Citations

Abstract Background Breast cancer chemoprevention is underutilized among high-risk women. We examined whether decision support could increase informed decision making. Methods We conducted a cluster randomized controlled trial to evaluate the effect of a patient-facing decision aid (RealRisks) and provider tool (BNAV) compared with standard education on the number of women with high-risk breast lesions making informed choices. Questionnaires were administered at baseline and 6 and 12 months. The primary outcome was chemoprevention informed choice (defined as adequate knowledge and attitudes congruent with decision) at 6 months. Secondary endpoints included breast cancer risk perceptions, worry, chemoprevention knowledge, decision conflict, and chemoprevention decision. Results Across 31 randomized sites, we enrolled 210 providers, who were primarily breast health specialists. Of these, 24 sites (14 intervention, 10 control) enrolled 412 patients. Among 287 patients evaluable for the primary outcome, there were no statistically significant differences in informed choice between the intervention and control arms at 6 months (35% vs 27%, respectively; P = .20) and 12 months (37% vs 25%, respectively; P = .05). At 6 months, women in the intervention compared with control arm were more likely to have accurate breast cancer risk perceptions (31% vs 21%, respectively; P = .03) and adequate chemoprevention knowledge (33% vs 23%, respectively; P = .04). At 12 months, 52% of women in the control and 50% in the intervention arm self-reported initiating chemoprevention. Conclusion Decision support led to modest improvements in accurate risk perceptions and chemoprevention knowledge but not informed choice. Relatively high chemoprevention uptake was achieved among women with high-risk breast lesions managed mainly by breast health specialists. Trial Registration NCT04496739, https://clinicaltrials.gov/study/NCT04496739?cond=NCT04496739&viewType=Card&rank=1

Abstract Background Large language models (LLMs) offer promise for systematic review data extraction, but performance in complex multidisciplinary domains and utility for clinical statement generation remain insufficiently described. Objectives To evaluate Google NotebookLM for AI-assisted data extraction and RAND/UCLA consensus statement generation in a systematic review of IBD, obesity, and cardiometabolic comorbidities. Methods Studies were organized into domain-specific notebooks; structured prompts generated standardized evidence tables. Two independent reviewers validated outputs against full-text articles using a four-category error classification. Cell-level accuracy and critical accuracy (cells free of major factual errors) were the primary metrics; workflow time was compared against a published conventional extraction benchmark. Concordance between AI-generated and expert-finalized statements was assessed. Results Across 57 articles, 1,710 data cells were extracted; 151 (8.83%) were flagged, yielding 91.17% cell-level accuracy. Major factual errors occurred in only 4 cells (0.23%), for a critical accuracy of 99.77%. Most errors were minor omissions (59.6%) or incomplete extractions (30.5%); domain error rates ranged from 7.08% to 11.33%. The pipeline required 17.7 versus a projected 165.1 person-hours (89.3% reduction). PICO-structured prompting generated 70 candidate statements; 58 of 112 finalized panel statements (51.8%) were AI-derived, and 85.7% were retained in the finalized set. Conclusion Google NotebookLM demonstrates feasibility as a primary extraction and synthesis tool in a multidisciplinary systematic review, with extractive incompleteness as the principal limitation and substantial time savings over conventional approaches. Its novel application to RAND/UCLA consensus statement generation extends AI-assisted evidence synthesis to clinical consensus generation workflow.

