Citations

ABSTRACT Patients with chronic thromboembolic pulmonary disease (CTEPD) without resting pulmonary hypertension (PH) frequently experience dyspnea which is not explained by stationary assessment and therefore requires cardiopulmonary exercise testing for further diagnostic insight. Ventilatory inefficiency in CTEPD is commonly identified; however, consensus definitions are lacking. Upright iCPET and careful definition of ventilatory inefficiency (i.e., lack of reduction in VD/VT with exercise) may allow optimal characterization of patients with chronic thromboembolic disease and specifically helps correctly identify patients with preload insufficiency who may otherwise be misclassified or undiagnosed.

Abstract Scientific foundation models learn high-dimensional representations from diverse data modalities, yet what they encode and how to extract that knowledge remain open questions. Here we show that probing the internal representations of Evo 2, a 7-billion-parameter genomic foundation model, enables accurate and interpretable genetic variant effect prediction. We introduce a covariance-based probe that captures second-order structure from Evo 2 sequence embeddings to predict variant pathogenicity across variant types and functional consequences, matching or exceeding specialized predictors within their domains. To ground these predictions in known biological mechanisms, we train a complementary panel of probes on existing annotations to detect which genomic properties are disrupted by a variant. This categorized evidence is then integrated with each variant’s genomic context through a language model to generate variant-specific mechanistic hypotheses. Our pathogenicity predictions correlate with experimental measures of variant function, clinical penetrance, and biobank disease associations while the mechanistic hypotheses are consistent with expert reviews, known mechanism classes, and downstream molecular readouts. We release pathogenicity scores, disruption profiles, and contextualized interpretations for 4.2 million variants from the ClinVar database as an open resource through the Evo Variant Effect Explorer (EVEE). More broadly, this structured probing approach offers a general framework for interrogating foundation models across scientific disciplines and grounding their outputs in existing domain concepts.

ABSTRACT Background Quality of life and symptom burden of patients with myelofibrosis are well recognized and compounded in those with anemia; however, the effects of transfusion burden or anemia severity on quality of life have not been comprehensively characterized. This post hoc descriptive analysis explored the association between transfusion status or hemoglobin improvement and patient‐reported outcomes (PROs). Methods The analysis used pooled populations across treatment arms from 3 clinical trials (SIMPLIFY‐1, SIMPLIFY‐2, MOMENTUM); sample sizes for each PRO measure were dependent on the trials in which they were administered. Results At both baseline and week 24, transfusion independence was associated with umerically greater mean SF ‐36v2 and EORTC QLQ ‐ C30 scores than transfusion dependence; in the subgroup that was transfusion dependent at baseline, those who achieved transfusion independence at week 24 had greater PRO improvements than those who remained reliant on transfusions. Regardless of transfusion status, patients who achieved a hemoglobin improvement ≥ 1, ≥ 1.5, or ≥ 2 g/ dL from baseline also had clinically meaningful improvements in quality of life (assessed via mean EQ ‐ 5D ‐ 5L or SF ‐36v2 scores) and symptoms (assessed via PGIC or MPN ‐ SAF / MFSAF Total Symptom Score) at week 24 compared with those who did not. Conclusions Collectively, these results provide preliminary insights into the associations of transfusion status and anemia severity with quality of life in myelofibrosis; as current PRO measures do not directly evaluate the relationship between symptoms such as fatigue and anemia, development of new measures to more comprehensively capture the patient experience for those with anemia in myelofibrosis may be warranted. Trial Registration NCT01969838, NCT02101268, NCT04173494

Abstract The Bridging the Gaps (BTG) in Leukemia, Lymphoma and Multiple Myeloma Consensus Conference 2025 brought together a multidisciplinary group of oncology experts to address the complexities of lymphoma management, focusing on mantle cell lymphoma (MCL), chronic lymphocytic leukemia (CLL), and diffuse large B‐cell lymphoma (DLBCL). This article presents consensus recommendations developed through a modified Delphi process, which emphasize the need for tailored therapeutic strategies in light of recent advancements in treatment options. Key recommendations include the screening for high‐risk features in MCL, use of the BOVen regimen (zanubrutinib, obinutuzumab, and venetoclax) for TP53 ‐aberrant cases, and integration of chimeric antigen receptor T‐cell therapy for patients with mantle cell lymphoma that is refractory to covalent Bruton tyrosine kinase inhibitors. For CLL, recommendations include consideration of time‐limited therapies for younger patients and a “watch and wait” strategy for asymptomatic patients despite the improved activity and safety of current treatment regimens. For DLBCL, this article highlights the challenges in treatment sequencing and the role of circulating tumor DNA and minimal residual disease testing in monitoring disease progression. Overall, the conference describes the importance of ongoing research to refine management strategies and improve patient outcomes in lymphoma care, addressing the gaps in clinical practice where high‐level evidence is lacking.

