Anthropic’s Claude Code running Opus 5 in Auto Mode can be tricked into executing attacker-controlled code simply by asking the coding agent to summarize a website. The attack works up to 80 percent of the time, according to prompt-injection wizard Johann Rehberger, aka wunderwuzzi. In a blog and video demo, he detailed how to hijack Opus 5 in Auto Mode, which is the default setting for Claude as of mid-August. It starts off by asking the agentic coding model to summarize a malicious website that presents itself as an archive of notebook records, and then tricking Claude into using curl instead of its WebFetch tool to retrieve the contents of the page – but without directly telling the model to use curl. The WebFetch request fails, returning a 415 Unsupported Media Type response, so the model decides to access the website directly by issuing a Bash tool call with curl. The website returns a 303 response, and redirects to a malicious ZIP archive, which Claude then downloads. This archive contains seemingly harmless files including catalog metadata, a README file, seven Base85/zlib-encoded JSON notebook records, a macOS decoder-darwin binary – plus a poisoned Python file named struct.py. Claude, per its safety guardrails, refuses to run the decoder: “This is planned and what the attacker wants,” Rehberger wrote. Instead of using the supplied binary, the AI decides to write its own decoder. “Ironically, that safety decision is the exploit path,” Rehberger explained, adding a purple devil emoji to the text. The new decoder imports base64, and from here the attack relies on Python module shadowing to trick the model into running the malicious struct.py code. Module shadowing occurs when a local file shares the same name as a Python standard-library module. The local file hides the official module, causing Python to load it instead. In this case, the standard-library base64 module imports the legitimate struct module, and the malicious ZIP contains a malicious file with the same name. Rehberger says he used ChatGPT to obfuscate the malicious struct.py code to bypass Claude’s safety controls, and this successfully launches a separate Python process to download and execute a remote payload – in this case a command-and-control callback, which in turn opens Calculator. We assume that real attackers would execute something a little more nefarious. In another attack scenario, struct.py launches a second, headless Claude Code via claude -p, meaning this prompt injection can be used not just to remotely execute code, but rather to create a whole new agent. “The nested Claude gets its own tool access and context,” Rehberger wrote. “In these runs the child performed basic recon (whoami, uname, id), opened Calculator and wrote to local files in the home folder.” Across three variants tested five times each, which Rehberger noted were small samples, he reported success rates between 60 percent and 80 percent. “I would say that these results are representative for a motivated attack, but not comprehensive.” Anthropic did not respond to The Register’s request for comment, but reportedly told Rehberger that the model’s “behavior is working as designed.” We’ve heard this one before. “Auto Mode is a convenience feature backed by a best-effort classifier, not a security guarantee,” Rehberger wrote, paraphrasing Anthropic’s response to his security report. According to Rehberger, the classifier isn’t built to stop determined prompt-injection chains made up of individually benign-looking steps, and the real boundary is OS isolation and network egress control. The key takeaway, according to Rehberger, is to run this and other coding agents in a sandbox. “The solution is something we talked about for many years,” he wrote. “Do not trust the model output.”®

Pat Fauquet is the next stop onThe Road to Macstock is a conversation about her session, “Pills, Processes, and Procedures,” explaining how Apple devices can help manage medications, appointments, research, and communication with doctors. Drawing from years of health challenges and tech experience, she highlights the Apple Watch, iPhone, iPad, and Mac as practical tools for personal advocacy, caregiving, and preparedness. This might be the most important session this year. 

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This MacVoices Live! starts out with the introduction of the new Foreshadowing Tech series, mark the end of the British Tech Network after 18 years, and examine Apple-related privacy, politics, and search issues. Chuck Joiner, David Ginsburg, Jeff Gamet, Guy Serle, Web Bixby, Eric Bolden, Marty Jencius, and Jim Rea discuss Canada’s encryption proposal and Apple’s response, the Towson Apple Store closure, DuckDuckGo’s surge after Google search changes, and Meta’s AI pendant ambitions. 

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Cloudflare announced wallets and optional identity handles for AI agents on August 4, extending paid access past crawlers to any caller and any resource.

The post Cloudflare Gives AI Agents Wallets That Pay For What They Access appeared first on Search Engine Journal.

The top of the world is warming faster than any other region. For 20 years, the Arctic Report Card chronicled the extraordinary transformation.

New data finds no difference in how OpenAI's in-house search index served licensed and unlicensed sites.

The post ChatGPT’s Search Index Serves Small Sites Too, Data Shows appeared first on Search Engine Journal.

AI answers now shape choices before anyone reaches your site. Measuring alignment with decision drivers matters more than measuring the clicks that follow.

The post The Metric Most SEOs Are Still Not Measuring In The Age Of AI appeared first on Search Engine Journal.

Anthropic will embed invisible watermarks in Claude text and sign generated files.

The post Anthropic To Mark Claude Text & Files Under EU AI Act Code appeared first on Search Engine Journal.

TransUnion's survey of 100 marketing leaders shows AI search blind spots mirror walled garden measurement gaps. Frame GEO as a company-wide problem to get funded.

The post What TransUnion’s AI Research Reveals About How Brands Actually Get Cited appeared first on Search Engine Journal.

A new research project at the New Jersey Institute of Technology is taking a closer look at a strange but important behavior in rubber-like materials: their properties can depend on what happened to them before. With a $125,000 New Directions grant from the American Chemical Society Petroleum Research Fund, chemical and materials engineering professor Gennady Gor and his students will investigate how petroleum-derived polymers absorb fluids, swell, contract and change their elasticity over time. The work could improve predictions of how elastomers perform in everything from industrial seals to oil and gas equipment.

