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Background. Liver transplant recipients face high risks of cardiometabolic events after transplant, driven by posttransplant weight gain, diabetes, hypertension, as well as immunosuppression-related side effects. Glucagon-like peptide-1 receptor agonists (GLP1RAs) improve metabolic and cardiorenal outcomes in nontransplant populations, but their role in liver transplant recipients remains understudied. Methods. This retrospective cohort study used TriNetX data (January 2010–December 2023) to compare outcomes in liver transplant recipients prescribed GLP1RAs (semaglutide, dulaglutide, liraglutide) within 1-mo posttransplant (n = 546) versus nonusers (n = 37 153). Propensity score matching (1:1) balanced demographics, comorbidities, and medications (n = 541 per group). Outcomes included mortality, hospitalizations, cardiovascular/renal/respiratory events, and graft outcomes. Results. Over a mean follow-up 838.5 d (SD 291.9) in the GLP1RA cohort and 884.3 d (SD 313.7) in the non-GLP1RA group, GLP1RA use was associated with a 43% lower all-cause mortality (7.0% versus 12.9%; hazard ratio [HR], 0.566; 95% confidence interval [CI], 0.381-0.841) and 39% fewer hospitalizations (60.4% versus 74.5%; HR, 0.613; 95% CI, 0.530-0.710). Acute heart failure (HR, 0.386; 95% CI, 0.285-0.524), renal failure/dialysis (HR, 0.489; 95% CI, 0.413-0.579), and respiratory failure (HR, 0.484; 95% CI, 354-0.662) risks were significantly reduced. No differences were observed in graft failure/rejection, myocardial infarction, stroke, atrial fibrillation/flutter, ventricular tachycardia, or ischemic optic neuropathy. Conclusions. Early GLP1RA initiation in liver transplant recipients was associated with reduced mortality, hospitalizations, respiratory, and cardiorenal complications without compromising graft safety. These findings support GLP1RAs as a promising adjunct therapy, warranting prospective trials to confirm benefits in this high-risk population.

Abstract INTRODUCTION The infectious hypothesis suggests that microbes like herpesviruses may play a role in the pathogenesis of Alzheimer's disease (AD) and other related dementias through methods that may include viral genome integration. The occurrence of herpesvirus genome integration in dementia patients has not been thoroughly characterized. METHODS Over 7500 total whole‐genome sequences from control, frontotemporal dementia/amyotrophic lateral sclerosis spectrum, Lewy body dementia (LBD), multiple system atrophy (MSA), and AD cohorts were screened for the integration of pathogen genomes using the PathSeq computational tool. RESULTS Low PathSeq scores for human herpesvirus 6 (HHV‐6) were consistent with the suspected integration of viral genome segments. The LBD and MSA cohorts had a significantly higher prevalence of this partial HHV‐6 genome integration. DISCUSSION This higher prevalence in both synucleinopathies was not noted in other herpesviruses, suggesting that the integration of HHV‐6 may play a role in a subset of these patients. Highlights Over 7500 whole‐genome sequences from controls and dementia patients were analyzed. Sequences consistent with integrated herpesviruses were identified using PathSeq. Prevalence of partial HHV‐6 integration was higher in synucleinopathies. Herpesviruses genome integration may play a role in subsets of dementia patients.

Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal cancer with a 5-year survival rate of 13%. Despite recent molecular stratification of tumors into distinct classical and basal-like cell states, most tumors are heterogeneous and contain both subtypes. Therefore, therapeutic approaches targeting only one subtype are unlikely to be effective as standalone PDAC treatments. Here, we integrated chromatin accessibility [assay for transposase-accessible chromatin with sequencing (ATAC-seq)], genome-wide occupancy [chromatin immunoprecipitation sequencing (ChIP-seq)] for epigenetic status (H3K27ac), and H3K4me3-anchored chromatin topology (HiChIP) to uncover subtype-independent highly interactive enhancers that interact with essential genes in PDAC. Motif analysis revealed that these common enhancers were bound by KLF5 with subsequent depletion leading to decreased cell viability via induction of apoptosis. To elucidate the transcriptional and epigenetic mechanisms by which KLF5 functions in PDAC, we used rapid depletion of KLF5 with dTAG technology and profiled the effects on the open and active chromatin landscape and transcription with nascent RNA and messenger RNA sequencing over time. Enhancer inactivation via KRAB domain Zim3-dCas9 fusion protein confirmed KLF5-bound enhancers regulate target genes, including the anti-apoptotic gene BCL2L1 . Multiplex immunofluorescence confirmed costaining of KLF5 and Bcl-xL in patient samples and overexpression of Bcl-xL rescued the induction of apoptosis after KLF5 depletion. Together, this study provides insights into common mechanisms to target highly heterogeneous PDAC tumors.

