Gaze Allocation and Performance Across Task-Demand Conditions During Squat-Based Exergaming: Pilot Study Using Eye Tracking

<strong>Background:</strong> Exergames integrate motor, cognitive, and postural-control demands; however, how specific task-demand manipulations influence gaze allocation during exergame performance remains insufficiently characterized. <strong>Objective:</strong> This exploratory pilot study used a within-participant design to examine whether gaze allocation and task performance differed across baseline, concurrent cognitive-task, and unilateral-squat conditions during squat-based exergaming in healthy young adults. <strong>Methods:</strong> Eight healthy adults (mean age 20, SD 1 years; 7 men and 1 woman) used a squat-based exergame (<i>Ring Fit Adventure</i>; Nintendo) under 3 randomized conditions: baseline, concurrent cognitive task, and unilateral squats. In the concurrent cognitive task condition, participants performed serial subtraction during squatting. In the unilateral-squat condition, participants performed single-leg squats, which were intended to increase support-leg muscular demand as well as postural and motor-control requirements. Execution time, squat score, arithmetic performance, and eye-tracking metrics were recorded. Primary gaze outcomes were the proportion of fixation time and fixation counts allocated to predefined areas of interest (AOIs; command, avatar, and score) and to regions outside these areas across the entire trial. Differences from the baseline condition were examined using Dunnett tests or paired Wilcoxon signed-rank tests with Holm adjustment, and effect sizes were reported. <strong>Results:</strong> Exergame execution time increased from 31.0 (SD 3.3) seconds in the baseline condition to 38.2 (SD 6.1) seconds in the concurrent cognitive task condition and to 34.3 (SD 6.0) seconds in the unilateral-squat condition. In contrast, the squat score decreased from 98.8 (SD 2.8) in the baseline condition to 69.6 (SD 18.3) in the unilateral-squat condition. For fixation counts, allocation to the command AOI decreased from 44.8% (SD 11.4%) in the baseline condition to 32.1% (SD 8.9%) in the concurrent cognitive task condition and 36.2% (SD 8.5%) in the unilateral-squat condition. Outside AOI fixation counts increased in the concurrent cognitive task condition (55.7%, SD 8.6%) relative to the baseline condition (36%, SD 16.9%). <strong>Conclusions:</strong> In this exploratory within-participant pilot study, adding a concurrent cognitive task and performing unilateral squats resulted in different patterns of performance change and fixation count redistribution during squat-based exergaming. These preliminary findings suggest that AOI-based gaze allocation metrics may help characterize task demand–related attentional shifts in this setting. Larger confirmatory studies with more diverse samples and individual-level validation are needed before these metrics can be considered for adaptive or clinical applications.

STAT+: Want high-quality generic drugs? One expert has ideas on how consumers can trust their supply

For many years, generic drugs have accounted for roughly 90% of the prescriptions doled out to Americans thanks to their lower cost. Yet reliable supplies have been an issue due to inconsistent quality — more than 60% of the generic shortages have been attributed to quality concerns, according to the Food and Drug Administration. Numerous manufacturers, many based in India, have been cited for violating manufacturing protocols that led to product recalls and, sometimes, bans on sending drugs to the U.S.

But Kevin Schulman, a professor and deputy director of the Clinical Excellence Research Center at the Stanford University School of Medicine, believes a solution is within reach. Schulman — who has also worked with an independent lab called Valisure that found impurities in some widely used medicines — argues the FDA should encourage testing by independent, accredited laboratories.

We recently spoke with him about the subject. This is an edited version of our conversation.

Continue to STAT+ to read the full story…

<![CDATA[Phase 2 IRIS finds ML-004 misses social communication goals in autism, but notably reduces irritability in teens.]]>

American, British, and Italian Online Information on the Health Risks Associated With Eating Meat: Cross-Sectional Study