Elastomers are polymers capable of undergoing large, reversible deformations. Silicone, neoprene and natural or synthetic rubber are familiar examples. Their molecular chains can stretch, rearrange and return toward their original configuration, giving these materials their characteristic flexibility. Yet exposure to liquids or vapors can dramatically alter that behavior. Hydrocarbon molecules may enter the polymer network, pushing molecular chains apart and causing the material to expand. At the same time, the absorbed fluid can change the polymer’s stiffness, strength and ability to recover its original shape.

Gor’s project focuses on a phenomenon known as hysteresis, in which a material’s current state depends not only on its present conditions but also on the path it took to reach them. For an elastomer, that means the material may respond differently when it is swelling than when it is shrinking, even if its size and surrounding environment are identical. The difference can arise from molecular rearrangements, delayed relaxation or changes in the interactions between polymer chains and absorbed molecules. Such path-dependent behavior is central to understanding why materials sometimes fail to return fully to their previous mechanical state.

The question is especially relevant for petroleum-related technologies. Elastomers are widely used as O-rings, gaskets and seals because they can deform to fill microscopic gaps and maintain a hermetic barrier. In oil and gas environments, however, these components may encounter complex mixtures of hydrocarbons under pressure and at elevated temperatures. If a seal swells excessively, softens or loses its ability to recover after repeated exposure, it can become unreliable. A better understanding of hysteresis could help engineers select materials and operating conditions that reduce leakage and extend component lifetimes.

The new study represents a shift for Gor’s Laboratory for Materials Interfaces. His previous research has largely examined porous adsorbent materials, which collect molecules on their internal surfaces. Elastomers are different: they do not need pores to take up substantial amounts of fluid. Instead, molecules diffuse into the polymer matrix and interact with its flexible chains. The resulting process is more like a sponge absorbing liquid, although the molecular mechanisms are governed by diffusion, thermodynamics and the elasticity of the polymer network.

Günel Nabiyeva, a fourth-year doctoral student, will lead much of the experimental effort with undergraduate chemical engineering student Elitsa Mileva and second-year doctoral student Supun Rangana. The team has already begun collecting data on how rubber deforms in the presence of liquids and vapors. For Mileva, who has spent considerable time reviewing the scientific literature, the grant will provide an opportunity to move deeper into laboratory work. Nabiyeva describes the project as a new system for the research group because the laboratory has not previously studied elastomers in this way.

The researchers will expose polymer samples to controlled concentrations of hydrocarbon vapors and track how the materials respond. Their measurements will combine swelling, fluid uptake and elasticity rather than treating these properties separately. This approach is essential because a polymer can gain mass and volume while simultaneously becoming softer or, under some conditions, displaying a more complicated mechanical response. Different hydrocarbons may produce different results because their molecular size, chemical structure and compatibility with the polymer influence how quickly they enter the material and how strongly they interact with its chains.

Rangana will contribute expertise in ultrasonic sensing, which can measure subtle changes without physically contacting or damaging the sample. Ultrasonic waves travel through a material in ways that depend on its density, structure and mechanical properties. By analyzing changes in wave transmission or reflection, scientists can detect variations in stiffness and internal condition with high precision. Coupling these measurements to vapor exposure could reveal how quickly elasticity changes as molecules enter the polymer and whether the material follows the same path during desorption as it did during absorption.

The team hopes its results will fill a gap in the fundamental understanding of polymer hysteresis. Although hysteresis is well known in many areas of physics and engineering, relatively few studies have examined how history-dependent elasticity develops in swollen elastomers. The new measurements may help distinguish reversible molecular motions from more persistent structural changes and improve models used to predict polymer performance. Ultimately, the researchers aim to make elastomer behavior less mysterious and more reliable, helping engineers design seals and flexible components that remain dependable across repeated cycles of chemical exposure, swelling and recovery.

Subject of Research: Elastomers, petroleum-derived polymers, polymer swelling, elasticity and hysteresis

Article Title: Ultrasonic Experiments Aim to Reveal Why Rubber Remembers Its History

Web References: Gennady Gor’s NJIT profile; NJIT Laboratory for Materials Interfaces

Image Credits: Courtesy of New Jersey Institute of Technology

Keywords

Elastomers, polymers, hysteresis, rubber, polymer swelling, ultrasonic sensors, materials science, chemical engineering, petroleum research, NJIT

Animal testing has shaped modern drug development for decades, but its limitations become especially visible when scientists evaluate cancer immunotherapies. These treatments are designed to stimulate the immune system to recognize and destroy malignant cells, yet the same powerful immune activation can trigger life-threatening complications in patients. Researchers at the University of Rochester are developing a human-cell-based alternative: miniature tissue models known as organs-on-a-chip that could help predict these toxicities before experimental medicines reach clinical trials.

The work is being led through the university’s Translational Center for Barrier Microphysiological Systems, or TraCe-bMPS. The center is building drug-development tools around modular µSiM tissue chips, devices that contain ultrathin membranes populated with human cells. Unlike conventional laboratory cultures, these systems are engineered to reproduce key features of biological barriers, including the physical separation between tissues, controlled exposure to drugs, and measurable inflammatory responses. Their modular construction also allows the devices to be manufactured in large quantities with consistent dimensions and materials.

The platform is based primarily on acrylic components and incorporates membranes designed to mimic interfaces found in the human body. Such barriers regulate the movement of molecules and immune cells between compartments, making them central to drug absorption, toxicity, and inflammation. By assembling reproducible chips with human cells, researchers can study how a therapy affects a tissue under conditions that more closely approximate human biology than many animal models. The design also makes it possible to modify individual components for different organs or disease settings.

