Citations

Abstract Aims Coronary angiography might contain clinically relevant information, beyond its traditional role in delineating coronary artery disease. We sought to explore the role of deep learning (DL) in detecting right ventricular dysfunction from routinely acquired cine images during coronary angiography. Methods and results 3D-convolutional neural networks (CNN) were trained to identify right ventricle (RV) dysfunction from diagnostic angiography of the right coronary artery (RCA). The model’s input was two cine angiograms from LAO and RAO projections. The model’s output was normal vs. abnormal RV function. Ground truth on RV function was obtained from the nearest transthoracic echocardiogram to the coronary angiogram. Two analyses were performed to identify (i) any RV dysfunction (≥mild) and (ii) significant RV dysfunction (≥mild–moderate). A total of 10 336 coronary angiograms from 9849 patients (mean age 66, 36% women) were included. The cohort was split into training (70%), validation (15%), and testing (15%) sets. In the testing sets, the models attained an area under the ROC curve (AUC), sensitivity, and specificity of 0.82 (95% CI: 0.80–0.84), 0.75, and 0.74, for detecting any RV dysfunction, and 0.83 (95% CI: 0.80–0.86), 0.82, and 0.70 for detecting significant (≥mild–moderate) RV dysfunction. Combining an ECG-driven AI model with the angio-driven model improved the AUC values to 0.83 and 0.87 for detecting any or advanced RV dysfunction, respectively. Conclusion A novel deep learning algorithm yielded acceptable classification accuracy in detecting RV dysfunction from routine cine angiography of the RCA. The model’s performance improved further by adding ECG to its inputs.

ABSTRACT Obstructive sleep apnea (OSA) is a common, heritable disorder with diverse clinical presentations and etiologies. We conducted a genome-wide association meta-analysis of 492,107 individuals, including 46,028 OSA cases, identifying 14 genome-wide significant loci, eight of which are novel. Analyses adjusting for body mass index (BMI) revealed three loci independent of obesity, implicating alternative biological pathways contributing to disease. Integrative functional analyses, including chromatin interaction mapping, fine-mapping, and eQTL colocalization, prioritized candidate effector genes. Notably, implicated genes in chondrocytes were associated with craniofacial morphology in both mouse and human multivariate genotype-phenotype mapping, supporting a role for craniofacial structure in OSA risk. These findings highlight key genetic pathways (obesity-related, neurological, and craniofacial) underlying the development of OSA, offering new insights into its complex etiology.

BACKGROUND: Cardiac radioablation is emerging as a treatment modality for refractory ventricular tachycardia. This study aimed to evaluate the effects of radiation on the coronary arteries in a swine model of proton beam cardiac radioablation. METHODS: Eighteen swine underwent single-fraction 30 to 40 Gy pencil-beam scanning proton therapy targeting the left ventricle and were euthanized 12 to 40 weeks later. Treatment planning was performed without restricting the dose to the coronary arteries. The maximum point dose (D max ) to the epicardial coronary arteries was calculated. In secondary analyses, the mean (D mean ) and minimum dose received by the highest irradiated 0.01 cm 3 (D 0.01 ) of each coronary artery were also calculated. Coronary artery segments were harvested from the D max sites for histological analysis, and the D max was correlated with stenosis severity. RESULTS: Ninety-six coronary arteries were analyzed. No stenoses were observed by computed tomography imaging preirradiation. By histological analysis posteuthanasia, 25/96 (26%) coronaries sampled at their D max sites had ≥75% stenosis. The median D max was 4.7 Gy for the <75% stenosis group and 29.7 Gy for the ≥75% stenosis group ( P <0.001). The AUC-ROC for the association between D max and stenosis ≥75% was 92.2%. A D max value of 20.1 Gy best predicted stenosis ≥75%, with sensitivity 92.3% and specificity 87.1%. The AUC-ROCs for the associations of D mean and D 0.01 with stenosis ≥75% were 84.8% and 91.6%, respectively. In histopathologic analysis, intimal hyperplasia was the most common coronary artery abnormality at the D max sites, and it was present in 61.5% of all arteries and in 93.9% of arteries with D max ≥20 Gy. CONCLUSIONS: In this preclinical model of proton beam cardiac radioablation, coronary stenoses occurred in a dose-dependent manner, with D max showing the closest correlation with stenosis ≥75%. These data provide for the first time a framework for dose constraint considerations for the coronary arteries during treatment planning for cardiac radioablation and thoracic malignancy radiation.