Background: The quality of online information regarding the risks associated with meat consumption could play a crucial role in shaping consumers’ behavior. Objective: This study aimed to investigate the quality of Italian, British, and American websites addressing this topic. Methods: A cross-sectional assessment of the top 100 British, Italian, and American web pages on the risks attributable to meat consumption was performed using the JAMA benchmarks tool, evaluating authorship by certified professionals and the inclusion of information on recommended meat consumption, potential meat substitutes, and coverage of issues such as diet sustainability and cancer, cardiovascular, and chronic disease prevention. Websites were then classified according to their stance toward meat consumption (neutral, promoting, or demonizing). Results: American and British websites were classified as high quality in 61% (61/100) and 78.1% (75/96) of cases, respectively, while only 22.3% (21/94) of Italian websites were classified as high quality. Multinomial regression showed that web pages with a demonizing stance toward meat consumption and those authored by certified health professionals were less likely to be Italian than American. Similarly, web pages discussing environmental risks and chronic diseases associated with excessive meat consumption were less likely to be Italian. Compared with American web pages, those promoting meat consumption and those authored by qualified professionals were less likely to be British. Web pages discussing chronic disease risks were also less likely to be British, whereas those mentioning cancer risks were more likely to be British. Conclusions: The widespread prevalence of poor online information quality, especially in certain countries, demands action. Promoting user education in assessing the reliability of websites and involving health professionals in this educational effort may represent viable strategies.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/2bac4d7e6e24532a0dbf79b936f8f403" />

Three Subtypes of Severe Pneumonia Might Inform Personalized Therapies

The results of a study headed by researchers at the University of Cambridge suggest that severe pneumonia has three different subtypes, a discovery that could help to explain why some patients in intensive care units (ICUs) recover from their illness faster than others, and for some patients, the disease can be life-threatening. Rather than assessing patients’ symptoms, the Cambridge team analyzed fluid taken from the lungs of patients admitted to the hospital with suspected pneumonia. Their results indicated that although each of the three different “pneumotypes” of severe pneumonia was associated with how the patients recovered, none could be reliably identified using standard blood tests.

The researchers suggest that their findings could in the future help inform personalized therapeutic strategies, allowing individual patients to receive the most appropriate treatment. Andrew Conway Morris, PhD, at the Department of Medicine at the University of Cambridge and an ICU consultant at Addenbrooke’s Hospital, Cambridge, is senior author of the team’s published paper in Nature Communications, titled “Pulmonary inflammation in severe pneumonia is characterised by compartmentalised and mechanistically distinct sub-phenotypes.”

Pneumonia is the commonest infectious cause of death worldwide, responsible for an estimated 2.5 million deaths per year, the researchers noted. In severe cases, patients may need to be admitted to an ICU and given mechanical ventilation. Severe pneumonia accounts for six in 10 infections managed in intensive care, and spread of the infection within ICUs is a significant concern.

Doctors have long struggled to understand why patients whose condition looks similar clinically can have very different recoveries. Some respond quickly to treatment, while others remain critically ill for weeks or even die. “Despite the considerable burden of pneumonia, the syndrome is incompletely understood, and diagnosis is difficult,” the team explained.

Conway Morris said, “Even though we’re able to treat the initial infection, many patients with severe pneumonia still struggle to come off the ventilator and can develop lung failure. Therapies to tackle inflammation in the lungs have had mixed results in clinical trials—some suggest they are beneficial, others that they’re harmful.”

Severe pneumonia is usually diagnosed through a combination of symptoms, imaging, and blood tests. Symptoms typically include fever or hypothermia, low oxygen levels, breathing difficulties, and confusion. “The current approach of classifying patients by their clinical syndromes—sepsis, acute respiratory distress syndrome, and so on—without looking at the underlying biology risks missing what’s key,” Conway Morris noted. “Instead of asking ‘Does this patient have pneumonia?’, we should be asking ‘What’s the inflammatory pattern in this patient’s lungs?’”

For their newly reported study, Conway Morris and team recruited 80 patients admitted with suspected severe pneumonia to the ICU at Addenbrooke’s Hospital. Instead of relying only on blood tests or scans, however, the Cambridge team analyzed the patient’s immune cells, inflammatory signals, and gene activity in bronchoalveolar lavage samples. “Here, we perform multifaceted assessments of bronchoalveolar transcriptome, cytokines, microbiology, and clinical features to biologically characterise a cohort of patients with suspected severe pneumonia,” they reported in their paper. The researchers discovered three distinct biological types—or pneumotypes (Pn)—of severe pneumonia, none of which could be reliably detected using standard blood tests, even though they were strongly linked to how patients recovered.

