Nature Medicine, Published online: 24 June 2026; doi:10.1038/s41591-026-04508-1
Retraction Note: Time-of-day immunochemotherapy in non-small cell lung cancer: a randomized phase 3 trial
Publisher Correction: Oral small molecule GLP-1 receptor agonist aleniglipron in people with overweight or obesity: a randomized, double-blind, placebo-controlled phase 2b trial
Nature Medicine, Published online: 24 June 2026; doi:10.1038/s41591-026-04532-1
Publisher Correction: Oral small molecule GLP-1 receptor agonist aleniglipron in people with overweight or obesity: a randomized, double-blind, placebo-controlled phase 2b trial
Cell atlas of brain aneurysms reveals fibroblast–macrophage crosstalk
Nature Neuroscience, Published online: 24 June 2026; doi:10.1038/s41593-026-02368-z
Brain aneurysms are a major cause of stroke worldwide, yet the cellular mechanisms that drive vessel instability remain poorly defined. A combined single-cell and spatial transcriptomic atlas of aneurysm tissue shows previously unrecognized interplay between scarring-associated fibroblasts and osteoclast-like macrophages that is associated with aneurysm formation and rupture.
Drug Targets LDL Receptor Pathway to Control Cholesterol
Cholesterol-related heart disease remains the leading cause of death worldwide, and while doctors have more tools than ever to treat it, many patients still can’t achieve safe cholesterol levels or can’t tolerate the side effects of available medications. Researchers at the University of California (UC), San Diego, School of Medicine have now uncovered a hidden biological pathway, dependent on a protein known as Ral, which explains why high-cholesterol diets steadily chip away at our body’s ability to clear harmful low-density lipoprotein (LDL) cholesterol from the blood. The team‘s preclinical study, including tests in mice, also identified a drug candidate already proven safe in humans that could potentially target the pathway.
“We’ve known for a long time that a high-cholesterol diet reduces the liver’s ability to clear cholesterol from the blood, but we didn’t fully understand why,” said Alan Saltiel, PhD, professor of medicine at UC San Diego School of Medicine and director of the UC San Diego/UCLA Diabetes Research Center. “This new discovery explains a critical piece of that puzzle.” Saltiel is senior author of the researchers’ published paper in Nature, titled “Dietary cholesterol activates a Ral-dependent pathway driving LDLR turnover,” in which they concluded, “Together, our findings reveal a Ral-dependent signalling pathway as a key regulator of LDLR turnover and cholesterol homeostasis.”
Disruptions in cholesterol homeostasis are closely linked to an increased risk of atherosclerosis and cardiovascular disease (CVD), the authors wrote. “Elevated low-density lipoprotein cholesterol (LDL-C) significantly contributes to CVD by promoting the formation of atherosclerotic plaques in arteries.”
The liver is the main organ involved in removing cholesterol from the blood so it can be broken down and used elsewhere. This is done through LDL receptors (LDLRs), which sit on the surface of liver cells and act like docking stations, grabbing LDL cholesterol from the bloodstream and pulling it inside the cell for processing. “LDLRs have a crucial role in the uptake of LDL-C from the circulation by hepatocytes,” the investigators continued. The more LDL receptors on liver cells, the more cholesterol gets cleared from the blood, which is why most cholesterol-lowering drugs, such as statins or PCSK9 inhibitors, work by preserving or increasing the number of these receptors. However, the team noted, such treatments have their limitations. “The molecular switches that coordinate LDLR trafficking and turnover in response to nutritional cues, including high dietary cholesterol, remain poorly defined.”
The new research, carried out in mice and in human cells, reveals a previously unknown mechanism that quietly works against the cholesterol removal process, slowly reducing the number of LDL receptors and contributing to high blood cholesterol. The team found that this process begins when a protein called Ral—which Saltiel has previously studied in fat cells—is activated by high dietary cholesterol. “We describe here a previously unrecognized role for Ral signaling in orchestrating LDLR cellular trafficking and lysosomal routing in hepatocytes under chronic cholesterol stress,” the team stated.
Their studies showed that the more Ral is activated, the fewer LDL receptors remain available to clear cholesterol from the blood. This depletion process ultimately relies on a lysosomal protease enzyme called cathepsin A (CTSA). They further explained, “Ral engages the endocytic RalBP1–REPS1 complex to promote LDLR internalization and lysosomal routing, where LDLR is degraded by the lysosomal protease cathepsin A (CTSA).”
The researchers also found that blocking CTSA with a selective small molecule inhibitor (SAR164653) was enough to stabilize LDL receptors and dramatically lower circulating LDL cholesterol in mice. “Pharmacological inhibition of CTSA activity increases hepatic LDLR function and improves cholesterol clearance, offering a potential new therapeutic strategy for hypercholesterolaemia and cardiovascular disease,” they stated.
