automate biomedical research tasks, driving forward basic research into practical application.
The AI-powered “co-scientist” Biomni autonomously executes diverse research tasks, working across complex tasks across fields as diverse as genomics, immunology, pharmacology, and clinical medicine.
The large-language model (LLM), outlined in Science, mines through biomedical literature interpreting requests, both composing and executing multi-step workflows.
It can formulate hypotheses, performs complex bioinformatics analyses, and design rigorous experimental protocols, with tests showing comparable accuracy to experts while taking a fraction of the speed.
“Biomni is able to understand a simple question like, ‘Why are these patients responding differently to the drug?’” explained research Kexin Huang, who was studying for his PhD at Stanford University at the time of the research.
“Then it digs in, doing a lot of the scientific legwork.”
Initially, the researchers constructed a unified and comprehensive biomedical action space by systematically analyzing 2500 biomedical research papers spanning 25 distinct subfields, curated from literature repositories.
From this foundation, they developed an LLM-powered action discovery agent capable of reading papers and extracting key tasks, tools, and databases essential to driving biomedical discoveries.
These elements are then chosen and developed into Biomni-E1, the foundational environment that defines the biomedical action space for agentic interaction and includes 150 specialized biomedical tools, 105 software packages, and 59 databases.
The team then designed Biomni-A1, a general-purpose agent architecture capable of flexibly executing a broad spectrum of biomedical tasks by using tools and datasets provided by Biomni E1.
After a user query is entered, the agent uses a retrieval system to identify the most relevant tools, databases, and software needed.
It then applies LLM-based reasoning and domain expertise to generate a detailed, step-by-step plan. Each step is expressed through executable code, enabling precise and flexible compositions of biomedical actions—an essential 10 feature given the domain’s reliance on highly specialized tools and data resources.
This integrated system allows Biomni to efficiently generate solutions for challenging, large-scale biomedical problems, but also to generalize to tasks across previously unseen areas of biomedical research.
In this way it removes the laborious work from biomedical science, allowing researchers to focus on creating hypotheses and innovative experiments and collaborating across disciplines.
“The hurdle in biomedical science is not intelligence or ideas; it is mechanics,” said researcher Jure Leskovec, PhD, also at Stanford.
“It’s this laborious stuff that slows innovation. Biomni can do this work in minutes.”
The team tested Biomni’s practical capabilities through five case studies: analyzing wearable sensor data; performing comprehensive bioinformatics analyses on massive raw datasets such as single-cell RNA-seq and ATAC-seq data; designing laboratory protocols to assist wet-lab researchers; optimizing a protein sequence for better thermostability; and orchestrating robotics wet-lab instruments.
In one test, more than 450 files of real-world continuous glucose monitoring, food intake, and physical activity data from a single person was uploaded and Biomni asked to find interesting and plausible hypotheses.
The researchers asked a simple question: “Analyze this data, find interesting and plausible hypotheses.” In just 40 minutes, the AI-agent identified patterns relating food intake and body temperature that would have taken an estimated 60 or more hours for a human to complete.
“With Biomni, we introduce a scalable, general-purpose biomedical AI agent, pointing toward a future in which AI agents work alongside human researchers to accelerate biomedical discovery from basic research to translation,” the authors concluded.
A prototype of the AI agent is already being used by more than 10,000 labs in academia and industry, making it the most widely used AI co-scientist system in biomedicine.
The post AI Tool Speeds Biomedical Research Progress appeared first on Inside Precision Medicine.
Clinicians Trust Faulty AI Recommendations Over Experience
Reliance of artificial intelligence (AI) has been increasing across all fields, including in the clinic. How to ensure AI is integrated practically, effectively, and ethically has been the focus of many discussions and debates, however final consensus almost always lands on the idea that however AI is integrated into clinical use, conscientious human oversight is necessary.
To test the veracity of this goal, a team of researchers in Spain put over 200 medical doctors to the test to determine if the clinicians would trust their experience and training over AI recommendations.
“It is important to investigate the errors that humans (including doctors) make when working with algorithms, in order to learn how to minimize the problems that arise from them,” said co-author of the study, Fernando Blanco, PhD, researcher at the Mind, Brain and Behavior Research Center (CIMCYC) in Granada, Spain.
“We wanted to see if professional physicians would act differently so that they would notice and correct these errors,” the authors wrote in their paper published in PLOS Digital Health.
The researchers created treatment plan options for a series of fictitious patients with a rare disease. They asked 223 physician participants whether or not to provide a treatment to a patient based on whether the patient was classified by AI as being highly or lowly sensitive to the treatment plan. The physicians were then presented with patient recovery data and asked to rate their perception on how reliable the AI classification was.