“Using bulk RNA sequencing of bronchoalveolar fluid, we have identified three phenotypes in the lungs of patients with lung injury and suspected pneumonia,” they stated. “These phenotypes were reflected in the differential immune cell populations and inflammatory proteins.”

The most common pneumotype—accounting for almost half (49%) of cases—was characterized by immune suppression, significant damage to the lining of the lungs, and bleeding in the alveoli (tiny air sacs within the lungs). There were fewer signs of inflammation, which may explain why treatments targeting inflammation can fail or even harm some patients. “Pn1, the most common, is characterized by low alveolar cytokines, expanded tolerogenic macrophages, and epithelial damage,” the investigators reported.

The second pneumotype—accounting for just under a quarter (23%) of cases—was characterized by a balanced immune response and active repair of damage to the lungs. Patients were most likely to recover faster from this pneumotype and require the shortest time on the ventilator, even though they initially looked just as ill as the others. “Pn2 displays the fastest resolution, exhibiting a balanced immune response and epithelial-endothelial repair signatures,” they continued.

Patients with the most dangerous pneumotype—the one that most resembles “classic” pneumonia—spent the longest on mechanical ventilation and had prolonged critical illness. They had severe and persistent inflammation, with a flood of immature immune cells in the lung. This group may be most likely to respond to anti-inflammatory therapies, the team said. “Pn3 is characterized by immature neutrophil infiltration, IL-6-STAT3 activation, and longer duration of mechanical ventilation,” the scientists stated.

First author Dr. Mark Jeffrey, at the Department of Medicine at the University of Cambridge, added, “Even though on the surface, all of the patients seemed to have similar types of pneumonia, with comparable illness severity, oxygen levels, and clinical diagnoses, their outcomes were very different. It was only when we drilled down and looked at patterns of inflammation that the differences became apparent. Severe pneumonia is not a single disease, but several biologically distinct conditions that happen to look alike. This helps explain why ‘one-size-fits-all’ treatments—including some immune-modulating drugs—have often failed in clinical trials.”

Interestingly, the authors added in their report, “Each of the Pneumotypes contained both patients with and without confirmed pneumonia, implying common mechanisms underpinning lung injury arising from different mechanisms.”

The tests used to determine the pneumotypes are too complex to enable rapid classification, but the researchers hope to develop a simplified tool that could help them stratify the patients and ultimately offer tailored treatments.

Co-author Vilas Navapurkar, MBChB, from the John Farman Intensive Care Unit at Addenbrooke’s Hospital, said, “If we know which subtype of pneumonia an individual has, we can potentially tailor their treatment more precisely, boosting the immune response in some, while calming harmful inflammation in others. This has the potential to help critically ill patients, reduce deaths from pneumonia, shorten ICU stays, and cut unnecessary antibiotic use.”

The team also noted that while their study identified three Pneumotypes, it’s likely that others may exist, which might be identified in larger studies. In conclusion, they wrote, “… we have identified and validated three pulmonary confined endotypes in patients with severe pneumonia and lung injury. These phenotypes are underpinned by distinct mechanisms and have differential outcomes. The mechanisms point to different therapeutic options, as well as extending our understanding of the biology of lung inflammation in the context of severe pneumonia.”

The post Three Subtypes of Severe Pneumonia Might Inform Personalized Therapies appeared first on GEN – Genetic Engineering and Biotechnology News.

Nvidia Unveils Science Reasoning AI Suite with BioNeMo Agent Toolkit

Nvidia has announced the NVIDIA BioNeMo Agent Toolkit, which turns complex scientific workflows into agent-executable tasks, including model selection, input preparation, workflow execution, output inspection, and results explanation.

The toolkit includes NVIDIA BioNeMo and is powered by NVIDIA NIM microservices, NVIDIA Parabricks, NVIDIA NeMo, and NVIDIA Nemotron and has applications across protein structure prediction, molecular docking, generative chemistry, genomic analysis, protein design, and biomarker discovery.  

“For the first time, researchers can build AI agents that understand scientific knowledge, use scientific tools, and execute scientific workflows,” said Jensen Huang, founder and CEO of Nvidia, in a press release. “This is a new way to do science—one that can dramatically accelerate discovery across biology, chemistry, genomics, and medicine.” 