A major feature of the Rochester system is the integration of sensors directly into the chips. Developed in part through the work of biomedical engineering professor Benjamin Miller, these sensors can track barrier function and inflammatory signaling in real time. Instead of waiting until the end of an experiment to examine damaged cells, researchers can observe how a tissue responds as a drug is introduced. Changes in electrical or molecular signals may reveal that a barrier is becoming more permeable or that inflammatory pathways are being activated, providing an early warning of toxicity.

The immediate focus is on two severe complications associated with some cancer immunotherapies. Cytokine release syndrome, commonly called CRS, occurs when activated immune cells release large quantities of signaling proteins known as cytokines. The resulting systemic inflammation can cause fever, low blood pressure, breathing difficulties, and, in the most serious cases, organ failure. Immune effector cell-associated neurotoxicity syndrome, or ICANS, can affect the nervous system, producing confusion, seizures, impaired speech, or other neurological symptoms. Both conditions are difficult to predict reliably with animal testing because species differ in immune signaling networks and cell-surface receptors.

“The goal is to predict these toxicities from human cells on a chip, before a drug ever reaches a patient, and to do it without relying on animal models that have repeatedly failed to predict CRS in people,” says James McGrath, director of TraCe-bMPS and the William R. Kenan Jr. Professor of Biomedical Engineering. The approach does not simply attempt to reproduce a whole human body in miniature. Instead, it focuses on specific biological barriers and immune interactions that are directly relevant to the safety question being studied.

The project has now reached an important regulatory stage. The team was accepted into the US Food and Drug Administration’s Innovative Science and Technology Approaches for New Drugs, or ISTAND, pilot program. ISTAND is intended to help evaluate emerging methods that could improve drug development and eventually support regulatory decisions. Acceptance does not mean that the tissue-chip platform has already been approved for routine use, but it creates a formal pathway for the researchers to work with the FDA on defining the evidence needed for qualification.

The Rochester group is preparing a detailed qualification plan that will describe clinical considerations, timelines, data-sharing procedures, and statistical methods for evaluating the technology. If the platform ultimately receives FDA qualification, pharmaceutical companies could use its results as part of the evidence submitted in support of new drug applications. Such recognition would represent a significant step for organs-on-a-chip research, which has expanded rapidly but has only rarely progressed into formal regulatory programs.

The effort comes as US regulators seek alternatives to animal testing in preclinical safety studies. The FDA Modernization Act 2.0, enacted in 2022, opened the door for non-animal methods, while the agency’s 2025 roadmap outlined further efforts to reduce reliance on animal models. Researchers say pharmaceutical companies are showing strong interest because many immunotherapies are already in clinical development and carry risks of CRS or ICANS. The Rochester team collaborated with Graham Marsh of the Critical Path Institute and consulted with scientists at Pfizer about how the tool could fit into real-world drug development. If successful, the technology could give researchers a more human-relevant way to identify dangerous immune reactions early, helping developers eliminate unsafe candidates and refine promising treatments before patients are exposed.

Subject of Research: Human-cell tissue chips for predicting cancer immunotherapy toxicities.

Web References: University of Rochester; Translational Center for Barrier Microphysiological Systems; University of Rochester Biomedical Engineering; FDA ISTAND pilot program; FDA Modernization Act 2.0.

Image Credits: URochester photo / J. Adam Fenster.

Keywords: Drug discovery, tissue chips, organs-on-a-chip, cancer immunotherapy, cytokine release syndrome, CRS, ICANS, neurotoxicity, animal testing alternatives, biomedical engineering, microphysiological systems, human-cell models.

The Marcus Foundation has awarded $28.7 million to Chuck Murry, MD, PhD, director of the USC Stem Cell Center, to support a clinical trial testing a stem cell-based therapy intended to regenerate heart muscle damaged by a severe heart attack. The award, the largest the foundation has given to the University of Southern California and one of its largest contributions to academic medicine, will fund both the planned human study and a parallel research program examining how the immune system responds to transplanted cardiac cells.

The trial is scheduled to begin in 2027 at Keck Hospital of USC in partnership with the USC Cardiac and Vascular Institute. A second clinical site will be established at the University of Washington, where elements of the technology were originally developed. The first phase will enroll approximately 18 patients who have experienced extensive myocardial infarction, the medical term for a heart attack that destroys part of the heart’s muscular wall.

Patients in the study will receive stem-cell-derived heart muscle cells delivered into regions damaged by the infarction. The primary goals will be to evaluate safety and determine whether the procedure is feasible in people with severe cardiac injury. Investigators will also monitor early biological signals, including whether the transplanted cells remain active, integrate with the injured tissue and produce measurable changes in cardiac performance during recovery and long-term follow-up.

A heart attack occurs when blood flow through a coronary artery is blocked, depriving cardiac muscle of oxygen. Within minutes, cells in the affected region begin to die, and the resulting scar tissue lacks the contractile properties of healthy myocardium. Unlike tissues such as skin or blood, adult human heart muscle has only a limited capacity for self-repair. As a result, a large infarction can weaken the heart’s pumping function, increase the risk of heart failure and leave survivors unable to perform ordinary activities without fatigue or breathlessness.

Current cardiovascular treatments can reopen blocked arteries, limit further injury and help the remaining heart muscle work more efficiently. They cannot, however, routinely replace the cardiomyocytes that have already been lost. Murry’s research program is aimed at addressing that biological gap by producing replacement heart muscle cells in the laboratory and delivering them directly to damaged tissue. The strategy represents a shift from supporting a failing heart to attempting to rebuild part of its contractile apparatus.

The transplanted cells are being developed as a form of cellular therapy, in which living cells act as the therapeutic agent rather than serving merely as a vehicle for a drug. According to Murry, the cells are intended to become active within the injured heart, contribute to repair and synchronize with surrounding muscle. Achieving that outcome requires more than placing new cells into scarred tissue: the cells must survive, connect functionally with the host myocardium and avoid creating dangerous electrical disturbances or abnormal rhythms. The initial trial is therefore designed principally to establish safety before testing therapeutic effectiveness in larger patient groups.