In these experiments, both groups of patients responded to treatment with similar sensitivity, resulting in ineffective AI recommendations that could be identified using the patient recovery data.
“In the first experiment, the treatment worked moderately (and equally) well for both groups. In the second, the treatment did not work at all for either group,” the authors wrote. They expected that “in both experiments, participants would administer the treatment less often to the fictitious patients classified as lowly sensitive to the treatment, consistently with the AI classification.” However, this was not the case.
“In both experiments, physicians mostly trusted the AI’s classifications and had trouble learning from the feedback,” said lead author Aranzazu Vinas, PhD, University of the Basque Country, Spain. “Furthermore, in the second experiment, professionals did not notice that the treatment was completely ineffective.”
These results present a major concern for the medical community in its use of AI in the clinic. While AI can be highly effective and useful for data collection and summary, supervision and critical thinking are still required for effective and safe patient care. This study highlights the need for physicians to take the time to critically consider all available data, regardless of recommendations by AI in their diagnostic and treatment decisions.
“People tend to say that there is always a human controlling the algorithm,” opined Helena Matute, PhD, professor, University of Deusto, Spain on the team’s findings “but our experiments show that doctors (as well as anyone else) have problems in learning from the available evidence when it contradicts the suggestions of an algorithm.”
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Blended Genome–Exome Sequencing Slashes Costs Without Quality Loss
A novel sequencing strategy that combines low-pass whole-genome sequencing with deep whole-exome sequencing in a single assay could significantly lower the cost of large-scale genomic studies without sacrificing analytical performance, according to a study published in Nature Genetics. The approach, known as blended genome–exome (BGE) sequencing, may help accelerate precision medicine initiatives by making comprehensive genomic profiling more accessible across diverse populations.
Researchers from Massachusetts General Hospital and the Broad Institute of MIT and Harvard developed BGE to overcome a persistent tradeoff in human genomics. High-coverage whole-genome sequencing offers the most comprehensive view of genetic variation but remains prohibitively expensive for many population studies. Genotyping arrays and exome sequencing are more affordable but either miss large portions of the genome or introduce bias by relying on variants selected primarily from European ancestry populations.
The BGE workflow integrates low-pass whole-genome sequencing at 1–4x coverage with deep exome sequencing at 30–40x coverage within a single library preparation and sequencing run on Illumina’s NovaSeqS4. The result is a unified dataset capable of supporting genome-wide association studies, rare variant discovery, copy number variant (CNV) detection, and polygenic analyses at approximately 28% of the cost of conventional 30x whole-genome sequencing.
The investigators validated the approach in more than 53,000 participants enrolled in the Populations Underrepresented in Mental Illness Associations Studies (PUMAS) Project, which includes African, African American, Hispanic/Latino, and Colombian cohorts. The scale and diversity of the study allowed the researchers to assess performance in populations that have historically been underrepresented in genomic research.
Imputed genotypes generated from BGE showed excellent agreement with Illumina Global Screening Array data, achieving concordance exceeding 95% for variants with minor allele frequencies above 1%. Importantly, performance remained consistent across multiple ancestry groups and local ancestry backgrounds, addressing one of the major limitations of conventional array-based genotyping.
The platform also demonstrated strong performance for clinically relevant structural variation. Using established computational pipelines, investigators achieved approximately 90% positive predictive value for protein-coding CNVs spanning three or more exons compared with deep whole-genome sequencing. In benchmarking studies, the method successfully detected all validated de novo coding CNVs in a reference autism cohort while maintaining low false-positive rates.
Beyond analytical performance, the study highlights potential operational advantages. By combining genome and exome sequencing into a single workflow, BGE simplifies laboratory processing, reduces the need for multiple assays, and minimizes sample attrition between sequencing platforms. These efficiencies could prove valuable for national biobanks, health system sequencing programs, and pharmaceutical research efforts that increasingly require genomic datasets from hundreds of thousands of participants.
The technology may also advance equity in precision medicine. Because low-pass genome sequencing does not depend on predefined variant content, it avoids many of the ascertainment biases associated with traditional genotyping arrays. The authors found that BGE captured substantially more coding and noncoding variants than array-based approaches while maintaining high-quality rare variant detection through deep exome coverage.
The researchers acknowledge that imputation performance remains influenced by the diversity of available reference panels, particularly for Indigenous American ancestry. However, as more globally representative reference datasets become available, they expect the accuracy of low-pass genome imputation to improve further.
As precision medicine increasingly depends on large, ancestrally diverse genomic datasets, technologies that balance cost, scalability, and comprehensive variant detection will be essential. BGE sequencing offers a practical alternative to deep whole-genome sequencing, enabling broader participation in genomic discovery while preserving much of the analytical power needed to identify clinically meaningful genetic variation.
