Citations

BACKGROUND: Aortic enlargement is a powerful predictor of dissection and rupture, yet it is rarely evaluated during routine myocardial perfusion imaging, despite the widespread availability of computed tomography (CT) attenuation correction scans. The aim of this study was to determine whether fully automated, opportunistically derived, artificial intelligence-based aortic measurements from myocardial perfusion imaging CT attenuation correction scans are associated with adverse outcomes in a large multicenter cohort. METHODS: CT attenuation correction scans from patients undergoing positron emission tomography/CT and single-photon emission CT/CT myocardial perfusion imaging across 10 centers were included. A deep learning model automatically segmented the thoracic aorta, and a postprocessing algorithm extracted maximum ascending and descending diameters. The aortic size index was calculated by indexing the diameter to body surface area. RESULTS: A total of 29 339 patients (56% men; median age, 66 years [interquartile range, 58–75 years]) were included. Over a median follow-up of 3.5 years (interquartile range, 1.9–5.0 years), 5083 (17.3%) patients died. Median ascending and descending aortic size index values were 1.8 cm/m 2 (interquartile range, 1.6–2.0) and 1.5 cm/m 2 (interquartile range, 1.4–1.6), respectively, with an increase with age and higher values in females. Elevated aortic size index thresholds (ascending >2.2 cm/m 2 ; descending >1.6 cm/m 2 ) were significantly associated with increased all-cause mortality (ascending: adjusted hazard ratio, 1.16 [95% CI, 1.07–1.26], P <0.001; descending: adjusted hazard ratio, 1.23 [95% CI, 1.14–1.31]; P <0.001). Notably, the prognostic value of an abnormal aortic size index persisted independent of age, sex, and perfusion abnormalities. CONCLUSIONS: Artificial intelligence can unlock previously unused information within routine myocardial perfusion imaging CT attenuation correction scans by rapidly and automatically quantifying aortic size at scale. Opportunistic aortic measurements derived from CT attenuation correction may serve as an adjunctive risk biomarker and could add prognostic value to standard myocardial perfusion imaging without additional imaging or radiation.

The adult chronic pancreatitis (CP) working group, formed at the inception of the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer (CPDPC), has developed a robust framework and infrastructure to conduct clinical, translational, and mechanistic studies of CP. At its core is PRO spective Evaluation of C hronic Pancreatitis for E pid E miologic and Translational Stu D ies (PROCEED), the first longitudinal cohort study of CP in US adults, launched in 2017. PROCEED has developed a well-annotated clinical data set of over 2000 deeply-phenotyped participants, a biorepository and an imaging repository. Investigators have published promising data on blood-based and imaging-based biomarkers of CP diagnosis and pain, which are awaiting validation. Clinical observations include classification of patients into mechanism-based pain phenotypes using responses to PROMIS questionnaires, prevalence and predictors of opioid use, prevalence of psychological comorbidity, and osteopathy in CP patients. Pilot clinical trials have evaluated the effect of oral indomethacin on pancreatic fluid prostaglandin E2 levels and Internet-based cognitive behavioral therapy (CBT) for chronic pain. Results of pilot trials have led to an ongoing definitive randomized clinical trial for CBT. The rich environment and resources have facilitated mentorship and academic development of many early-stage investigators. Investigators outside of the consortium have opportunities for collaboration. In the next 5 years, the working group plans to further strengthen the research platform, complete primary and key secondary analyses of PROCEED and ancillary studies, continue efforts to develop biomarkers for diagnosis and prognosis of CP, and complete ongoing clinical trials to address unanswered questions in the field.

Summary Long-read nanopore sequencing enables simultaneous detection of germline variation and native DNA base modifications on individual DNA molecules, providing a unique opportunity to investigate allele-specific epigenetic regulation. Here, we performed whole-genome nanopore sequencing on normal and tumor prostate tissues to characterize differential methylation, methylation entropy, and allele-specific methylation (ASM) associated with noncoding genetic variants. Genome-wide analysis identified extensive cancer-associated differentially methylated regions (DMRs), with hypermethylated DMRs significantly enriched near transcription start sites and transcriptional regulatory regions. Integration with transcriptomic datasets revealed strong inverse relationships between promoter methylation and gene expression, while 5-hydroxymethylcytosine (5hmC) levels positively correlated with transcriptional activity across gene bodies. Using fragment-level methylation patterns enabled by long-read sequencing, we further quantified methylation entropy incorporating both 5mCG and 5hmCG states. Cancer-hypermethylated DMRs exhibited markedly reduced entropy, consistent with clonal fixation of methylation states during tumor progression. Entropy profiling across chromatin annotations demonstrated maximal epigenetic heterogeneity at partially modified enhancer-associated regions. To investigate cis-regulatory genetic effects, we developed a simple ASM framework (nanoASM) that can partition sequencing reads by allelic state and identifies allele-specific DMRs directly from long-read data. Compared with conventional population-level mQTL analysis, ASM demonstrated substantially improved statistical efficiency by leveraging within-individual contrasts and reducing sample-level heterogeneity. Although germline single nucleotide polymorphisms (SNPs) were largely shared between normal and tumor tissues, ASM patterns differed substantially, with tumor-associated ASM regions displaying significantly larger genomic span and stronger allelic methylation differences. Comparative analysis with TCGA prostate mQTL and GTEx prostate eQTL datasets demonstrated substantial concordance between ASM directionality and downstream transcriptional effects, particularly for variants located within DMRs and near transcription start sites. At the IRX4 prostate cancer risk locus, ASM identified an androgen-responsive regulatory domain overlapping AR ChIP-seq and H3K27ac peaks, nominating rs6885084 as a candidate functional variant. At the PSCA locus, ASM anchored by rs4736369 was associated with allele-specific methylation, chromatin activation, transcript abundance, and isoform usage. Together, these findings establish nanopore-based ASM analysis as a powerful approach for resolving functional noncoding variants and their regulatory domains they control in prostate cancer.