Background Acute kidney injury (AKI) is associated with high morbidity and mortality in critically ill patients. Multicenter analyses describing the epidemiology of AKI in the cardiac intensive care unit (CICU) are scarce. Here, we describe the prevalence, predictors, and outcomes associated with AKI in a contemporary multicenter CICU population. Methods The Critical Care Cardiology Trials Network (CCCTN) is a collaboration of CICUs in North America coordinated by the TIMI Study Group (Boston, MA). We evaluated patient and hospital-level outcomes as a function of creatinine-only Kidney Disease: Improving Global Outcomes (KDIGO) AKI stage within the CCCTN registry. Logistic regression was used to assess associations with in-hospital mortality. Results Among 21 603 admissions, the overall prevalence of AKI was 23.7%, which was composed of KDIGO stage 1: 12.7%, stage 2: 1.7%, and stage 3: 9.2%. A higher prevalence and severity of AKI were seen in patients with baseline kidney disease, hypertension, or diabetes and in admissions with heart failure, cardiogenic shock, and cardiac arrest ( P < .001 for all). There was a stepwise increase in hospital mortality across AKI stages (stage 1: 23%, stage 2: 39%, stage 3: 42%; P < .0001). The increased risk of death associated with progressive AKI stage persisted after multivariate adjustment (adjusted OR: stage 1: 2.1, stage 2: 3.7, stage 3: 3.4). Conclusions AKI occurs in 1 in 4 admissions to the CICU, and a higher AKI stage is incrementally associated with in-hospital mortality, highlighting a need to develop strategies to mitigate AKI and its complications.

Abstract Intercellular communication between multiple myeloma (MM) cells and osteoclast precursor cells (pre-OCs) contributes extensively to the occurrence and development of myeloma-related bone destruction. However, key interacting modes and the exchanged substances involved in this communication remain unclear. In this study, we discover that tunneling nanotubes (TNTs) directly connect MM and pre-OCs. Using the stable isotope labeling with amino acids in cell culture assay and a positive-negative double selection strategy, we identify filamin A (FLNA) as a major protein transported from MM to pre-OCs. FLNA acts as a molecular clutch linking extracellular matrix–bound MAC1 to the cytoskeleton, activating Rho and MAPK signaling pathways and promoting F-actin polymerization, which subsequently enhances osteoclast differentiation by modulating cellular stiffness, traction force, and deformability. In addition, FLNA directly binds vinculin and promotes its recruitment to podosomes, thereby enhancing the functions of podosomes and the bone resorption capacity of osteoclasts. The conditional depletion of Flna in mice suppresses podosome activity and reduces stiffness, traction force, and deformability in pre-OCs, leading to significantly impaired osteoclast differentiation and increased bone mass. In the Vk∗MYC mouse model of myeloma, the administration of the TNT inhibitor latrunculin B disrupts FLNA transport to osteoclasts and alleviates osteolytic bone disease. These findings highlight the critical role of MM-transferred FLNA in osteoclastogenesis and suggest that targeting TNTs may represent a therapeutic strategy to limit pathological bone resorption associated with MM.

Introduction Angina with no obstructive coronary artery disease (ANOCA) affects millions and is frequently under-recognised because diagnostic pathways and risk tools predominantly target obstructive coronary artery disease (CAD). This protocol describes shared methods for two machine-learning (ML) studies: (1) differentiating ANOCA from stable angina with obstructive CAD and (2) predicting long-term mortality among patients with ANOCA and obstructive CAD. Methods and analysis We will develop and cross-site validate ML classification models using a multicentre retrospective cohort drawn from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease registry and institutional datasets from the University of Ottawa Heart Institute and the University Health Network. Eligible participants are adults (≥18 years) undergoing initial cardiac catheterisation for chest pain/anginal equivalents since 1995, excluding prior revascularisation, major structural heart disease and predefined non-anginal indications. Outcomes are (1) ANOCA (0% to <50% stenosis) versus obstructive CAD (≥50% stenosis) and (2) 1, 3 and 5-year mortality, modelled separately for ANOCA and obstructive CAD. Model development will use nested cross-validation with stratified k-fold inner-loop tuning and leave-one-site-out cross-validation for repeated external validation. Candidate predictors will be harmonised across sites, filtered for missingness and refined using expert/directed acyclic graph-guided selection plus Boruta and Least Absolute Shrinkage and Selection Operator. Preprocessing includes appropriate encoding, missing-data imputation (multivariate imputation by chained equations) and feature scaling. Algorithms will include elastic-net logistic regression, random forest, LightGBM and multilayer perceptron models; hyperparameters will be optimised via Bayesian optimisation. Performance and threshold tuning will be reported. Explainability and subgroup fairness will be assessed using SHapley Additive exPlanations. Final models will be deployed as a web-based clinical risk calculator. Ethics and dissemination Ethics approval has been obtained from the University of Calgary and the University Health Network (#24-5916). Analyses will use deidentified data in secure environments; only aggregate results will be reported. Findings will be disseminated via peer-reviewed publications, conferences and a web-based calculator.