Abstract Hereditary hemorrhagic telangiectasia (HHT), an autosomal dominant vasculopathy affecting 1 in 5000 individuals, is the second most common inherited bleeding disorder worldwide. Despite this prevalence, comprehensive data on disease manifestations and complications remain limited. To address this gap, the US Congress allocated funding leading to the Comprehensive HHT Outcomes Registry of the United States (CHORUS), a prospective, 15-center longitudinal registry enrolling unselected patients with confirmed HHT. In this initial report, we describe findings from the first 600 participants, with a median age of 53 (range, 0-88) years and 60% female. Despite most participants developing typical HHT manifestations by age 13 years, the majority (63%) were not diagnosed until mid-to-late adulthood. Recurrent spontaneous epistaxis occurred in 95% of participants, chronic gastrointestinal bleeding in 30%, and heavy menstrual bleeding in 35% of postmenarche females, together resulting in moderate-to-severe mucosal bleeding in 76%. Iron deficiency and/or anemia were diagnosed in 68%, with 41% requiring IV iron and 25% requiring red cell transfusions. Serious complications of solid-organ arteriovenous malformations were frequent, including intracranial hemorrhage (3%), pulmonary hemorrhage (2%), venous thromboembolism (7%), arterial thromboembolism (11%), heart failure (7%), and pulmonary hypertension (7%). These data from CHORUS, the first national US registry of its kind, provide reliable, real-world estimates of the incidence, prevalence, and severity of numerous HHT manifestations and complications. HHT has a high burden of moderate-to-severe bleeding, anemia, thrombosis, and major neurologic and cardiopulmonary complications. There is a mean interval between first symptoms and diagnosis of >2 decades, during which substantial, serious, and preventable HHT morbidity, including early intracranial hemorrhage, may occur. This trial was registered at clinicaltrials.gov as NCT06259292.

The 2025 Voice AI Symposium represented a transition from conceptual research to clinical implementation in vocal biomarker science. Hosted by the NIH-funded Bridge2AI-Voice consortium, the meeting convened global experts to address the methodological, ethical, and translational challenges of integrating voice-based artificial intelligence (AI) into healthcare. This mini-review synthesizes symposium insights across six domains: multimodal integration, FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) data governance, clinical translation, interdisciplinary training, and cross-sector innovation. Research presented demonstrated voice as a latent, multimodal biomarker reflecting neurological, cardiopulmonary, and psychological states, while discussions emphasized ethical data practices and human-centered design. The implementation-focused panels underscored the importance of workflow alignment and usability for adoption in real-world care. Collectively, the symposium reflects a field advancing toward translational readiness and ethical accountability, positioning voice AI as a scalable, inclusive tool for next-generation healthcare.

ABSTRACT Objective The Minnesota Electronic Health Record Consortium (MNEHRC) was established during the early days of the COVID‐19 pandemic to provide data for public health surveillance from the eleven largest health care systems in Minnesota. Methods This is a descriptive study of the Consortium, which is a federated network that implements best practices for governance and data infrastructure to support public health surveillance and clinical research. We conducted an analysis of the Consortium members, governance structure, infrastructure, and the characteristics of the patient population. Results The Consortium health systems collect information from 105 hospitals, 773 clinics, 100 emergency departments and 29 040 providers. Information about the health systems and the demographic and clinical characteristics of its 5 471 367 patients is provided, which represents more than 90% of the patients in Minnesota. This manuscript also details the MNEHRC governance structure, working groups, data use agreements and technical infrastructure. The Consortium has produced several studies with state‐wide impact. One study, Health Trends Across Communities in Minnesota, is described in detail to illustrate aspects of this collaboration. Conclusion MNEHRC has been a successful collaboration and vital resource for public health surveillance in the state of Minnesota. Initially, the Consortium focused on surveillance related to COVID‐19 infections and vaccinations but has recently expanded into other public health and chronic disease research.

Abstract Objectives Traditional laboratory regulatory frameworks provide robust guidance for conventional clinical testing but lack requirements for data documentation for artificial intelligence and machine learning (AI/ML) solutions. This gap creates significant risks since AI solutions directly inherit patterns, biases, and limitations from their training data. Here, we describe the development and implementation of a comprehensive data documentation checklist with accompanying data lineage for use within an AI lifecycle framework for clinical laboratories. Methods Building on the previously established Clinical AI Readiness Evaluator (CARE) framework, we developed a comprehensive data checklist and data lineage methodology through a multiphase process, including (1) comprehensive review of existing AI/ML data documentation frameworks, (2) focused meetings with 3 institutional AI operations teams, and (3) 3 rounds of iterative refinement by our multidisciplinary team. The checklist’s effectiveness was then assessed using 3 diverse AI/ML projects moving through the CARE framework. Results The CARE Data Checklist and Data Lineage provide a structured approach to documenting critical aspects of datasets used in AI/ML projects, including data composition, demographics, collection methods, labeling processes, usage constraints, maintenance requirements, and a data readiness assessment. The checklist addresses unique data-centric challenges of AI/ML applications, facilitating transparency, reproducibility, and regulatory compliance. Conclusions The CARE Data Checklist and Data Lineage serve as both a technical guide and a communication tool bridging gaps between technical and clinical stakeholders. By working on these documents early in the AI lifecycle, laboratories can anticipate and address data-related challenges, ultimately saving time, optimizing resources, and improving the reliability of AI-augmented laboratory solutions.

Abstract Rare Mendelian disorders affect 300-400 million people globally. Although genetic testing has become widely adopted, gene-specific evidence for tailored variant interpretation remains scattered across resources. We present Gene Portals, a framework for gene-centered multimodal knowledge bases that co-localize expert-harmonized clinical data, functional assays, population variation, structural annotations and gene-specific ACMG/AMP specifications within a single resource. A modular interface integrates this unified evidence with VCEP-refined ACMG specifications to enable automated gene-specific variant classification, infer molecular mechanisms, and support cross-gene analyses. We demonstrate the framework’s utility across five Gene portals spanning eleven neurodevelopmental disorder-associated genes, integrating data from 4,423 individuals with 2,838 unique variants, 36,149 ClinVar submissions, and 1,044 expert-curated molecular readouts. By organizing evidence that is otherwise dispersed across multiple sources into a unified, queryable framework, the SCN, GRIN, CACNA1A, SATB2 and SLC6A1 Gene Portals became widely used community resources and provide an extensible template for standardized rare-disease variant interpretation and mechanism-aware discovery.