Abstract Background Lysosomal dysfunction plays a crucial role in the pathogenesis of Parkinson’s disease (PD), particularly among GBA1 mutation carriers. Beyond GBA1 , genes such as TMEM175 , SCARB2 , and CTSB identified in genome-wide association studies (GWAS) are also implicated in lysosomal pathways contributing to PD risk, although their functional effects in patients remain unclear. Proteins encoded by these lysosome-related genes have been explored as potential therapeutic targets in experimental models. Biomarker profiles, including clinical measures, α-syn seeding activity, lysosomal proteins, and sphingolipids, may facilitate patient stratification and support therapeutic monitoring in future clinical trials. Aim The aim of this study is to investigate the impact of genetic variants of three lysosomal-related genes ( TMEM175 , SCARB2 , and CTSB ) on biomarker profiles in PD with and without GBA1 variants. Cross-sectional data from two German cohorts: the Tuebingen Parkinson Cohort (TUEPAC) and the DESCRIBE PD cohort of the German Center for Neurodegenerative Diseases were used as explorative cohorts, and data from Accelerating Medicines Partnership® Parkinson’s Disease (AMP-PD) were used as a validation cohort. The ultimate goal is to provide new data for patient stratification based on genetics, which might serve as a readout for target engagement and treatment efficiency assessment. Methods Three cohorts were analyzed: TUEPAC, DESCRIBE PD, and AMP-PD. TUEPAC and DESCRIBE PD were combined into a single German discovery cohort (TUEPAC-DESCRIBE-PD), while AMP-PD served as an independent validation cohort. Within each cohort, for subgroup analyses, PD patients were classified as the overall PD cohort (PD all ), and further stratified by GBA1 mutation status into PD patients without GBA1 mutations (PD GBA1 _wildtype ), and PD patients carrying GBA1 mutations (PD GBA1 ). We evaluated cognitive and motor function, as well as depression using the Montreal Cognitive Assessment (MoCA), Unified Parkinson Disease Rating Scale-part I II (UPDRS III), and Beck Depression Inventory-II(BDI-II) scales. Analyzed biomarkers included CSF α-syn seeding activity using seed amplification assay (SAA), CSF lysosomal protein levels of lysosomal integral membrane protein 2 (LIMP2), also known as SCARB2, cathepsin B (CTSB) and lysosome-associated membrane protein 2 (LAMP2), blood-based enzyme activity of the lysosomal glucocerebrosidase (GCase), and CSF sphingolipid profiles. PD patients carrying risk alleles in TMEM175 , SCARB2 , and CTSB were compared to non-carriers. Results Genotype-phenotype correlation analysis in TUEPAC-DESCRIBE-PD and AMP-PD revealed: (1) In PD all , the TMEM175 p.M393T risk variant was nominally associated with decreased cognitive function when adjusted for GBA1 mutation status in TUEPAC-DESCRIBE-PD; this association could not be replicated, although a similar trend was observed in the slightly smaller, but multicentric AMP-PD cohort; TMEM175 p.M393T was not significantly associated with BDI-I I or UPDRS-III scores in either cohort. (2) In PD GBA1 _wildtype , GCase activity was significantly lower in PD patients with SCARB2 rs6812193 risk allele in TUEPAC-DESCRIBE-PD, while a similar but non-significant trend was observed in AMP-PD; (3) In PD all , CSF levels of CTSB were nominally lower in carriers of CTSB rs1293298 risk allele compared to carriers of CTSB rs1293298 protective allele in TUEPAC-DESCRIBE-PD; in PD GBA1 _wildtype , LAMP2 was significantly lower in carriers of CTSB rs1293298 risk allele compared to carriers of CTSB rs1293298 protective allele in TUEPAC-DESCRIBE-PD; (4) In PD all , TMEM175 p.M393T risk allele was nominally associated with altered sphingolipid profiles across both TUEPAC-DESCRIBE-PD and AMP-PD cohorts. Conclusion These findings demonstrate that genetic variants in lysosomal-related genes ( TMEM175 , SCARB2 , and CTSB ) have a functional impact on biomarker profiles in PD patients. Integrating genetic characterization with biochemical profiling provides a framework for patient stratification and may serve as a translational strategy to monitor target engagement and evaluate treatment efficacy in future clinical trials.

Abstract Background New-onset atrial fibrillation (AF) is common in patients who are hospitalized with acute illness and is associated with overall poor outcomes. Current clinical and demographic predictors of new-onset AF have limitations. Electrocardiogram (ECG) waveforms, combined with these factors as part of multimodal approach could help improve prediction of new-onset AF. Purpose The purpose of our study was to develop a multimodal, deep-learning system that combines ECG waveforms with clinical data for prediction of new-onset AF in hospitalized patients. Methods We developed and compared two separate 1-dimensional models with a fusion model for prediction of new-onset AF. (a): one-dimensional convolutional neural network (1D-CNN) that learns patterns from the raw ECG waveforms. (b): a Multi-Layer Perceptron (MLP), unsupervised machine learning model which utilizes data from structured variables such as: age, heart rate, routinely obtained laboratory data and medications. The two modules each produce latent feature vectors, which are then placed end-to-end and fed into a final Multi- MLP head that outputs the probability of New-onset AF during hospitalization. We then compared this fused MLP model with the two simpler unimodal models based solely on ECG waveforms and clinical data respectively. Results On the held-out test set data, the feature-level fused model achieved an Area Under the Curve (AUC) of 0.81. This significantly outperformed both the ECG-alone model (0.77) and the clinical-alone model (0.74). By adjusting the threshold, we can fine-tune the tool at a threshold of 0.5, it flags 83% of patients who later develop AF (207 of 248) with a precision of 10%, a setting that could be useful for wider screening. By adjusting the threshold of 0.98 for a high-precision alert configuration, it has the ability to predict 46% of future occurrence of AF cases (113 of 248) while reducing false alarms by 81% (from 1,797 to 328), increasing precision to 26% and achieving an overall accuracy of 89%, a setting useful when resources for follow-up are limited. Conclusions Combining ECG signals with clinical data significantly enhances the model’s ability to detect new-onset AF in hospitalized patients as compared to a uni-dimensional CNN model. The dynamic nature of ML model also allows for adjustment of its decision threshold which can aid in broader screening and improving adverse outcomes related to occurrence of AF.ROC Curve for Fusion model