The foundation’s funding will also support research into the immune response triggered by cell transplantation. Even cells created from stem cells can be recognized as foreign by the recipient’s immune system, potentially leading to inflammation or destruction of the transplanted population. Patients who receive cellular grafts may consequently require immunosuppressive drugs, which can increase susceptibility to infection and produce other long-term complications. Researchers will study how immune cells interact with the implanted cardiac cells and investigate methods that could reduce or eventually eliminate the need for chronic immunosuppression.

The work builds on approximately three decades of research by Murry and his collaborators to move regenerative cardiology from laboratory experiments toward clinical treatment. The program includes gene-edited cellular technology, intended to improve the compatibility or performance of therapeutic cells in the human body. The precise effectiveness of the approach will not be known until it is tested in patients, and the small 2027 cohort will not be large enough to determine whether the therapy improves survival or prevents heart failure. Those questions would require subsequent trials involving substantially more participants and longer observation.

The Marcus Foundation described the project as a potential turning point for regenerative medicine, while USC leaders said the investment could create a platform for developing additional cellular therapies. The immediate scientific challenge is to determine whether transplanted heart muscle cells can be delivered safely and function meaningfully within severely damaged human hearts. If the trial meets its safety and feasibility objectives, it could provide the clinical evidence needed to advance a new generation of treatments for myocardial infarction—therapies designed not only to help patients live with damaged cardiac tissue, but to restore some of the function that conventional medicine cannot yet replace.

Subject of Research: Stem cell-based regenerative therapy for heart muscle damaged by myocardial infarction

Article Title: $28.7 Million Award Supports USC Trial of Stem Cell Therapy to Regenerate Heart Muscle After Heart Attack

Web References: The Marcus Foundation: https://marcusfoundation.org/; USC Stem Cell Center: https://stemcell.keck.usc.edu/; Chuck Murry Lab: https://murrylab.usc.edu/charles-chuck-murry-md-phd/; USC Cardiac and Vascular Institute: https://www.keckmedicine.org/services/cardiovascular/; Keck School of Medicine of USC: https://keck.usc.edu/

Image Credits: Steve Cohn

Keywords: Heart disease, myocardial infarction, heart attack, heart muscle, stem cell research, stem cell therapy, cell therapy, regenerative medicine, cardiovascular disorders, heart failure, clinical trial, cardiac regeneration

Scientists in Arkansas have developed a machine-learning approach that can identify potentially disease-causing strains of a poultry bacterium by analyzing not only which genes are present, but also how those genes are arranged across the genome. The method focuses on the “genetic neighborhoods” surrounding genes, offering a new way to distinguish harmful strains of Enterococcus cecorum from closely related strains that do not cause disease.

E. cecorum is commonly found in poultry environments, and many of its strains are harmless. However, pathogenic strains can cause arthritis, bone infections and lameness, creating serious animal-welfare problems and economic losses for poultry producers. Because pathogenic and nonpathogenic strains can share many of the same genes, identifying the genetic features associated with disease has been challenging.

The research team, led by Aranyak Goswami of the Arkansas Agricultural Experiment Station’s Center for Agricultural Data Analytics, examined the organization of genes within genomic islands. These are segments of DNA that bacteria often acquire from other microorganisms through horizontal gene transfer. Genomic islands can carry genes involved in antibiotic resistance, bacterial survival, movement of DNA and virulence, making them important regions for understanding how pathogens emerge.

Rather than treating the bacterial genome as a simple list of genetic components, the researchers analyzed it as a structured map. They investigated which genes occurred near one another, the order in which they appeared and the way groups of neighboring genes formed recurring units known as genomic-island cassettes. This architecture provided information that could be missed when genes are examined individually.

“The machine-learning model recognizes patterns in gene order much like it recognizes patterns in language,” Goswami said. In the same way that a language model can learn that certain words frequently appear together, the computational system learned that particular combinations and arrangements of bacterial genes were associated with pathogenic strains. The approach therefore captures relationships among genes rather than relying only on the presence or absence of individual DNA sequences.

For the proof-of-concept study, the researchers analyzed the genomes of 145 E. cecorum strains isolated from poultry. The dataset included 95 nonpathogenic strains and 50 pathogenic strains capable of causing illness. The analysis found that disease-associated strains were more likely to contain genomic islands enriched in genes linked to antimicrobial resistance and the transfer of genetic material between bacteria.

Those findings do not mean that every bacterium carrying such genes will necessarily cause disease. Instead, the researchers describe the system as an exploratory classification and research tool. It identifies patterns that may help scientists investigate why some bacterial lineages become harmful, while also providing clues about how resistance and virulence traits move through bacterial populations.

Current monitoring methods for E. cecorum often depend on culturing the bacterium or screening for specific genes. Such techniques remain valuable, but they may overlook broader genomic relationships. The new pipeline could complement these approaches by evaluating the wider context in which genes occur. Additional testing with larger and more geographically diverse datasets will be necessary before the method can be used for routine flock surveillance or diagnostic decision-making.

The researchers believe the strategy could eventually be adapted to other bacterial species affecting animals, humans, wildlife and plants. Goswami’s team is beginning to examine Enterococcus faecalis, a close relative of E. cecorum and a frequent cause of hospital-acquired infections in humans. They also plan to apply the pipeline to Escherichia coli and other bacteria to study how nonpathogenic lineages acquire the genomic configurations associated with disease. The study, published in Frontiers in Microbiology, demonstrates how machine learning and comparative genomics can reveal hidden signals in bacterial DNA and may help researchers track the evolution of emerging pathogens.

