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Prepregnancy Lifestyle Risk Factors in Women Seeking Digital Fertility Services: Cross-Sectional Descriptive Study

<strong>Background:</strong> Approximately 1 in 3 pregnancies in the United States are complicated by one or more adverse pregnancy outcomes. This high prevalence contributes to the elevated rates of maternal and infant mortality in the United States. Modifiable prepregnancy or preconception lifestyle factors have been associated with adverse pregnancy outcomes in observational studies, which underscores the importance of preconception care. <strong>Objective:</strong> This cross-sectional descriptive study aimed to (1) estimate the prevalence of preconception lifestyle risk factors among women seeking services from a digital fertility platform, (2) characterize the study population and present relevant reference data, and (3) examine the distribution of prepregnancy lifestyle scores across demographic and clinical subgroups. <strong>Methods:</strong> The digital health company, Doveras Fertility, has built a prepregnancy digital health platform for individuals and couples seeking to optimize their fertility potential. Targeting users prior to initiation of pregnancy, the platform facilitates the assessment of baseline lifestyle risk factors. This paper reports on 396 adult women who sought the platform’s services in a 1-month period between May and June 2024 by completing a digital fertility questionnaire. Self-reported data were analyzed for 6 healthy prepregnancy lifestyle factors known to be associated with maternal health outcomes in prior observational studies, and each participant was given a composite score between 0 to 6 to represent the number of these healthy behaviors reported. The 6 healthy prepregnancy lifestyle factors include a BMI of 18.5 to 24.9 kg/m<sup>2</sup>, not currently smoking, ≥150 min/week of moderate to vigorous physical activity, healthy eating, no daily alcohol intake, and use of a prenatal multivitamin. <strong>Results:</strong> The study population was racially and ethnically diverse, with a mean age of 32.9 (SD 6.3) years. Most (235/396, 59%) participants received a composite score of 3 factors or fewer, and less than 5% (19/396) scored 6 out of 6. For context, this cohort had higher proportions of participants with unhealthy BMI and dietary patterns than those in the reference data. Regarding fertility, 46% (182/396) met the clinical definition of infertility (≥1 year trying to conceive), with the prevalence of infertility ranging from 16% (3/19) among those with the highest lifestyle scores to 59% (17/29) among those with the lowest. <strong>Conclusions:</strong> Most women seeking services from this digital fertility platform exhibited multiple lifestyle factors that have been previously associated with adverse pregnancy outcomes in observational studies. These results suggest that nearly all survey participants have potential risk factors for adverse maternal outcomes and therefore the potential to adopt at least one improvement in their lifestyle behavior. A digital platform may offer an accessible mechanism for identifying and characterizing preconception risk factors; however, future longitudinal studies are needed to evaluate whether platform-based interventions can effectively support behavior change and improve maternal health outcomes.
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Multimodal Depression Detection Through Conversational Interactions with an Emotion-Aware Social Robot: Pilot Study

Background: Depression affects more than 300 million people worldwide and is a leading contributor to the global disease burden. Traditional diagnostic methods, such as structured clinical interviews, are reliable but impractical for frequent or large-scale screening. Self-report tools like the Patient Health Questionnaire-8 (PHQ-8) require disclosure and clinician oversight, limiting accessibility. Recent artificial intelligence–based approaches leverage multimodal behavioral cues (linguistic, acoustic, and visual) for automated depression detection but remain constrained by limited adaptability, scarce annotated data, weak emotional expression in real-world settings, and the high computational cost of deployment of socially assistive robots (SARs). Objective: This study introduces Depression Social Assistant Robot (DEPRESAR)-Fusion, a lightweight multimodal depression detection framework designed for natural interactions with emotion-aware SARs. The objective of this study was to enhance detection accuracy in everyday conversations while addressing the challenges of data scarcity, weak emotional cues, and computational efficiency. Methods: DEPRESAR-Fusion integrates acoustic, linguistic, and visual features with an emotion-aware response module powered by large language models to adapt conversational strategies dynamically. To stimulate richer emotional expression, participants were exposed to emotionally evocative videos before SAR interactions. To overcome data scarcity, we augmented training with (1) public depression-related social media corpora and (2) synthetic samples generated via large language models. The proposed multimodal fusion architecture was evaluated on benchmark clinical datasets for both binary depression classification and PHQ-8 regression tasks. Performance was compared against prior multimodal baselines using root mean square error, mean absolute error, and standard classification metrics. Results: Participants who viewed emotional stimuli before interacting with SARs exhibited significantly higher emotional expressiveness, leading to improved model performance. Regression tasks showed lower root mean square error and mean absolute error, while classification tasks achieved significantly higher accuracy than the nonstimulus condition. DEPRESAR-Fusion outperformed prior multimodal baselines across multiple benchmark datasets, achieving state-of-the-art performance in both binary classification and PHQ-8 regression. The system maintained a lightweight architecture suitable for real-time deployment on SARs. Conclusions: DEPRESAR-Fusion demonstrates that integrating emotion induction, data augmentation, and lightweight multimodal fusion can enable accurate and scalable depression detection in naturalistic SAR interactions. By bridging the gap between structured clinical assessments and everyday conversations, this approach highlights the potential of SAR-based systems as nonintrusive, artificial intelligence–driven tools for proactive mental health support.
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New U.S. recommendation on hepatitis B vaccine will have dire consequences, studies project

