Citations

Abstract Frailty predicts adverse outcomes in multiple myeloma (MM), yet an objective frailty biomarker is lacking. Circulating senescence-associated secretory phenotype (SASP) factors correlate with frailty and adverse outcomes in chronic diseases, but their relevance in MM is unclear. In this study, we investigated the relationship between 36 plasma SASP factors, frailty, and clinical outcomes in: (1) a historical cohort using stored specimens from 59 patients with newly diagnosed MM, and (2) a prospective cohort of 36 patients initiating new MM treatment. Several SASP factors were significantly associated with frailty after adjusting for age, sex, and body mass index; GDF-15, TNF-RI, IL-6, and IL-8 showed positive correlations with both cumulative-deficit based and physical frailty indices. Additionally, significant age-adjusted associations were observed between SASP factors and treatment outcomes, including overall survival (OS), high-grade toxicity, and healthcare utilization. We developed a 5-factor SASP model that demonstrated superior predictive performance for OS compared to chronological age, R-ISS stage, and clinical frailty, and TNF-RI alone (5-year OS AUC: 0.90). These findings highlight the association between senescence biomarkers and frailty and OS, and support further investigation of SASP factors as objective frailty biomarkers in MM.

ABSTRACT The HLA‐C*03:31 allele is characterised by a nucleotide substitution at genomic DNA position 400 in exon 2.

ABSTRACT Myelofibrosis is characterized by perturbation of the JAK/STAT pathway and upregulation of anti‐apoptotic factors leading to myeloproliferation, bone marrow fibrosis (BMF), extramedullary hematopoiesis, splenomegaly, and cytopenias. Navitoclax, a potent oral B‐cell lymphoma (BCL)‐X L /BCL‐2 inhibitor, promotes apoptosis of malignant myelofibrosis cells. Herein, we present results of Cohort 3 of the Phase 2 REFINE study (NCT03222609), which evaluated efficacy and safety of navitoclax plus ruxolitinib in JAKi‐naïve patients with myelofibrosis. JAKi‐naïve patients with primary or secondary myelofibrosis (≥ 18 years with splenomegaly, DIPSS intermediate‐2 and high‐risk myelofibrosis, and ECOG 0–2) and platelet count > 100 × 10 9 /L were enrolled and treated with navitoclax 100 mg once daily (QD) or 200 mg QD according to platelet count (≤ 150 × 10 9 /L or > 150 × 10 9 /L, respectively). Ruxolitinib was given twice daily (dose per label). Primary endpoint: spleen volume reduction of ≥ 35% (SVR 35 ) at week 24. Secondary endpoints: ≥ 50% reduction in total symptom score (TSS 50 ) at week 24, change in grade of BMF, anemia response, and safety. Thirty‐two patients received ≥ 1 dose of navitoclax plus ruxolitinib. Median (range) duration of follow‐up was 44 months (5–58). 63% (20/32) of patients achieved SVR 35 at week 24; median (range) time to first SVR 35 was 12 weeks (11─48). Of 24 evaluable patients, 21% achieved ≥ 50% reduction in driver gene variant allele frequency (VAF). Of 27 evaluable patients, 11 (41%) achieved TSS 50 at week 24; median (range) time to first TSS 50 of 3 weeks (0─16). BMF improved from baseline by ≥ 1 grade in 13/27 patients (48%) at any time on study. Anemia response rates were 38% (5/13) for transfusion‐independent and 100% (2/2) for transfusion‐dependent patients. No bleeding events or deaths were attributed to navitoclax. These findings suggest navitoclax plus ruxolitinib has a tolerable safety profile and provides clinically meaningful improvements for JAKi‐naïve patients with myelofibrosis. Trial Registration NCT03222609.

Abstract Echocardiography is the most widely used cardiac imaging modality, yet artificial intelligence-enabled interpretation remains limited by the inability of existing models to integrate visual assessment, quantitative measurement, and clinical reasoning within a unified framework. Here we present EchoAtlas, the first autoregressive vision-language model developed for echocardiographic interpretation. Trained on over 12.9 million question-answer pairs derived from approximately 2 million echocardiogram videos, EchoAtlas achieves 0.966 accuracy on multiple-choice questions in our internal test set and establishes a new state-of-the-art on the public MIMIC-EchoQA benchmark (0.699 vs. 0.508 previously). EchoAtlas also provides accurate quantitative measurements, segment-level regional wall motion assessment, longitudinal comparison, and diagnostic reasoning across diverse question formats — capabilities not previously demonstrated in this domain. These results highlight the potential of autoregressive vision-language models as a foundation for interactive echocardiographic interpretation, representing an early step toward scalable, auditable artificial intelligence systems in cardiology practice.

BACKGROUND: For reasons not fully understood, Black adults experience more socioeconomic disadvantages (SED) than their White counterparts, as well as earlier onset and greater mortality from heart failure (HF). The John Henryism hypothesis predicts that repeated high-effort coping, that is, John Henryism (JH), with socioeconomic adversity can accelerate cardiovascular aging, thus increasing the risk for HF. METHODS: The analysis sample consisted of participants from the JHS (Jackson Heart Study), a cohort of Black adults from the Jackson, Mississippi Metropolitan area. The analysis sample included participants with no cardiovascular disease at baseline (2000–2004) with complete SED and JH scores. Indicators of SED included low household income, low educational attainment, and low maternal educational attainment scores. JH scores (0–36) were categorized as low (<28), moderate (29–32), and high (>32). Effect moderation of JH in the SED and incident HF association was assessed using interaction terms and stratification. Proportional hazards regression determined the hazard ratio and 95% CI, and models adjusted for age, sex, and established lifestyle risk factors. RESULTS: Among 1704 participants (mean age: 52.15 years, 64.3% female), 100 HF events occurred by 2016 (mean follow-up, 10 years). A statistically significant interaction between income and JH was observed for incident HF ( P =0.04). For every 1-unit increase in income disadvantage score, the risk of HF increased 2-fold among those with high JH (hazard ratio, 2.00 [95% CI, 1.39–2.86]) after full adjustment. Among participants with low JH, the corresponding unadjusted association was hazard ratio, 1.40 [95% CI, 1.04–1.90], but this association attenuated after adjusting for age and sex (hazard ratio, 1.19 [95% CI, 0.87–1.63]). CONCLUSIONS: The association between income disadvantage and HF differed by JH level. Because SED and high JH tend to co-occur, both should be considered in future research aiming to decrease the burden of HF in Black Americans.

Foundation models, known as the large-scale, pretrained models capable of generalizing across diverse tasks, have significantly advanced the field of medical image analysis. While most early applications focused on 2D modalities, the unique challenges and opportunities associated with volumetric medical imaging have recently attracted growing interest. This study provides a comprehensive overview of the current landscape of foundation models tailored for volumetric medical image analysis, with a focus on CT, MRI, and PET imaging. We examine key components of these models, including 3D architectures, training strategies, and supported modalities. In addition, we highlight their contribution to major clinical tasks such as classification and prediction, segmentation, image registration, quality enhancement, and visual question answering. Critical challenges of these models, including high computational cost, limited and less diverse 3D datasets, and domain adaptation, are discussed alongside the promising solutions and future research directions. By synthesizing recent advances in volumetric foundation models and outlining key technical and clinical challenges, this review provides a thorough roadmap toward the development of scalable, generalizable, and clinically applicable AI systems for volumetric medical images.