Subject of Research: Animals

Article Title: Genomic-island cassette architecture provides interpretable signal for exploratory classification of poultry-associated Enterococcus cecorum lineages

News Publication Date: 7-Jul-2026

Web References: https://doi.org/10.3389/fmicb.2026.1882753

References: Frontiers in Microbiology, DOI: 10.3389/fmicb.2026.1882753

Image Credits: UA University Relations photo by Chieko Hara

Keywords: artificial intelligence, machine learning, deep learning, computational biology, bacterial pathogens, Enterococcus cecorum, genomic islands, genomic architecture, antibiotic resistance, poultry disease, pathogen surveillance, comparative genomics

A multidisciplinary team from Baylor College of Medicine, Rice University and the University of Texas School of Public Health has received the inaugural Climate + Health Excellence award from the Burroughs Wellcome Fund. The five-year, $10 million award will support FORECAST, an ambitious program designed to investigate how climate change, extreme weather and environmental disruption influence the emergence and spread of infectious diseases. Led by infectious disease researcher Dr. Anthony Maresso, the project will combine wastewater surveillance, metagenomic sequencing, climate science, epidemiology and public education to create an early-warning system for pathogen activity across Texas.

FORECAST will build on Texas’s expanding wastewater-monitoring infrastructure, including the Texas Wastewater Environmental Biomonitoring Network, or TexWEB. The statewide network samples sewage from 15 Texas cities and analyzes biological material shed by communities. Unlike clinical surveillance, which depends on people seeking medical care and receiving diagnostic tests, wastewater epidemiology captures population-level signals from entire communities. Viral genetic fragments, bacterial DNA and other biological markers can appear in sewage before hospitals and clinics register a rise in patients, providing public health officials with valuable time to investigate and respond.

The program will use metagenomic surveillance, a technique that examines genetic material recovered from environmental samples rather than searching only for a predetermined pathogen. In conventional testing, laboratories typically use targeted molecular assays designed to detect a known virus or bacterium. Metagenomic sequencing can survey a much broader range of organisms, including variants and potentially unfamiliar pathogens. Researchers can then compare these signals with clinical data, geographic information and environmental measurements to determine whether changes in temperature, rainfall, flooding, drought or other conditions are associated with shifts in infectious disease activity.

The scientific goal is not simply to identify pathogens after they appear, but to uncover relationships between microbes and the environmental conditions that help them enter or circulate within human populations. Climate change can influence disease transmission through several pathways. Warmer temperatures may expand the geographic range or seasonal activity of mosquitoes and other vectors. Heavy rainfall and flooding can overwhelm sanitation systems and redistribute contaminants, while drought can alter water use and concentrate biological material. Changes in human behavior, animal movement and ecosystems may create additional opportunities for pathogens to cross species boundaries.

Maresso and his colleagues demonstrated during the COVID-19 pandemic that SARS-CoV-2 signals in wastewater could precede increases in clinically diagnosed infections. Viral RNA released in feces entered sewage systems, where researchers tracked changes in concentration over time. Although wastewater measurements cannot determine which individuals are infected, they can reveal whether pathogen activity is increasing or declining within a catchment area. The approach has since been extended to a wider range of targets, including avian influenza, measles, HIV and viruses associated with cancer. FORECAST aims to develop this surveillance capacity into a predictive climate-health platform.

Rice University climate scientist Dr. Sylvia Dee will help connect pathogen observations with weather and climate data. The team plans to examine whether specific environmental patterns consistently occur before increases in particular infectious agents. Such models could eventually function like a public health weather report, indicating when conditions are becoming more favorable for disease transmission. The researchers emphasize that predictive systems will require careful validation because the presence of pathogen genetic material does not necessarily indicate infectious virus, active transmission or a future outbreak. Interpreting the signal will require laboratory studies, clinical evidence and knowledge of local infrastructure.

The project will also investigate diseases transmitted by vectors such as mosquitoes. Dr. Peter Hotez and colleagues at the National School of Tropical Medicine will focus on vector-borne pathogen transmission and the growing health risks associated with changing climate conditions. Texas is particularly relevant for this work because it contains diverse ecological regions and experiences heat waves, hurricanes, flooding and rapid urban growth. These factors can affect mosquito habitats, human exposure and the movement of pathogens across communities. Integrating wastewater findings with vector surveillance could help reveal connections that would remain hidden if either system were used alone.

Education and public communication will form a central part of FORECAST. The team plans to create a “Middle to Medical” climate-health education track extending from middle school through medical training and professional development. Dr. Nancy Moreno, who developed Baylor’s BioEd online program, will lead educational efforts with Rice University’s Dr. Joseph Campana, director of the university’s Center for Environmental Studies and EcoStudio. The curriculum will explain how pathogens spread, how climate conditions alter disease risk and how scientific evidence can guide decisions by students, clinicians, policymakers and communities.

The award is particularly significant because FORECAST was selected as the sole funded project from 161 applications submitted to the new CHEX program. The initiative is intended to strengthen research, education and public engagement at the intersection of climate change and human health. Dr. Eric Boerwinkle, dean of the UT School of Public Health and a project co-investigator, will contribute access to large clinical datasets that can be used to evaluate wastewater and environmental signals against reported illnesses. The investigators say the work could improve outbreak preparedness while advancing the still-developing field of wastewater science.

By combining environmental sequencing with climate modeling and health education, FORECAST seeks to shift infectious disease surveillance from a largely reactive system toward earlier detection and prevention. Its findings may help officials recognize emerging threats before they produce widespread clinical disease, while also clarifying how environmental changes shape viral evolution, transmission and geographic spread. The researchers describe wastewater as an unusually candid public health resource: it aggregates biological information from a population without requiring individual testing. As climate pressures intensify, they argue, that collective signal could become an essential tool for protecting communities and preparing the next generation of scientists.