“There’s still a real need for new cholesterol-lowering options, since some people can’t get to safe levels even with the drugs we have now,” said Saltiel. “This new pathway we discovered is completely separate from anything that existing drugs target, so it gives us a new opportunity to fill that gap.”
After a fundamental biological breakthrough, it typically takes significant additional research to find drugs that target it. However, in this case, a CTSA inhibitor has already been through the early stages of drug development, with the initial goal of treating heart failure. While it was eventually shelved for strategic reasons, the drug had previously advanced to a Phase I clinical trial, where it was successfully tested for safety.
This discovery suggests that the investigational drug is already ready for testing in a Phase II trial for high cholesterol. “Luckily, there’s an experimental drug sitting on the shelf that’s already been shown to be safe in humans,” said Saltiel. “We hope to test whether this might be effective by conducting a clinical trial, which could potentially bring a new treatment option to patients much sooner than would have been expected.”
The post Drug Targets LDL Receptor Pathway to Control Cholesterol appeared first on GEN – Genetic Engineering and Biotechnology News.
Coproduction Without Youth? Closing the Participation Gap in Digital Mental Health Research
Young people are among the most intensive users of digital and generative artificial intelligence (GenAI)–enabled mental health tools, yet they remain underrepresented in the research and design processes that shape these technologies. Although participatory approaches such as co-design and patient and public involvement are widely endorsed as best practices, youth involvement in digital youth mental health (DYMH) research is often inconsistent, superficial, or limited to late-stage consultation. This participation gap risks producing interventions that are misaligned with young people’s lived experiences, priorities, and vulnerabilities, particularly in the context of rapidly evolving and scalable GenAI systems. This Viewpoint aims to reexamine the underlying drivers of the participation gap in DYMH research; clarify how participation is conceptualized and implemented across disciplines; and propose concrete, actionable recommendations to support more meaningful and consistent youth involvement across the research life cycle. We draw on interdisciplinary literature from digital mental health, human-computer interaction, child-computer interaction, and health research policy. Our Viewpoint integrates conceptual frameworks (eg, Lundy’s model of participation), existing reviews of co-design practices, and emerging evidence on GenAI in mental health. We adopt a life cycle–oriented perspective to examine how youth participation is distributed across stages of research and development, including problem formulation, design, implementation, and evaluation. We identify 3 interrelated drivers of the participation gap. First, conceptual and linguistic fragmentation obscures what participation entails in practice, with terms such as co-design, participatory design, user-centered design, and patient and public involvement used inconsistently across disciplines. Second, youth involvement is uneven across the research life cycle, with participation often concentrated in early ideation or usability testing but largely absent from upstream decision-making and downstream evaluation. Third, institutional barriers—including ethics review processes, consent requirements, funding constraints, and adult-centric research norms—systematically limit meaningful youth partnership. These challenges are amplified in the context of GenAI, where opaque “black box” systems, simulated therapeutic interactions, and rapid deployment cycles introduce distinct risks if youth perspectives are not integrated. We propose a set of minimum expectations to address these gaps, including explicit specification of participatory models, life cycle mapping of youth involvement, reporting of youth influence on decisions, dedicated funding for participation, proportional ethics frameworks, and mechanisms for youth-informed governance of GenAI systems. Closing the participation gap in DYMH research is both an ethical imperative and a practical necessity. Moving beyond aspirational commitments requires embedding youth participation as a standard, resourced, and accountable component of research, design, and governance. In the context of rapidly evolving digital and GenAI technologies, failure to do so risks producing interventions that are scalable but not safe, credible, or responsive to the needs of young people.
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From Metrics to Meaning in Neurological Rehabilitation: Clinicians’ Perspectives on Digital Metrics of Upper Limb Functioning—A Focus Group Study
Background: Digital assessment technologies, such as optical motion capture and inertial measurement units, enable detailed kinematic analysis and continuous monitoring of upper limb activity in persons with neurological conditions. While such are increasingly recognized in research, their uptake in clinical neurorehabilitation is limited. It remains unclear which clinicians perceive as most meaningful and how these are integrated into patient-centered care. Understanding clinicians’ information needs and reasoning processes is a prerequisite for implementing digital assessment technology. Objective: This study aims to characterize how rehabilitation professionals perceive, prioritize, and integrate into clinical reasoning and to identify features that would support their routine use. Methods: Three 90-minute focus groups were conducted in 3 Swiss neurorehabilitation centers, involving 11 clinicians with diverse professional backgrounds (5 physiotherapists, 4 occupational therapists, 1 movement scientist, and 1 medical practitioner). Participants discussed essential parameter domains and individually rated the relevance and meaningfulness of 17 kinematic metrics for the well-studied drinking task and 10 established arm use performance metrics. Verbatim transcripts were analyzed using reflexive thematic analysis, and rating data were summarized descriptively. Results: Five main themes were identified. (1) (active/passive range of motion, strength, selective muscle control, and grasp) form the basis for interpreting movement. (2) (smoothness, efficiency, and compensatory movement) are valued when aligned with observable task execution. (3) (hourly activity profiles, arm-use symmetry, and functional workspace) represents the reference for patient-centered reasoning. (4) , including diagnosis-specific preferences, shapes assessment selection. (5) reflects clinicians’ reliance on visual judgment complemented by normative values. Intuitive metrics such as task duration, number of movement units, and range of motion were favored, whereas confidence was lower in more complex metrics (eg, jerk and interjoint coordination). Conclusions: Clinicians value intuitive when they are clearly linked to patient-centered outcomes and supported by normative references. The findings highlight the need for targeted educational strategies and digital competency training that help clinicians interpret digital metrics and integrate them with contextual information and clinical reasoning.