Abstract Geroscience research benefits from interdisciplinary approaches, team science, and collaborations, which collectively facilitate the discovery of aging mechanisms and their translation into tangible, clinical interventions. Since its inception in 2019, the Midwest Aging Consortium (MAC) has provided an engaging platform for aging researchers in the United States’ Midwest to connect, collaborate, and exchange ideas. The Sixth Annual Research Symposium of the MAC held at the Mayo Clinic in Rochester, Minnesota, in April 2025 highlighted the continued impact of the MAC in bringing together aging researchers, including many trainees and early career investigators, into a collaborative environment. This record-setting event featured interdisciplinary research on key aging mechanisms, including lipid metabolism, mitochondrial dysfunction, stress response, cellular senescence, and immune adaptations across organ systems. New therapeutic concepts and clinical trial approaches were presented. Cutting-edge methodologies including single-cell and spatial transcriptomics, metabolomics, and organoid cultures, to dissect aging process in tissue-specific and systemic contexts also were presented. Overall, the MAC symposium underscored the translational potential of geroscience and reinforced the MAC’s mission to accelerate aging research through regional collaborations and innovation.

Abstract Background Astroblastomas are rare brain tumors predominantly affecting children and young adults, for which molecular subtypes and clinical management remain undefined. Methods We analyzed tumor samples, molecular profiles, and clinical data from 200 patients, classified as “Astroblastoma, MN1-altered” under WHO criteria, using DNA methylation profiling, DNA/RNA profiling/sequencing, and survival analyses. Results DNA methylation analyses identified 3 groups: Group A (n = 143, characterized by MN1::BEND2 fusions, predominantly supratentorial location, with striking female predominance and favorable survival); Group B (n = 37, epigenetically and transcriptionally closely related to Group A, but characterized by EWSR1::BEND2 fusions, with spinal and infratentorial locations and poor prognosis); and Group C (n = 20, epigenetically and transcriptionally distinct, characterized by MN1::CXXC5 fusions, exclusively supratentorially located, with favorable survival). Progression-free and overall survival were significantly shorter in Group B (5-year PFS 14%; 10-year OS 54%) compared to A (5-year PFS 47%; 10-year OS 89%) and C (5-year PFS 75%; 10-year OS 89%). Radiotherapy improved PFS in Group B (hazard ratio 0.25), while no clear benefit was identified for Groups A and C. Conclusions Astroblastoma, MN1-altered, comprises 3 molecularly and clinically distinct groups, characterized by different fusion genes, including those without MN1. These new insights, including the identification of potential predictive biomarkers like 14q/16q loss, provide a framework for the development of risk-stratified therapeutic approaches. Importantly, we identified a molecularly defined high-risk group that benefits from radiation therapy. Our findings redefine Astroblastoma as a molecularly diverse tumor type, propose a refined classification, support the development of risk-adapted therapeutic strategies and provide a rational standard of care.