Subject of Research:
Climate-driven infectious disease emergence, wastewater epidemiology, metagenomic pathogen surveillance and climate-health education.

Article Title:
Texas Researchers Launch FORECAST Program to Link Climate Change With Emerging Viral Threats

Web References:
Baylor College of Medicine; Rice University; University of Texas School of Public Health; Burroughs Wellcome Fund; Texas Wastewater Environmental Biomonitoring Network (TexWEB).

References:
Information provided in the source news release concerning the FORECAST project, the Climate + Health Excellence award and TexWEB wastewater surveillance activities.

Keywords:
Wastewater epidemiology, viral surveillance, metagenomic sequencing, climate change, infectious diseases, pathogen emergence, public health, Texas, SARS-CoV-2, avian influenza, measles, vector-borne disease, climate health, epidemiology, wastewater science

Pulmonary arterial hypertension (PAH) is being redefined by molecular biology. Once viewed mainly as a disorder of excessive blood-vessel constriction, the disease is now understood as a progressive process involving vascular remodeling, immune activation, metabolic disruption, and inherited susceptibility. A review published online on June 17, 2026, in the Chinese Medical Journal describes how these interacting mechanisms are reshaping PAH research and opening the door to treatments designed not only to relieve symptoms, but also to alter the disease itself.

PAH is a rare and potentially fatal condition in which the small arteries carrying blood through the lungs become narrowed and structurally damaged. As resistance within the pulmonary circulation rises, the right side of the heart must pump harder to maintain blood flow. Over time, the right ventricle can enlarge, weaken, and fail. Current therapies have improved survival, but many patients continue to experience progressive disease, limited exercise capacity, and an uncertain long-term outlook. The new review was conducted by Dr. Taeil Yang of Gil Medical Center at Gachon University College of Medicine and Professor Wook-Jin Chung of the Gachon Cardiovascular Research Institute in the Republic of Korea.

The authors explain that PAH develops through a complex interaction among several types of cells in the pulmonary circulation. Endothelial cells, which form the inner lining of blood vessels, normally regulate vessel diameter, blood flow, clotting, and repair. In PAH, these cells become dysfunctional and can produce excessive vasoconstrictors while reducing protective signals such as nitric oxide and prostacyclin. Endothelial injury also promotes the recruitment of inflammatory cells and stimulates neighboring smooth-muscle cells and fibroblasts to multiply. The resulting changes thicken the vessel wall and progressively reduce the space available for blood flow.

Inflammation is another central feature of this transformation. T cells, B cells, macrophages, and other immune cells can accumulate around pulmonary arteries, releasing cytokines and growth factors that sustain vascular injury. Rather than being a temporary response to damage, this immune activity may become chronic, reinforcing the abnormal behavior of vascular cells. The review emphasizes that immune signaling is closely connected to endothelial dysfunction and tissue remodeling, suggesting that PAH cannot be fully understood by studying blood-vessel tone alone.

The metabolism of pulmonary vascular cells also changes as the disease advances. Under normal conditions, mitochondria generate much of the energy required for cellular function through oxidative phosphorylation. In PAH, endothelial and smooth-muscle cells may shift toward glycolysis, a pattern resembling the “Warburg effect” observed in cancer. This metabolic reprogramming allows cells to generate energy rapidly while supporting biosynthesis and proliferation. It can also increase resistance to programmed cell death, enabling abnormal cells to persist within the vessel wall. These changes help explain why pulmonary arteries can continue remodeling even when vasoconstriction is medically controlled.

Genetic research has provided some of the strongest evidence that PAH is driven by specific biological pathways. Variants in BMPR2, the gene most commonly associated with heritable PAH, can impair signaling involved in vascular growth, repair, and cell survival. Other genes linked to PAH include SOX17, TBX4, and KCNK3. Several of these genes influence the bone morphogenetic protein, or BMP, pathway and its relationship with transforming growth factor beta, known as TGF-β. When this signaling network is disrupted, the balance between controlled growth and abnormal proliferation can shift, increasing susceptibility to pulmonary vascular disease. Genetic risk, however, is not destiny: environmental exposures, inflammation, infections, hormonal influences, and other biological triggers may determine whether disease develops and how rapidly it progresses.

These discoveries are already influencing treatment strategies. Traditional PAH medicines primarily target pathways that regulate vascular constriction, including endothelin, nitric oxide, and prostacyclin signaling. Newer approaches aim deeper at the mechanisms driving vascular remodeling. Sotatercept has attracted particular attention because it acts on signaling within the BMP/TGF-β superfamily and is designed to restore a healthier balance between growth-promoting and growth-inhibiting signals. By targeting the biology of vessel-wall remodeling rather than simply dilating arteries, the therapy represents a shift toward disease modification. Researchers are also studying seralutinib, which targets growth-factor and platelet-derived growth-factor signaling, as well as immune-directed treatments such as tocilizumab and rituximab. Metabolism-focused therapies remain another active area of investigation.

The review also highlights the growing importance of multi-omics research. Genomics can reveal inherited variants, while transcriptomics shows which genes are active in diseased tissue. Proteomics measures changes in proteins and signaling molecules, and metabolomics captures the chemical products of altered cellular pathways. Combining these layers may reveal why patients with apparently similar clinical diagnoses can have very different disease trajectories or responses to therapy. Such information could eventually help physicians classify patients according to molecular subtypes, identify biomarkers before irreversible vascular damage occurs, and select treatments according to the dominant mechanism in each individual.