A new U.S. policy that recommends offering hepatitis B vaccine at birth only to babies perceived to be at risk of neonatal infection will lead to increased numbers of infected infants and more cases of chronic hepatitis B infection in children that will generate millions of extra dollars in health care costs, two studies published Monday project.

“Avoiding an increase in neonatal infections under the targeted recommendation would require historically unattained levels of maternal [hepatitis B] screening or birth-dose coverage among infants of unscreened mothers,” said one of the studies, from researchers at Boston University, the University of Florida, and Johns Hopkins University.

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Supreme Court grapples with multibillion-dollar wave of lawsuits over Roundup cancer claims

WASHINGTON — The Supreme Court seemed divided Monday over whether to block thousands of lawsuits alleging the maker of the weedkiller Roundup failed to warn people it could cause cancer.

The case came before the justices after a tidal wave of litigation that included some multibillion-dollar verdicts against the global agrochemical manufacturer Bayer, which owns Roundup maker Monsanto.

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Pencil Beam Laser Could Help Researchers Design Brain-Targeted Therapies

Scientists at MIT say they made a finding in optical physics that could enable a new bioimaging method that’s faster and higher-resolution than existing technology. They discovered that, under the right conditions, laser light clutter can spontaneously self-organize into a highly focused “pencil beam.”

Using this self-organized pencil beam, the team captured 3D images of the human blood-brain barrier 25 times faster than the gold-standard method, while maintaining comparable resolution, according to the scientists.

By showing individual cells absorbing drugs in real-time, this technology could help scientists test whether new drugs for neurodegenerative disease like Alzheimer’s or ALS reach their targets in the brain, with greater speed and resolution, they add.

“The common belief in the field is that if you crank up the power in this type of laser, the light will inevitably become chaotic. But we proved that this is not the case. We followed the evidence, embraced the uncertainty, and found a way to let the light organize itself into a novel solution for bioimaging,” says Sixian You, PhD, assistant professor in the MIT department of electrical engineering and computer science (EECS), a member of the research laboratory for electronics.

You is senior author of a paper “Self-localized ultrafast pencil beam for volumetric multiphoton imaging” on this imaging technique in Nature Medicine.

A better beam

When the researchers performed characterization experiments of this pencil beam, it was more stable and high-resolution than many similar beams. Other beams often suffer from “sidelobes,”  blurry halos of light that can distort images.

Their beam was more pristine and tightly focused, according to You. Building on those experiments, the researchers demonstrated the use of this pencil-beam in biomedical imaging of the human blood-brain barrier.

Scientists and clinicians often want to see how drugs flow inside the vasculature of the blood-brain barrier and whether they reach their targets within the brain. But with standard optical settings, the best one can do is capture one 2D section of the vasculature at a time, and then repeat the process multiple times to generate a fuller image, You explains.

Using this new technique, the researchers created an ultrafast, high-precision pencil beam that enabled them to dynamically track how cells absorb proteins in real-time.

“The pharmaceutical industry is especially interested in using human-based models to screen for drugs that effectively cross the barrier, as animal models often fail to predict what happens in humans. That this new method doesn’t require the cells to have a fluorescent tag is a game-changer,” notes Roger Kamm, PhD, the Cecil and Ida Green Distinguished Professor of Biological Science and Mechanical Engineering.

“For the first time, we can now visualize the time-dependent entry of drugs into the brain and even identify the rate at which specific cell types internalize the drug.”

“Importantly, however, this approach is not limited to the blood-brain barrier but enables time-resolved tracking of diverse compounds and molecular targets across engineered tissue models, providing a powerful tool for biological engineering,” points out postdoctoral fellow Sarah Spitz, PhD.