PURPOSE To compare patient-reported and clinical outcomes between radical cystectomy (RC) and bladder-sparing therapy (BST) in patients with recurrent high-grade non–muscle-invasive bladder cancer (NMIBC). PATIENTS AND METHODS This pragmatic, prospective observational cohort study was designed with patients, who selected and prioritized outcomes. Eligible adults were candidates for both RC or BST, had previous induction Bacillus Calmette-Guérin (BCG), and received their last treatment within 12 months. The primary outcome was the EORTC-QLQ-C30 physical function scale at 12 months. Secondary outcomes included other EORTC-QLQ-C30 scales, depression, anxiety, bladder cancer–specific quality of life (QOL), financial burden, and cancer-specific outcomes. Targeted maximum likelihood estimation (TMLE) was used to calculate average treatment effect (ATE) estimates between arms. Inverse probability weighted risk ratios (wRR) were calculated using quasi-Poisson regression. RESULTS Of 570 participants (mean age 71.4 years; 21% female), 371 selected BST and 199 selected RC. Physical function was significantly worse in the RC arm at 3 months; by 9 months, there was no difference between arms, and at 12 months, physical function did not differ (ATE, 0.9; 95% CI, –0.6 to 2.4; P = .22). RC was associated with better emotional function, generic health-related QOL, and financial burden, and lower depression and anxiety, while BST was associated with better bowel and sexual health. Cancer-specific survival was 99% for BST versus 96% for RC (wRR, 0.99; 95% CI, 0.97 to 1.01). RC was associated with a higher risk of adverse events and serious adverse events, including a 90-day mortality rate of 2.5%. CONCLUSION Most patient-prioritized outcomes were similar or better among participants who chose RC compared with BST. These findings support the continued role of RC in managing recurrent high-grade NMIBC.

ABSTRACT Chimeric antigen receptor T-cell (CART) and T-cell engager (TCE) therapies targeting B-cell maturation antigen (BCMA) are transforming the treatment landscape for relapsed multiple myeloma (MM). However, despite impressive initial response rates, most patients eventually relapse. To investigate this unmet medical need, we applied whole-genome sequencing (WGS) to MM cells from cohorts of 102 relapsed patients treated with anti-BCMA CART and TCE therapies. Several genomic alterations were associated with clinical outcomes, particularly primary refractoriness, including high genomic complexity and mutations in genes regulating plasma cell identity, which predicted resistance to therapy. Single-cell RNA sequencing further revealed that MM cells from refractory patients exhibited high proliferation signatures and reduced expression of TNFRSF17 (encoding BCMA), while were less enriched for plasma cell–associated transcriptional programs, a phenomenon we term “plasma cell identity escape.” This profile was strongly associated with immune dysregulation of CD8 T cells including increased activation and exhaustion. This evolution of MM toward a more proliferative and lineage-divergent state, refractory to the anti-BCMA T-cell redirecting therapies, was functionally validated in preclinical MM mouse models. Collectively, our results comprehensively define the cellular and molecular mechanisms underlying primary resistance to anti-BCMA therapies.

Abstract Background Standardizing beam modeling in the treatment planning system is challenging due to machine‐specific configuration variations. Because of the proton therapy system (PTS) complexity and continuous development, there is no vendor's representative (“golden”) beam data like in LINACs at this stage. Purpose The primary goal of this study is to create a PTS‐specific repository of source model parameters and beam commissioning data for scanning proton therapy treatment planning systems and establish a set of reference values with associated bands of variations for this data. Methods We conducted a survey of 31 PTSs at 27 proton therapy centers to gather beam commissioning source model parameters used in their treatment planning system, ensuring a diverse selection of PTSs from major vendors: Hitachi, IBA, Varian, and Mevion. For each proton therapy center, we collected (i) beam meterset calibration in protons per MU, (ii) in‐air spot size at isocenter in terms of standard deviation in x‐ (crossline, σ x ) and y‐ (inline, σ y ) directions, (iii) in‐air angular spread at isocenter in x‐direction (θ x ) and y‐direction (θ y ) and (iv) distal falloff. For each PTS, we plotted each parameter as a function of nominal energy and performed polynomial regression analysis with optimal model selection based on Akaike Information Criterion. We calculated intraclass correlation coefficients (ICC) to quantify inter‐PTS variability and generated uncertainty bands representing standard deviations across PTSs for each parameter. Additionally, we computed percentage deviations of each individual PTS from the overall multi‐vendor mean to identify systematic vendor‐specific patterns and outliers across the 31 total PTSs analyzed. Results Multi‐vendor beam parameter analysis across 31 PTSs revealed systematic vendor‐specific patterns in inter‐PTS consistency and energy dependencies. Beam meterset calibration demonstrated excellent inter‐PTS consistency across most vendors (ICC: Varian ProBeam ≈ 0, IBA Proteus ONE = 0.006, Mevion S250i with HYPERSCAN = 0.015), with IBA Proteus PLUS systems showing moderate variation (ICC = 0.156 for the dedicated nozzle, ICC = 0.248, for the universal nozzle). Spot size and angular spread variability were highly vendor‐dependent, ranging from excellent consistency in IBA Proteus ONE to substantial variation in IBA Proteus PLUS with the universal nozzle (angular spread ICC = 0.566). Distal falloff maintained excellent consistency across all vendors (ICC < 0.023), indicating reliable energy spread characteristics. Uncertainty band analysis revealed characteristic energy‐dependent patterns, with most systems showing larger uncertainties at lower energies for spot size and angular spread, while IBA Proteus PLUS with the universal nozzle exhibited the largest uncertainty bands overall. Percentage deviation analysis demonstrated vendor‐specific clustering patterns, with some systems showing systematic positive or negative deviations from the multi‐vendor mean. Conclusions The reference data provided in this work will help to cross‐check the beam model during treatment planning system commissioning. Furthermore, this information is useful for Monte Carlo applications and research, where beam characteristics are required to set up phase space parameters, thereby avoiding the need to model the entire PTS. Finally, the results of this study will have indispensable educational value by providing valuable insights into the characteristics and capabilities of different PTSs.