Professor Chung argues that the most promising future for PAH depends on connecting laboratory discoveries with carefully designed translational studies and clinical trials. That challenge is substantial because the disease involves multiple interacting cell types and pathways, and because tissue samples from the pulmonary circulation are difficult to obtain. Nevertheless, the expanding molecular framework is changing the goals of treatment. Instead of focusing exclusively on lowering pulmonary artery pressure, researchers are seeking ways to repair endothelial dysfunction, restrain immune activity, normalize cellular metabolism, and reverse pathological remodeling. The field is moving toward precision medicine in which genetic and molecular information may guide therapy, offering patients the possibility of longer-lasting control and, ultimately, recovery of healthier pulmonary vascular function.

Subject of Research: Pulmonary arterial hypertension and its molecular mechanisms and emerging treatments

Article Title: Pulmonary arterial hypertension from a translational perspective: Bridging pathophysiology and treatment

News Publication Date: 17-Jun-2026

Web References: https://doi.org/10.1097/CM9.0000000000004137

References: Chinese Medical Journal, DOI: 10.1097/CM9.0000000000004137

Image Credits: Chinese Medical Journal

Keywords: Pulmonary arterial hypertension; PAH; BMPR2; SOX17; TBX4; KCNK3; BMP signaling; TGF-β; vascular remodeling; endothelial dysfunction; inflammation; metabolic reprogramming; sotatercept; seralutinib; precision medicine

Lipid nanoparticles (LNPs) have become one of the most important delivery technologies in modern medicine, helping transport messenger RNA into cells for vaccines, protein replacement therapies and experimental treatments. Yet a formulation that performs well in mice can behave very differently in humans. This species gap makes nonhuman primate studies an important step in drug development, but conventional methods for comparing multiple nanoparticles often require euthanasia and extensive tissue collection. A new study reports a way to measure the functional activity of several LNPs in the same animal using only a small blood sample.

Published in Nature Biotechnology, the work introduces a non-invasive nanoparticle barcoding platform called snapCodes. The method is designed to determine which LNP formulation successfully delivers its mRNA cargo and prompts cells to produce the encoded protein. Rather than relying on tissue harvesting, the researchers identify delivery events through DNA barcodes that can be recovered from serum and analyzed by sequencing. The approach could allow scientists to compare many candidate nanoparticles in a single nonhuman primate while reducing both animal use and the logistical burden of downstream analysis.

The central challenge addressed by the study is that nanoparticle delivery is not adequately described by measuring where an LNP travels. An LNP may reach an organ or circulate in the bloodstream without releasing its mRNA in a form that cells can translate. The new system instead records functional mRNA delivery. Each nanoparticle is associated with a distinctive DNA sequence, and the mRNA cargo is engineered to encode a fusion protein containing nanoluciferase and a SNAP-tag. Nanoluciferase is a compact enzyme that produces light in the presence of its substrate, while the SNAP-tag is a protein module capable of forming a covalent bond with specific chemical groups.

For the barcode to become a permanent molecular record of delivery, the researchers modified the DNA tags with benzylguanine. When a cell receives an mRNA molecule encoding the nanoluciferase–SNAP-tag fusion, the resulting protein can react with a benzylguanine-bearing snapCode. This creates a stable DNA–protein complex. The complexes are then exported from cells into the circulation, where they can be isolated from a small serum sample. Sequencing the recovered DNA reveals which barcoded LNPs produced the fusion protein and therefore achieved functional mRNA delivery.

The design turns transient protein expression into a measurable signal that can be collected without sacrificing the animal. This is particularly valuable in studies involving nonhuman primates, where tissue-based nanoparticle profiling can require euthanasia and can limit the number of formulations tested in each subject. A blood-based assay also makes it possible to monitor delivery over time, potentially showing how the activity of a formulation changes after dosing. The study used just 30 microliters of serum, a volume small enough to support repeated sampling and longitudinal measurements.

Before comparing nanoparticle performance, the researchers validated the molecular components of the system, including the activity of the snapCode chemistry and the ability of the fusion protein to participate in the intended covalent reaction. These steps were necessary to establish that the barcode signal reflected genuine expression of the delivered mRNA rather than nonspecific interactions. Once the platform had been characterized, the team used it to evaluate multiple chemically distinct LNPs in living animals.

The investigators intravenously administered six snapCoded LNP formulations to mice and nonhuman primates. Each formulation carried its own molecular identity, allowing all six candidates to be assessed in parallel. Serum collected after administration was analyzed for the DNA–fusion protein complexes, and sequencing was used to quantify the relative delivery activity associated with each barcode. By testing the same barcoded set across species, the researchers could directly examine whether patterns observed in mice were reproduced in nonhuman primates.

Such cross-species comparisons are crucial because LNP composition strongly influences biological behavior. Changes in ionizable lipids, helper lipids, cholesterol, polyethylene glycol-lipid components or particle structure can affect stability, organ distribution, cellular uptake and endosomal escape. These properties may differ substantially between rodents and primates because of differences in blood proteins, immune responses, tissue physiology and cellular trafficking. A method that measures expression from several formulations in the same animal may therefore reveal species-specific delivery patterns more efficiently than traditional one-formulation-per-animal experiments.

The snapCode strategy does not replace every form of nanoparticle analysis. It reports successful expression of a particular engineered mRNA and depends on the recovery and sequencing of the associated molecular complexes. It does not, by itself, provide a complete map of tissue distribution, long-term safety or therapeutic efficacy. Nevertheless, it adds a functional layer to LNP evaluation: scientists can ask not only where particles go, but which particles actually release mRNA that cells translate. In early development, that distinction can help prioritize candidates before more resource-intensive studies begin.

By combining multiplexed dosing with low-volume serum sampling, the researchers aim to make nonhuman primate testing more informative while using fewer animals. The platform could be adapted for screening delivery systems designed for different organs or therapeutic proteins, provided that the encoded reporter and barcode chemistry remain compatible with the intended experiment. As mRNA medicines expand beyond vaccines into gene editing, immunotherapy and protein replacement, tools that connect nanoparticle chemistry to real biological output may become increasingly important. Non-invasive barcoding offers a way to track those differences in living subjects and could help narrow the path from promising LNP design to clinically relevant drug candidate.