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Supporting Student Mental Health With the Safespace Generative AI Chatbot: Mixed Methods Feasibility Study
Background: Generative artificial intelligence (GenAI) chatbots have the potential to provide personalized mental health support to individuals at scale. Objective: This study evaluates the feasibility and usage patterns of the Safespace GenAI chatbot, an artificial intelligence (AI)–driven smartphone app that offers a large language model–powered interactive chatbot to support mental health. Methods: Using a mixed methods approach, we explored baseline attitudes toward GenAI chatbots and chatbot usage patterns, conducted a qualitative content analysis of participants’ experiences, and descriptively assessed patterns related to preintervention depressive symptoms. The study included an initial sample of 42 university students, 20 of whom actively used the chatbot over 2 to 4 weeks, generating 286 user-chatbot interactions. Results: Preintervention surveys indicated that the majority of participants anticipated that the chatbot would be helpful (27/42, 64%) and that they trusted its privacy safeguards (39/42, 93%). Usage patterns suggested that the highest levels of interaction occurred early in the morning and late at night, when peer and professional support may be inaccessible. The qualitative analysis indicated that participants appreciated using the chatbot for reflection as a blended-care tool between their counseling sessions, while also naming technical barriers and specific design needs required to sustain engagement. In addition, our exploratory analyses descriptively showed that participants with elevated depression scores engaged in emotional disclosure during 99% (38 sessions with 8 participants) of their sessions, compared to 84% (26 sessions of 12 participants) of those with low symptoms. Due to the small sample size, future adequately powered studies are needed to inferentially examine these observed patterns. Conclusions: These findings provide initial insights into the usage and engagement dynamics of the Safespace GenAI chatbot and highlight directions for future research to optimize GenAI-driven mental health interventions. Trial Registration: AEA Registry AEARCTR-0013291; https://doi.org/10.1257/rct.13291-1.0
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Fine-Tuning Large Language Models for Motivational Interviewing in Health Behavior Change: Development and Evaluation Study
Background: Motivational interviewing (MI) is an effective counseling approach for promoting health behavior change, but its scalability is constrained by the need for highly trained human counselors. Large language models (LLMs) may provide a scalable way to support MI counseling, but evidence remains limited, especially for Chinese MI resources and evaluations based on standardized MI fidelity frameworks. Objective: This study aimed to develop Chinese large language models for motivational interviewing (MI-LLMs) and evaluate whether MI-focused fine-tuning could improve their ability to generate counseling responses consistent with MI principles. Methods: We first curated 5 publicly available Chinese psychological counseling datasets and assessed sampled conversations in terms of comprehensiveness, professionalism, authenticity, and safety. The 2 highest-scoring datasets, CPsyCounD and PsyDTCorpus, were selected for MI-style data construction. Using GPT-4 with a structured MI-informed prompt, we transformed 2040 multiturn counseling conversations into MI-style dialogs. Among these, 2000 dialogs were used for training and 40 for testing. Three Chinese-capable open-source LLMs (Baichuan2-7B-Chat, ChatGLM-4-9B-Chat, and Llama-3-8B-Chinese-Chat-v2) were fine-tuned with low-rank adaptation on the training dataset and were referred to as MI-LLMs. Automatic evaluation was conducted on the testing dataset using Bilingual Evaluation Understudy–4 (BLEU-4) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. Manual evaluation was conducted using the Motivational Interviewing Treatment Integrity Coding Manual 4.2.1. Thirty simulated counseling dialogs generated by the MI-LLMs were compared with 30 real MI dialogs sampled from AnnoMI and translated into Chinese. Two trained graduate student raters coded global scores and behavior counts, from which summary scores were subsequently calculated. Results: In automatic evaluation, fine-tuning substantially improved BLEU-4 and ROUGE scores across all 3 models compared with the base models. In manual evaluation, the MI-LLMs achieved technical and relational global scores, as well as total MI-adherent ratios that approached those of real MI dialogs. The MI-LLM based on ChatGLM-4-9B-Chat showed the strongest overall global performance. However, MI-LLMs produced fewer complex reflections and had lower reflection-to-question ratios than real MI dialogs. Conclusions: This study provides preliminary evidence that MI focused fine-tuning can help Chinese LLMs acquire core counseling behaviors consistent with MI principles. It also offers a scalable approach for constructing MI style dialog resources in Chinese. Nevertheless, current MI-LLMs should be regarded as early-stage tools for supporting, rather than replacing human counselors. Future work should expand real MI training data and strengthen the complex reflective skills of MI-LLMs. Further studies are needed to evaluate their effectiveness, acceptability, and safety in health behavior change settings in the real world.