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Mobile AI Tool Expands Access to Prenatal Ultrasonography
A portable AI tool can estimate the age of an unborn baby from scans as well as trained sonographers, potentially extending access to vital prenatal ultrasonography where it might not otherwise be available.
The findings, in JAMA Network Open, reveal the potential of artificial intelligence to expand access to diagnostic tasks such as ultrasonography using novice operators in low-resource settings.
An AI model trained on blind sweep ultrasonography scans performed as well as traditional sonographers on estimating gestational age when used by novice operators across diverse geographical and infrastructural contexts, with minimal fine tuning.
“This system could mitigate disproportionate maternal or fetal comorbidity in low-resource areas by providing access to an essential clinical tool, serving as a template for automating and democratizing other ultrasonography-based diagnostics in future obstetric work,” reported Ryan Gomes, PhD, from Google in California, and co-workers.
Determining gestational age is a fundamental component of prenatal care, allowing obstetric care that can include life-saving interventions to prevent pre- and post-term births and manage high-risk conditions.
But while traditional ultrasonography is recommended by the World Health Organization, it requires skilled sonographers and expensive equipment that may not be available, particularly in low-resource settings.
AI using low-cost portable devices offer a potentially accessibly alternative, with blind sweep ultrasonography—a set of protocolized sweeps that does not rely on real-time imaging interpretation—emerging as a particularly promising approach.
However, clinical sites vary significantly in workflow, staffing, patient demographics, and equipment, which could affect the accuracy of AI-based assessment.
Gomes and team therefore examined the value of an AI tool to estimate gestational age from blind sweep ultrasonography scans across a variety of settings.
The AI-based system was originally trained on data from suburban North Carolina and urban Zambia and validated in a Chicago urban academic center and a Kenyan urban clinic using a different portable probe.
The broader cohort included 2043 participants—consisting of 1008 in Chicago and 1035 in Nairobi.
Fine-tuning using 180 examinations from 120 Chicago participants—approximately 6% of original training size, split evenly for training and validation—targeted generalization to new hardware and gestational-age distributions.
The primary evaluation set of 385 participants—192 in Chicago and 193 in Nairobi—had gestational ages from 16 to 36 weeks.
The researchers found that the AI model effectively generalized to new clinical environments and institutions, achieving a mean absolute error of 4.2 days that was noninferior to the clinical standard.
Its robust performance in Nairobi, with a mean absolute error of 4.3 days without local-tuning mirrored results in Chicago, where this mean error was 4.1 days and underscored the model’s inherent adaptability and transfer-learning efficacy.
These mean absolute errors with the adapted model were similar to the standard of care.
“The lower sweep rejection rate in the Nairobi setting (1.8% vs 7.9% in Chicago) may suggest that approximately six hours of formal, interactive, hands-on training improves acquisition quality compared with informal and written instruction,” the authors noted.
Nonetheless, they conclude overall: “This generalizable accuracy, achieved with low-cost probes, represents an important step toward World Health Organization–recommended scalable prenatal implementation.”
The post Mobile AI Tool Expands Access to Prenatal Ultrasonography appeared first on Inside Precision Medicine.
Outbreak of diarrhea-causing parasite grows to more than 1,000 cases
NEW YORK — Nearly 1,000 people in Michigan have been diagnosed with a parasitic infection that can cause weeks of watery diarrhea, making it the largest such outbreak in state history and one of the nation’s largest in years.
No deaths have been reported and the source of the cyclospora infections hasn’t been identified. Meanwhile, investigations into similar illnesses have been going on in 28 other states, including in Ohio, where people just across the Michigan border are also becoming sick.
STAT+: ARPA-H launches $160 million effort to develop custom gene editing drugs
ARPA-H, the U.S.’ “moonshot” agency for health research, announced Thursday that it will spend up to $160 million to push forward custom gene editing treatments for a spate of rare diseases.
The program, called THRIVE, will back seven different teams pursuing various groups of conditions affecting different organ systems.
Each team has a deadline of starting clinical trials by year three of the program, although some may start much sooner.
STAT+: FDA finalists under review
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A father fights to create and then save his daughter’s gene therapy, the White House nears a decision on an FDA chief, and the Trump administration pushes drugmakers to reshore generics manufacturing.
A quest to save Grace — and help rare disease patients everywhere
With $70 million and seemingly every fiber of his being, Matt Wilsey built a gene therapy company from scratch — recruiting Nobel laureates and biotech veterans to will an experimental treatment into existence for his daughter Grace. The treatment, for the ultra-rare disorder NGLY1 deficiency, landed her back in the hospital before she slowly began to recover, STAT’s Jason Mast and Matt Herper write.