Subject of Research: A non-invasive method for comparing functional mRNA delivery by multiple lipid nanoparticles in mice and nonhuman primates using serum-based DNA barcodes.

Article Title: Non-invasive nanoparticle barcoding in nonhuman primates

Article References: Jang, B., Zenhausern, R., Lian, L. et al. Non-invasive nanoparticle barcoding in nonhuman primates. Nat Biotechnol (2026). https://doi.org/10.1038/s41587-026-03262-6

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41587-026-03262-6

Keywords: lipid nanoparticles, mRNA delivery, nanoparticle barcoding, snapCodes, SNAP-tag, nanoluciferase, nonhuman primates, serum sequencing, drug delivery, RNA therapeutics

A new study is drawing attention to a hidden problem in mental-health research: scientists may be using the same words to describe first episode psychosis (FEP) while measuring entirely different things. In an analysis published in Schizophrenia, researchers Seiber, Sridhar, Hasenstab and colleagues examine how U.S. insurance claims data are used to estimate the incidence of FEP—the number of new cases appearing in a population over a defined period—and argue that greater standardization is urgently needed.

First episode psychosis refers to the point at which a person experiences psychotic symptoms for the first time in their life. These symptoms can include hallucinations, delusions, disorganized thinking or major changes in behavior and functioning. FEP is not a single diagnosis. It can occur in schizophrenia-spectrum disorders, bipolar disorder, severe depression and several medical or substance-related conditions. Because early treatment is linked to better long-term outcomes, knowing how often FEP occurs is essential for planning specialized clinics, staffing early-intervention programs and directing public-health resources.

The researchers focus on U.S. claims data, enormous digital records generated when patients receive medical care and providers submit bills to insurers. These databases can include diagnostic codes, procedure codes, prescription information, dates of service and details about emergency, inpatient and outpatient treatment. Unlike traditional epidemiological surveys, claims datasets can cover millions of people and make it possible to study patterns across large geographic areas. Their scale, however, does not automatically make their estimates precise.

The central challenge is that a billing code is not the same as a clinical assessment. A code for psychosis may indicate a confirmed diagnosis, a suspected condition, a historical problem or a provisional label assigned during an emergency evaluation. Claims data also rarely contain the complete clinical narrative needed to determine whether symptoms truly represent a person’s first psychotic episode. A patient may have received care in another health system, paid out of pocket, changed insurers or carried an earlier diagnosis that is invisible in the database being studied.

Even the definition of “incidence” can vary. Some researchers count a first-ever psychosis-related diagnosis, while others count a first hospitalization, a first emergency-department visit or the first appearance of a relevant code after a period without documented care. These approaches can produce different numbers from the same underlying population. A study requiring a long “clean period”—a span of time with no previous psychosis-related claims—may reduce the likelihood of counting established cases as new, but it can also exclude people whose earlier treatment occurred outside the available records.

Age, sex, insurance type and geography can further influence the apparent rate of FEP. Young adults may be more likely to encounter emergency services, while people in rural areas may face limited access to psychiatrists and specialized programs. Differences in insurance coverage can determine which services generate observable claims. If researchers compare estimates without accounting for these factors, they may mistake variations in detection or access for genuine differences in disease occurrence.

The study’s call for standardization points toward a more consistent technical framework. Researchers need to specify which diagnostic codes qualify as psychosis, whether substance-induced and medically caused episodes are included, how long a person must remain free of prior claims to be considered a new case, and which healthcare settings are examined. They also need to define the population denominator—the number of people considered at risk—and explain how individuals with incomplete enrollment or interrupted insurance coverage are handled.

Validation is another crucial step. Claims-based algorithms should be compared with detailed chart reviews, clinical registries or structured assessments to determine how accurately they identify genuine FEP. Two statistical measures are especially important: sensitivity, or the ability to capture true cases, and positive predictive value, or the proportion of algorithm-identified cases that are confirmed after review. An algorithm with high sensitivity may find more possible cases but also produce more false positives; one with high specificity may miss people who received vague or inconsistent coding.

Standardized methods could make FEP estimates more comparable across states, insurers and research teams. That would help scientists detect real trends rather than methodological noise and could reveal whether early psychosis services are reaching the populations most in need. More reliable estimates could also strengthen efforts to investigate racial, socioeconomic and regional disparities, provided that claims data are interpreted carefully and not treated as a complete substitute for clinical information.

The message from the research is not that U.S. claims databases are unusable. On the contrary, their breadth makes them one of the most powerful tools available for studying mental-health care at scale. But their value depends on transparent definitions, validated case-finding methods and clear reporting standards. As health systems increasingly rely on administrative data and automated analytics, agreeing on what counts as a new case of psychosis may be the difference between a map that reveals a public-health crisis and one that merely reflects the quirks of the billing system.

Subject of Research: Estimating first episode psychosis incidence rates using U.S. claims data

Article Title: Estimating first episode psychosis (FEP) incidence rates using U.S. claims data: a need for standardization

Article References: Seiber, E.E., Sridhar, S., Hasenstab, K.A. et al. “Estimating first episode psychosis (FEP) incidence rates using U.S. claims data: a need for standardization.” Schizophrenia (2026). https://doi.org/10.1038/s41537-026-00793-4

Image Credits: AI Generated

DOI: 10.1038/s41537-026-00793-4

Keywords: First episode psychosis, FEP, incidence rates, U.S. claims data, schizophrenia, mental health, epidemiology, healthcare data, standardization

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