Summary Central obesity is a risk factor for esophageal adenocarcinoma (EAC) independent of acid reflux. However, about a quarter of patients with Barrett’s esophagus (BE) and EAC have a normal body mass index (BMI). One hypothesis is that chronic systemic inflammation is as critical to the cancer pathway than the BMI alone. Therefore, we hypothesized that lean patients with BE/EAC would have a high prevalence of metabolic diseases. We aimed to compare metabolic diseases among lean patients with BE/EAC and overweight/obese BE/EAC and lean control without BE/EAC. We performed a propensity score–matched case–control study including patients with BE/EAC and a control group without BE/EAC from the Rochester Epidemiology Project (REP). The groups were compared using chi-square χ 2 and Student t-test as appropriate. Adjusted logistic regression models were used to compare the association with metabolic diseases. We included a total of 2504 patients (631 EAC, 621 BE, and 1252 controls). A quarter of patients (24.6%) with BE/EAC were lean. When compared to controls without BE/EAC, lean patients with BE were more likely to have diabetes (32.2% vs. 11.2%, P &lt; 0.001) and hyperlipidemia (76.2% vs. 53%, P &lt; 0.001). When comparing lean EAC patients to controls without BE/EAC, lean patients had a higher association with smoking (73.3% vs. 58.6%, P: 0.002) and diabetes (18.7% vs. 11.2%, P: 0.03). When compared to overweight/obese BE patients, lean BE were less likely to have NASH (7.7% vs. 20.3%, P: 0.001), diabetes (32.3% vs. 42.1%, P: 0.03), hyperlipidemia (77.2% vs. 87%, P: 0.003), and metabolic syndrome (24% vs. 54%, P &lt; 0.001). Similarly, when compared to overweight/obese patients with EAC, lean EAC patients were less likely to have NASH (0% vs. 15.5%, P &lt; 0.001), diabetes (18.7% vs. 34.1%, P &lt; 0.001), hyperlipidemia (37.3% vs. 51.8%, P: 0.002), hypertension (37.3% vs. 55.3%, P &lt; 0.001), and metabolic syndrome (8% vs. 37.6%, P &lt; 0.001). A quarter of patients with BE/EAC are lean. Although lean BE/EAC patients have a more favorable metabolic profile compared to overweight/obese BE/EAC patients, diabetes and smoking are more common among lean patients with EAC compared to lean controls. The higher association with smoking and diabetes among lean EAC challenges traditional risk factor paradigms, suggesting a significant role for insulin resistance and chronic inflammation in EAC pathogenesis, especially in patients without typical risk factors. BMI-determined obesity may need to be supplemented with inflammatory metabolic diseases to improve assessment of BE/EAC risk in lean patients.