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Beyond Area Under the Receiver Operating Characteristic Curve: Evaluating Predictive Performance Metrics Under Class Imbalance in Real-World Clinical Data
Background: Predictive models increasingly support clinical decision-making, although imbalanced outcome distributions are common in health care datasets and can distort performance evaluation. The area under the receiver operating characteristic curve (AUROC) remains the most frequently reported metric, despite its limited ability to reflect clinically meaningful performance under class imbalance. Objective: This study aimed to examine the influences of metric selection on the clinical interpretation of predictive models in imbalanced real-world health care data. Methods: This was a retrospective cohort study, including 17,018 hospitalized patients with COVID-19. Two predictive models using extreme gradient boosting (XGBoost) were developed to predict kidney replacement therapy (KRT) and mortality. Model performance was assessed using AUROC, macro–score, class-specific precision and recall, calibration (curve, slope, and intercept), decision curve analysis, and learning curves. Standard rebalancing strategies were applied exclusively to the training data to evaluate their impact on performance. Results: KRT occurred in 9.5%, and mortality in 18.0%. Although AUROC values were high (0.928 for KRT and 0.945 for mortality), performance in the minority class was substantially lower. For KRT, precision was 0.539 and recall 0.372; for mortality, precision was 0.725 and recall 0.718. Rebalancing strategies were associated with higher recall for the minority class, but this gain was accompanied by a reduction in precision, with minimal impact on AUROC values. As a result, AUROC remained high despite clinically relevant changes in error distribution between false positives and false negatives. The learning curves show a plateau-like shape, with stable validation performance across all training set sizes for both outcomes. Conclusions: AUROC alone is insufficient to evaluate prediction models in imbalanced health care scenarios, even with rebalancing. Routine reporting of class-aware metrics, alongside learning curve analysis, is essential to support robust and clinically meaningful evaluation of predictive models, rather than their direct translation into practice.
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Leveraging Self-Reporting in an Existing e-Cohort to Identify Clinically Relevant Mitral Valve Prolapse: Pilot Questionnaire Study
Background: Mitral valve prolapse (MVP) is a common valvulopathy associated, in a minority of cases, with heart failure, severe mitral regurgitation (MR), and sudden arrhythmic death. Digital tools hold promise for faster and more efficient recruitment of study participants into a large-scale MVP Registry. Objective: This study sought to evaluate the feasibility of surveying participants in an existing e-cohort to identify and clinically characterize MVP cases based on self-reporting and to recruit them in an MVP Registry at the University of California, San Francisco. Methods: We surveyed Northern Californian participants of the Health eHeart Study, a large e-cohort using the Eureka digital research infrastructure, about a prior diagnosis of MVP. MVP-positive respondents were asked to provide relevant medical records to confirm their eligibility and were invited to enroll in an MVP Registry if evidence of MVP was confirmed. A follow-up survey was sent after 1 month and after 5 years to collect data about clinical outcomes, including arrhythmias and the need for mitral valve repair. Results: The survey was delivered to 5746 participants, and 520 completed responses were collected. A prior diagnosis of MVP was self-reported by 16.3% (85/520) of respondents. Echocardiograms were obtained from 51.8% (44/85) of participants, and evidence of MVP was confirmed in 32.9% (n=28) of individuals, all of whom joined the registry. Participants with more severe MR had a higher number of correct responses regarding both MVP (odds ratio [OR] 10.58, 95% CI 3.58‐63.04; <.001) and MR diagnosis (OR 4.86, 95% CI 2.11‐16.14; =.002). Longitudinal data were available from most patients through responses to a follow-up survey sent 1 month and 5 years later (18/28, 64.3% and 17/28, 60.7% of MVP confirmed respondents, respectively). Among the patients with electronic health records available, 75% (3/4) had a correct self-reported diagnosis of arrhythmia. Conclusions: e-Cohort methods with self-reported clinical data can be used to prescreen candidates for a research study of MVP. These methods can rapidly identify and retain, among many cases of benign MVP, the minority with clinically relevant presentations such as significant MR and ventricular arrhythmias. These cases may be missed, especially when asymptomatic, by small-scale clinic-based recruitment or family screening methods.