Nvidia has entered collaborations with research organizations, including the Arc Institute, Open Molecular Software Foundation, and the University of Washington’s Institute for Protein Design (IPD). The partnership with IPD has accelerated runtimes for the biomolecular complex prediction tool, RosettaFold3, resulting in two times faster performance than the prior generation model.  

“Every tool we’ve built for protein design is only as powerful as the scientists who can efficiently access it,” said David Baker, PhD, professor of biochemistry at the University of Washington and director of the Institute for Protein Design, in a public release. “The next leap in science won’t come from a single discovery; it will come from the speed of iterative designs and agents that can repeatedly reason through the complexity of biology at a speed humans never could.” 

The toolkit’s applications include virtual screening, where agents identify promising small-molecule drug candidates by generating compound designs, docking them to a target, predicting binding strength, and filtering for developability properties. The agent can then output which candidates should be prioritized to compress timelines. 

In genomic analysis and target discovery, agents can identify genetic insights and biological targets from raw sequencing data. Agents can also connect real-world data to reasoning models for biomedical research, improving the efficiency and accuracy of clinical development processes, including literature review, protocol generation, clinical trial screening, and pharmacovigilance. In medical imaging analysis, agents can process, segment, synthesize, and reason over medical imaging data to support biomarker discovery. 

AI-native biology companies, including Boltz, Basecamp Research, Chai Discovery, PerturbAI, Dyno, and Proxima, have collaborated with NVIDIA to develop tools to accelerate therapeutic design workflows. Diagnostics and pharmaceutical companies, including Lilly and Natera, are using BioNeMo Agent Toolkit to scale agentic workflows across discovery, translational research, and clinical insight. 

The post Nvidia Unveils Science Reasoning AI Suite with BioNeMo Agent Toolkit appeared first on GEN – Genetic Engineering and Biotechnology News.

Judge rules government can’t stop SNAP dollars from buying candy and sugary drinks

The federal government can’t block benefits from the nation’s largest food aid program from being used to buy candy, soda and other sugary drinks, a judge ruled.

Monday’s ruling scuttles restrictions now in place or planned for the federally funded and state-run Supplemental Nutrition Assistance Program in 23 states. President Donald Trump’s administration has not said whether it will appeal to a higher court.

Read the rest…

Nextgen Platform Combines VectorBuilder and Maxcyte Technologies to Boost Clinical-Grade Cell Engineering

VectorBuilder and MaxCyte formed a strategic partnership focused on co-developing a new gene delivery solution using VectorBuilder’s MiniVec™ plasmid system and MaxCyte’s clinical electroporation platform for ex vivo cell engineering.

Officials at both companies say that ex vivo cell therapies such as CAR-T, CAR-NK, and iPSC-based treatments have gained significant traction, but critical challenges in safety and manufacturability continue to limit their broader application. They explain that existing gene delivery methods present significant trade-offs: traditional electroporation of conventional DNA or RNA often results in poor target cell viability or limited therapeutic durability, while lentiviral vectors, though widely used, carry high production costs and potential safety risks such as malignancy arising from vector integration into the host genome.

What is collectively needed, both companies maintain, are more efficient, safer, and scalable approaches to cell engineering.

The partnership between VectorBuilder and MaxCyte has been designed to address these challenges by developing the next-generation electroporation-based ex vivo gene delivery solution through the integration of VectorBuilder’s MiniVec backbone with MaxCyte’s Flow Electroporation® technology.

MiniVec is described as a miniaturized plasmid backbone that eliminates the need for antibiotic- or additive-based selection during fermentation to simplify translation to GMP-grade production. Its reduced prokaryotic sequences have been shown to improve yield and performance across a broad range of applications, according to VectorBuilder.

Flow Electroporation relies on a continuous-flow process that reduces cellular stress, preserving cell viability and functionality while enabling scalable, highly efficient gene delivery. This two-pronged approach is designed to deliver significantly improved cell viability and higher transfection efficiency compared to conventional models, pointed out Maher Masoud, president and CEO of MaxCyte.

“Cell therapy development requires delivering therapies that are manufacturable, scalable, and commercially viable,” he said. “We believe we can enable a new standard for nonviral gene delivery—one that enhances cell quality, improves manufacturing efficiency, and provides developers with a more streamlined path from research through commercialization.”