The team reports that it captured cellular-level 3D images that were higher quality than with other methods, and generated these images about 25 times faster.

“Usually, you have a tradeoff between image resolution and depth of focus—you can only probe so far at a time. But with our method, we can overcome this tradeoff by creating a pencil-beam with both high resolution and a large depth of focus,” You says.

In the future, the researchers want to better understand the fundamental physics of the pencil-beam and the mechanisms behind its self-organization. They also plan to apply the technique to other scenarios, such as imaging neurons in the brain, and work toward commercializing the technology.

The post Pencil Beam Laser Could Help Researchers Design Brain-Targeted Therapies appeared first on GEN – Genetic Engineering and Biotechnology News.

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Obesity Leaves Lasting DNA Methylation Memory in Immune Cells

A new study suggests that obesity leaves a durable molecular imprint on the immune system, one that persists long after weight loss and may continue to influence disease risk. Researchers at the University of Birmingham report that key immune cells retain an “epigenetic memory” of obesity, potentially sustaining inflammation and metabolic dysfunction even after patients return to a healthy weight.

The findings, published in EMBO Reports, provide a mechanistic explanation for a long-standing clinical observation: that individuals who lose weight often remain at elevated risk for conditions such as type 2 diabetes, cardiovascular disease, and certain cancers.

Immune cells retain a “memory” of obesity

The study focuses on CD4+ helper T cells, central regulators of immune coordination. By analyzing patient samples across multiple cohorts, including individuals undergoing pharmacological weight loss, rare genetic obesity syndromes, and lifestyle interventions, the researchers identified persistent epigenetic modifications in these cells.

Specifically, obesity was associated with changes in DNA methylation, a process in which chemical tags are added to DNA and alter gene expression without changing the underlying sequence. These modifications effectively encode a molecular memory of prior metabolic state.

As explained by the authors, these epigenetic marks can persist for years after weight loss. “The findings suggest that short-term weight loss may not immediately reduce the risk of some disease conditions associated with obesity,” said Claudio Mauro, PhD, senior author of the study. Instead, the immune system appears to retain a record of past metabolic stress that continues to influence cellular behavior.

Persistence beyond weight loss

The durability of this imprint is striking. The study estimates that obesity-associated DNA methylation patterns in T cells may persist for five to ten years after successful weight reduction. This suggests that immune remodeling lags far behind metabolic normalization.

Supporting this, the team observed similar patterns across diverse experimental systems, including human clinical samples and mouse models of diet-induced obesity. Together, these data point to a conserved biological mechanism rather than a transient or context-specific effect.

This persistent immune memory may help explain why relapse and long-term complications are common in obesity. As noted by Belinda Nedjai, PhD, of Queen Mary University of London, “the immune system retains a molecular record of past metabolic exposures, which may have implications for long-term disease risk and recovery.”

Disruption of cellular housekeeping and aging

At the functional level, the epigenetic changes identified in T cells appear to disrupt two critical biological processes: autophagy and immune senescence.

Autophagy, the process by which cells degrade and recycle damaged components, is essential for maintaining cellular health. The study suggests that obesity-associated DNA methylation impairs this pathway, reducing the cell’s ability to clear waste and maintain homeostasis.

In parallel, the researchers observed effects on immune aging, or senescence. Dysregulated T cells exhibited features of premature aging, potentially contributing to chronic inflammation and reduced immune resilience.

Together, these alterations could create a persistent pro-disease environment, even after weight loss. This reframes obesity not simply as a reversible metabolic state, but as a condition capable of inducing long-term immune reprogramming.

Implications for treatment strategies

The findings have direct implications for how obesity is managed clinically. If immune dysfunction persists for years after weight loss, then short-term interventions may be insufficient to fully restore health.

Instead, sustained weight maintenance—and potentially additional therapies targeting immune reprogramming—may be required. Mauro noted that “ongoing weight management following loss will see the ‘obesity memory’ slowly fade,” though this process may take years.

The study also points to potential therapeutic strategies. Drugs such as SGLT2 inhibitors, already used in diabetes treatment, may help accelerate the reversal of these epigenetic changes by reducing inflammation and promoting clearance of dysfunctional cells.

Rethinking obesity as a chronic immuno-metabolic disease

Beyond its immediate clinical implications, the study contributes to a broader conceptual shift in how obesity is understood. Rather than being defined solely by excess adiposity, obesity emerges as a condition that induces lasting systemic changes, particularly within the immune system.

As Andy Hogan, PhD, of Maynooth University emphasized, “obesity is a chronic progressive and relapsing disease,” and these findings help explain the biological basis of that persistence.