Abstract Background Type 8 long QT syndrome (LQT8) is characterised by prolonged QT interval and a high burden of ventricular repolarisation abnormalities including sudden cardiac death. Purpose Patients and clinicians face uncertainties about the daily risk of cardiac arrest, particularly during physical performance. Therefore, we aim to establish a risk assessment based on systematically collected data. Methods We analysed eight patients genetically confirmed to carry CACNA1C variants, of whom three had cardiac arrests in early childhood, while others were discovered incidentally. One patient met criteria for cardiac-only Timothy syndrome. All patients performed cardiopulmonary exercise testing (CPET) with several having multiple studies over time, resulting in a total of 19 CPETs. Echocardiograms were available for all patients, and Holter monitor results for all but one patient. All CPETs were conducted on beta-blockade. Results On resting ECG the average heart rate was 66.5 ± 10.7 beats per minute (bpm). Average QT at diagnosis was 476.8 ± 47.8 ms, with a corrected QTc of 487.9 ± 37.2 ms. Two patients had sinus arrhythmia and three were atrially paced at rest. No ventricular arrhythmias were observed at rest. On initial CPET, average peak oxygen uptake was 29.3 ± 8.5 ml/kg/min (82 ± 25% predicted) at a peak heart rate of 141.6 ± 24.3 bpm (73 ± 13% predicted). Peak respiratory exchange ratio averaged 1.09 ± 0.13. On initial CPET, four patients developed premature ventricular contractions (PVCs), but only in one patient were more than 5 per minute detected. That patient had a high PVC burden with multiple runs of non-sustained ventricular tachycardia. There were no episodes of syncope or cardiac arrest across all 19 CPETs. Conclusions Our cases demonstrate the safety and utility of exercise testing in CACNA1C-related disease. Longitudinal follow-up revealed diverse electrophysiological findings, including QT prolongation and reduced chronotropic response. From our data it is suggested that CPET is safe to perform on appropriate therapy and exercise should be encouraged.

Abstract Aims Transcatheter mitral valve replacement (TMVR) with the Tendyne Mitral Valve System is a treatment option for patients with severe symptomatic mitral regurgitation (MR) unsuitable for conventional mitral valve surgery or transcatheter edge-to-edge repair (TEER). This study sought to evaluate the safety and effectiveness of TMVR through 5-year follow-up. Methods and Results The Tendyne Expanded Clinical Study is a prospective, single-arm, multicentre study that enrolled patients between November 2014 and June 2020. The study enrolled 191 patients (mean age 74.1 ± 8.0 years, 62.8% male, 70.2% New York Heart Association Class (NYHA) class III/IV, 88.5% secondary MR), of whom 186 (97.4%) underwent TMVR with Tendyne. MR grade decreased from ≥3+ in 99.5% patients at baseline to grade ≤1+ in 95.3% at 5 years. In those patients surviving to 5 years (n = 49), durable symptomatic improvement was evidenced by 73.5% patients being in NYHA class I/II at 5 years. The Kansas City Cardiomyopathy Questionnaire overall summary score increased from 48.5 ± 22.5 points at baseline to 67.6 ± 21.5 points at 5 years. Serious adverse events that occurred through 5 years included life-threatening bleeding (11.5%), fatal bleeding (2.6%), renal insufficiency/failure (28.8%), endocarditis (7.3%), device thrombosis (5.8%), and new onset atrial fibrillation (13.1%). No structural device degeneration or device embolism occurred through 5 years. Conclusions The Tendyne TMVR was effective at achieving immediate and sustainable elimination of MR, which was associated with symptomatic improvement and absence of structural valve degeneration in a high-risk cohort over 5 years. These findings support TMVR with the Tendyne System as an alternative for patients with appropriate mitral valve anatomy and symptomatic secondary MR unsuitable for mitral surgery or TEER.