“This partnership combines our complementary strengths to establish a next-generation platform for efficient, safe, and scalable electroporation-based cell engineering for therapies such as CAR-T.

“As both companies have extensive expertise in GMP-compliant clinical development solutions, the combined platform is well aligned to enable a seamless development pipeline from clinical trials through to commercialization,” noted Bruce Lahn, PhD, founder and chief scientist of VectorBuilder. “Our aim is to provide a platform delivering high-end performance with an optimal cost-of-goods and price-per-dose model to ease scale-up, commercial strategy, and fundraising efforts for drug developers.”

The post Nextgen Platform Combines VectorBuilder and Maxcyte Technologies to Boost Clinical-Grade Cell Engineering appeared first on GEN – Genetic Engineering and Biotechnology News.

Justice Department announces hundreds of charges in multibillion-dollar health care fraud crackdown

WASHINGTON — The Justice Department on Tuesday announced criminal charges against 455 people as part of a two-week health care fraud crackdown that officials say involved more than $6.5 billion in false claims submitted to insurers.

Among those charged is a nurse practitioner accused in Texas of billing Medicaid for medically unnecessary wound-care procedures and using the proceeds for fancy jewelry and luxury cars; a mental health company owner who prosecutors say exploited the homeless by billing for crisis stabilization services they did not need; and a hospice owner alleged to have paid kickbacks to a funeral home employee for information about Medicare beneficiaries.

Read the rest…

Quantum Mechanics Principles Help Researchers Build Cancer Prediction Model

A team of researchers at the University of Utah has developed a quantum mechanics-based artificial intelligence and machine learning method, which they say can improve the prediction of cancer outcomes and identify treatment targets using the comprehensive molecular background of individual patients. The approach, described in APL Quantum, addresses a major roadblock to leveraging conventional AI for predicting patient outcomes in clinical trials, namely the vast amounts of data needed to train large language models and to account for the complexity of disease drivers.

“It’s much more than just one gene—everything that’s happening in the cells of the patient matters,” said Orly Alter, PhD, associate professor of biomedical engineering at the University of Utah’s Scientific Computing & Imaging Institute. To take this into account, the team developed a method that is capable of analyzing multiple layers of molecular information simultaneously, including tumor DNA, blood DNA, and tumor RNA.

Clinical trials can enroll as few as 20 to 100 patients, while existing genomic datasets often contain data detailing millions to billions of molecular features. According to the researchers, many existing AI and machine-learning methods need more patient samples than genetic features to properly train the model. For instance, they pointed to a recent large language model of the 30,000-nucleotide genome of the COVID-19 virus, which needed 110 million samples. Extrapolating from this, the Utah team said that a complete modeling of the three billion nucleotides in the human genome would require 33 trillion patient samples.

To overcome this constraint, the investigators used a collection of algorithms known as multitensor comparative spectral decompositions, which Alter developed based on the quantum mechanical concepts of entanglement and superposition. The result, the team said, is analogous to a prism splitting light into its individual color components, providing data on multiple layers of a patient’s molecular makeup, including tumor and blood genomes and RNA transcriptomics, able to demonstrate linked patterns in cancer that can predict individual patient outcomes.

“The model rewrites a set of multiple omic profiles from one patient as a superposition of phenotypes, each represented by a set of multiple entangled patterns,” the researchers wrote. Importantly, data from one molecular profile can approximate an analysis from other profiles, which allows predictions to remain consistent among different types of biological data.

The researchers tested their model using an open-source dataset of the childhood cancer neuroblastoma. Their analysis found two previously unrecognized predictors of survival and treatment response, with each predictive element found in three separate, but interconnected data types: tumor genomes, blood genomes, and tumor transcriptomes. The study found that these predictors outperformed the currently used biomarker, the MYCN gene, for predicting treatment response and outcomes.

The new method builds on the substantial body of work by Alter and colleagues. Earlier research in this area had used related comparative spectral decomposition methods to analyze genomic and transcriptomic data in other tumor types, including glioblastoma.

The team will continue its work as it looks to develop an approach that can be used in the clinic. “That’s the ultimate precision medicine,” Alter said. “You have a single person. Can you take the data from just that one person and come up with a treatment for them? I think we can get there.”

The post Quantum Mechanics Principles Help Researchers Build Cancer Prediction Model appeared first on Inside Precision Medicine.