By identifying an epigenetic “memory” within immune cells, the work highlights a previously underappreciated dimension of metabolic disease: its capacity to reprogram immune function over the long term.

Looking ahead

The discovery of obesity-induced immune memory raises new questions about reversibility and intervention. Can these epigenetic marks be actively erased? And if so, how can therapies be designed to accelerate immune recovery?

Future research will likely focus on targeting these pathways directly, with the aim of restoring normal immune function and reducing long-term disease risk.

For now, the findings underscore a key message: losing weight is only part of the story. Fully reversing the biological impact of obesity may require sustained intervention—not just at the metabolic level, but at the level of the immune system itself.

The post Obesity Leaves Lasting DNA Methylation Memory in Immune Cells appeared first on Inside Precision Medicine.

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The Power of Multimodality in Multimodal Large Language Models, Unimodal ChatGPT 5.0, and Human Clinical Experts on a Wound Care Certification Examination: Cross-Sectional Comparative Study

Background: Multimodal large language models (MLLMs) capable of integrating visual and textual information represent a promising advancement for clinical applications requiring image interpretation. Wound care assessment, which demands simultaneous analysis of wound photographs and clinical data, provides an ideal domain to evaluate multimodal vs unimodal artificial intelligence capabilities against human expertise. Objective: This study aims to compare the performance of MLLMs, unimodal ChatGPT 5.0, and human clinical experts on a standardized wound care certification examination. Methods: This cross-sectional comparative study evaluated 3 participant groups on a 25-question wound care certification examination spanning 4 clinical domains (Diagnosis, Treatment, Complication Management, and Wound Subtype Knowledge). Participants included 3 MLLMs (Med-PaLM 2, LLaVA-Med, and BioGPT), 1 unimodal large language model (ChatGPT 5.0), and 4 human clinical experts (general surgeon, wound care nurse, and 2 internal medicine physicians). Statistical analyses included one-way ANOVA with Tukey post hoc tests and domain-specific Kruskal-Wallis comparisons. Results: Human experts achieved the highest accuracy (mean 86%, SD 9.1%), followed by MLLMs (mean 78.7%, SD 12.2%), while ChatGPT 5.0 achieved 64% accuracy, failing the 70% certification threshold. Significant overall group differences were observed (=8.42, =.02, η²=0.74). MLLMs significantly outperformed ChatGPT 5.0 (difference=14.7 percentage points, =.03, Cohen =1.38), with the multimodal advantage most pronounced in visually dependent domains: Diagnosis (81% vs 43%, =.008) and Complication Management (72% vs 50%, =.03). No multimodal advantage was observed for text-based Wound Subtype Knowledge (both 67%). Med-PaLM 2 achieved 92% accuracy, matching that of the wound care nurse, while the general surgeon achieved the highest overall performance (96%). Conclusions: MLLMs demonstrate significant performance advantages over unimodal artificial intelligence in wound care assessment, particularly for visually dependent clinical tasks. While human experts with specialized wound care experience maintain overall superiority, the point estimate of the top-performing MLLM (Med-PaLM 2, 92%) fell within the observed range of human scores; however, the underpowered comparison (power=0.52) and wide CIs preclude definitive conclusions regarding noninferiority or equivalence to human experts. These findings support the potential role of MLLMs as clinical decision-support tools, warranting further adequately powered validation studies.
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RoBuster—Corpus Annotated With Risk of Bias Text Spans in Randomized Controlled Trials in Physiotherapy and Rehabilitation: Corpus Development and Annotation Study