Abstract The purpose of this study was to explore epigenetic dysregulation resulting from deleterious SPEN mutations in diffuse large B cell lymphoma (DLBCL). Truncating (tr) SPEN mutations are among defining variants in LymphGen:BN2 and DLBclass:C1. We previously found SPEN mutations enriched in DLBCL that failed to reach EFS24, and inferior survival was observed in SPEN-tr vs all other BN2 and C1. Differential gene expression (DEG) in these groups identified >1000 DEGs including upregulation of IKZF3 and BCL2 and downregulation of MHC-II, overall suggesting an alternative, aggressive biology for a subset of BN2/C1 cases generally thought to have favorable outcomes. In healthy cells, SPEN is a dynamic epigenetic regulator with roles in chromatin formation and as a NOTCH co-repressor. We hypothesized that loss of SPEN reprograms the epigenetic landscape to drive the aggressive phenotype in affected DLBCL. Methods: OCI-LY3 cell line with CRISPR/Cas9-induced SPEN-tr mutations was analyzed by ATAC and RNA-seq. Diagnostic DLBCL FFPE tumor biopsies from the Molecular Epidemiology Resource (MER) were analyzed by 850k/EPIC methylation array (n=164). SPEN-depleted OCI-LY3 cells displayed significant (|Log2FC|>2) peaks of open (n=3083) and closed (n=1729) chromatin indicative of systemic epigenetic reprogramming, with enriched peaks (FDR<0.01, |Log2FC|>2) converging on pro-survival and immune evasion (ATP11C, BCL11B, RAG1/2 genes) and negatively enriched for cell cycle and apoptotic pathways (CDK6, TP63, CD80 genes). Chromatin accessibility featured enrichment for TCF3/4 and ZEB1 binding motifs and depletion of EBF1, REL, and NFKB1/2 motifs (FDR<0.0001, |Z-score|>5), suggesting a shift to an activated, post-germinal center program (via open TCF3/4 motifs) with reduced reliance on NF-kB signaling. Footprinting analysis further revealed decreased occupancy at NFKB2 and REL/B motifs, and reduced occupancy at HES1 with elevated occupancy at Rbpjl motifs (p<0.05, |Score|>0.2) indicating downregulation of NF-kB is in favor of non-canonical Notch transcription programming. Merging gene expression and accessibility, gene-peak concordance was observed for elevated CXCR4 and IL6 expression and decreased SOCS1, CD274 (PD-L1), and CCND1 expression (FDR<0.01, |Log2FC|>1), suggesting expression programs feature uncontrolled JAK/STAT signaling (via high IL6, low SOCS1) with immune evasion mechanisms independent of PD-L1. By methylation array, strong epigenetic reprogramming was observed in SPEN-tr DLBCL (n=8), where differential methylation analysis vs WT (n=146), vs BN2 (n=4), and vs C1 (n=15) samples revealed hypermethylation signatures (p<0.05, |FC|>1) indicative of reduced cell-cell signaling and antigen presentation pathways (including HLA-A/G/H). In conclusion, SPEN-depletion triggers systemic epigenetic reprogramming to enhance immune evasion and non-canonical Notch target genes. SPEN-tr DLBCL should be considered with heightened risk estimation and as prime candidates for personalized treatments beyond standard therapy. Citation Format: Janek S. Walker, Joseph P. Novak, Abigail R. Dropik, Zilin Xianyu, Michelle K. Manske, Matthew J. Maurer, Eric Mou, Rebecca L. King, Stephen M. Ansell, Thomas M. Habermann, Thomas E. Witzig, James R. Cerhan, Anne J. Novak. Deleterious SPEN mutations induce epigenetic reprogramming to promote survival and enhance immune evasion pathways in an aggressive subset of BN2/C1 DLBCL tumors [abstract]. In: Proceedings of the Fifth AACR International Meeting on Advances in Malignant Lymphoma: From Discovery to Clinical Impact; 2026 Jun 24-27; Philadelphia, PA. Philadelphia (PA): AACR; Blood Cancer Discov 2026;7(3_Suppl):Abstract nr A006.

This cohort study examines patterns of bone-modifying agent use from 2013 to 2024 among men with metastatic castration-resistant prostate cancer.