Background: Risk of bias (RoB) assessment of randomized clinical trials (RCTs) is vital to answering systematic review questions accurately. Manual RoB assessment for hundreds of RCTs is a cognitively demanding and lengthy process. Automation has the potential to assist reviewers in rapidly identifying text descriptions in RCTs that indicate potential risks of bias. However, no RoB text span annotated corpus could be used to fine-tune or evaluate large language models (LLMs), and there are no established guidelines for annotating the RoB spans in RCTs. Objective: The revised Cochrane RoB 2 test (RoB 2) tool provides comprehensive guidelines for RoB assessment; however, due to the inherent subjectivity of this tool, it cannot be directly used as RoB annotation guidelines. The study aimed to develop precise RoB text span annotation instructions that could address this subjectivity and thus aid the corpus annotation. Methods: We leveraged RoB 2 guidelines to develop visual instructional placards that serve as annotation guidelines for RoB spans and risk judgments. Expert annotators used these visual placards to annotate a dataset named RoBuster, consisting of 41 full-text RCTs from the domains of physiotherapy and rehabilitation. We report interannotator agreement (IAA) between 2 annotators for text span annotations before and after applying visual instructions on a subset (n=9) of RoBuster. We also provide IAA on bias risk judgments using Cohen κ. Moreover, we used a portion of RoBuster (n=10) to evaluate an LLM using a straightforward evaluation framework. This evaluation aimed to gauge the performance of an LLM (here GPT 3.5) in the challenging task of RoB span extraction and demonstrate the utility of this corpus using a straightforward framework. Results: We present a corpus of 41 RCTs with fine-grained text span annotations comprising more than 28,427 tokens belonging to 22 RoB classes. The IAA at the text span level calculated using the F1 measure varies from 0% to 90%, while Cohen κ for risk judgments ranges between –0.235 and 1.0. Using visual instructions for annotation increases the IAA by more than 17 percentage points. LLM (GPT-3.5) shows promising but varied observed agreements with the expert annotation across the different bias questions. Conclusions: Despite having comprehensive bias assessment guidelines and visual instructional placards, RoB annotation remains a complex task. Using visual placards for bias assessment and annotation enhances IAA compared to cases where visual placards are absent; however, text annotation remains challenging for the subjective questions and the questions for which annotation data are unavailable in RCTs. Similarly, while GPT-3.5 demonstrates effectiveness, its accuracy diminishes with more subjective RoB questions and low information availability.
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Surgeons’ Perceptions on the Utility of a Conceptual Novel Force Sensor at the Surgeon-Tool Interface: Formative Interview Study

Background: Real-time force feedback is essential in many surgical specialties. While previous research has focused on force measured at the tool-tissue interface, little work has explored the benefits, limitations, or opportunities of measuring force at the surgeon-tool interface. Objective: This study aims to explore scenarios in which surgeons from different medical specialties and experience levels could benefit from receiving feedback on the force exerted at the surgeon-tool (or surgeon-tissue) interface. Methods: Exploratory qualitative research was conducted through interviews with medical practitioners (N=15). This study explored perceptions of a conceptual novel force-sensing surgical glove that could provide real-time feedback in terms of usability, utility, value, and limitations. Opportunities and barriers to implement a sensor of this type in clinical practice were also explored. Participants had experience in anesthetics, dental surgery, plastic and dermatological surgery, general surgery, and obstetrics and gynecology, as these surgical fields all require precise feedback on exerted forces. Results: Participants identified two key areas where a force sensor could yield significant benefits: (1) it could enhance surgical training through objective skill assessment and quantifiable feedback, and (2) it could provide valuable insights into the forces applied during practice, particularly in scenarios where other sensory feedback is masked. Participants appreciated that a sensorized glove that can provide real-time force sensing at the surgeon-tool interface would allow for continued feedback irrespective of the instrument, and integrate seamlessly into their current surgical workflow. Furthermore, as surgeons in some specialisms, for example, dental or obstetrics and gynecology, perform manual tasks, having a sensorized glove would provide feedback in instances where they are physically manipulating tissue. However, participants expressed concerns about accurately defining safe force ranges due to the variability in patients’ anatomical structures and the potential interference with tactile sensation. Conclusions: Surgeons from various clinical practices agreed that force sensing at the surgeon-tool interface could be valuable and provide them with optimal versatility as to when they would adopt force sensing. A sensorized glove could improve decision-making and surgical outcomes when other sources of information guiding force exertion are masked. Conversely, it could be detrimental when the organic information to guide force exertion is distorted when using the sensor. While the choice between interaction modalities is dependent on the accessibility of different senses during surgery, design suggestions as to where sensors are best placed on a sensorized glove are dependent on the instrument used or the type of manual procedure conducted.
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STAT+: Erasca touts strong, though preliminary, results in trial of pancreatic and lung cancer therapy

The drugmaker Erasca said Monday that its RAS-targeting pill shrank tumors in 40% of patients with advanced pancreatic cancer and 62% of patients with advanced non-small cell lung cancer, results that the company said exceeded its expectations. 

The new data, collected from studies done in the U.S. and China, are still preliminary. However, Erasca said the clinical benefit and tolerability of its drug, called ERAS-0015, compared favorably to daraxonrasib, a similar RAS-targeting drug from Revolution Medicines that recently showed a doubling of overall survival in patients with advanced pancreatic cancer. 

“I’m excited about both datasets, but I think lung is more definitive at this point. The pancreatic results are maturing, but are very, very promising,” Erasca CEO Jonathan Lim told STAT. “All options are on the table.” 

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