Deep brain stimulation (DBS), a cornerstone neurosurgical intervention for advanced Parkinson’s disease, exerts modulatory effects on aberrant basal ganglia circuitry through continuous high-frequency electrical stimulation. This approach yields sustained amelioration of motor symptoms while concurrently reducing the requisite dosage of dopaminergic pharmacotherapy. Nonetheless, DBS still has its limitations: its efficacy on non-motor symptoms remains limited, electrode implantation leads to biological reactions, and it cannot alter the trajectory of disease progression. Embryonic stem cells and induced pluripotent stem cells can be directed to differentiate into functionally mature nigrostriatal dopaminergic neurons, demonstrating the potential for neuronal replacement and neuroprotective effects in preclinical models and early-phase clinical trials. Mesenchymal stem cells and their derived exosomes mitigate neuroinflammation and impede α-synuclein aggregation through neurotrophic support and immunomodulatory mechanisms. This article reviews two core domains that have achieved breakthroughs in the treatment of Parkinson’s disease—DBS and stem cells—and covers their current status and future prospects. First, this review elucidates the therapeutic mechanisms of DBS, including neurotransmitter release regulation and neuroinflammation suppression, while evaluating its long-term efficacy (e.g., the sustainability of motor improvement and advancements in directional stimulation technology) and safety challenges (e.g., cognitive impairment, mood disturbances, and glial scar formation induced by electrode implantation). Second, it provides an in-depth analysis of the differentiation mechanisms of stem cells from different sources (embryonic stem cells, induced pluripotent stem cells, mesenchymal stem cells, etc.), preclinical efficacy validation, and early-phase clinical trial outcomes (e.g., the long-term safety and motor function enhancement observed in the transplantation of induced pluripotent stem cell-derived nigrostriatal dopaminergic progenitor cells). Third, the article addresses the standardization of stem cell therapy protocols, strategies for managing immune rejection, and current international regulatory and ethical frameworks. The core challenges of current Parkinson’s disease treatment paradigms are as follows: DBS shows limited efficacy in improving axial symptoms (e.g., freezing of gait), whereas stem cell transplantation faces issues such as low long-term graft survival and insufficient functional integration. Looking ahead, multidisciplinary integration is essential to promote the synergistic application of DBS and stem cell therapy, develop personalized treatment protocols under the guidance of precision medicine, and translate novel neural interface materials and genetic engineering technologies into clinical practice. Ultimately, such integrated strategies hold the promise of advancing Parkinson’s disease therapy from mere symptom control to true disease modification.

BACKGROUND: Although the prognostic utility of positron emission tomography (PET) myocardial flow reserve (MFR) is well established, emerging data suggest that reduced subendocardial flows also predict adverse outcomes. However, the incremental value of subendocardial MFR (MFR SE ) beyond transmural MFR (MFR TM ) remains unclear. METHODS: We studied patients in a multicenter PET registry with normal perfusion on stress/rest Rb-82 PET, excluding those with a previous history of coronary artery bypass surgery, heart transplantation, or left ventricular ejection fraction <40%. The optimal MFR SE cutoff for predicting major adverse cardiovascular events (MACEs; death, myocardial infarction [MI], revascularization, or heart failure [HF] hospitalization) was determined using Youden’s index. Patients were stratified into 3 groups: concordant-normal (MFR TM ≥2.0; MFR SE ≥2.1), discordant (low-MFR SE , normal MFR TM ), and abnormal MFR TM . Clinical outcomes were compared by MFR groups. RESULTS: Among 6603 patients (normal N=4103; discordant N=885; abnormal N=1615) the mean age was 66.3±12.4 years, and 54% were women. Compared with the concordant-normal group, patients with discordant low-MFR SE were older and more likely to have hypertension, diabetes, peripheral artery disease, and previous percutaneous coronary intervention. The median MFR TM for normal, discordant, and abnormal groups were 2.86, 2.15, and 1.72, respectively. Over a median follow-up of 4.9 years, 1661 MACE events occurred. Discordant low-MFR SE patients had a higher risk of MACE (hazard ratio [HR], 1.41; 95% CI, 1.22–1.64) and all-cause mortality (HR, 1.36; 95% CI, 1.14–1.61) compared with concordant-normal patients. The discordant group had an intermediate absolute risk of MACE, with an adjusted annualized event rate of 5.79% (95% CI, 5.10–6.49) compared with 3.99% (95% CI, 3.67–4.30; P <0.001) in the concordant-normal group and 8.35% (95% CI, 7.71–9.00; P <0.001) in the abnormal MFR TM group. CONCLUSIONS: Subendocardial MFR reveals clinically meaningful risk heterogeneity among patients with preserved transmural flow reserve, helping refine risk stratification beyond traditional PET metrics.

Abstract Purpose Modeling relative biological effectiveness (RBE) is central to carbon ion radiotherapy treatment planning. The modified Microdosimetric Kinetic Model (mMKM) is a clinically established RBE framework that has guided treatment protocols at many existing carbon centers, while the Mayo Clinic Florida Microdosimetric Kinetic Model (MCF MKM) is a recently developed alternative. This work aims to implement the MCF MKM in the open‐source treatment planning system matRad and to quantitatively compare its RBE‐weighted dose predictions with those of the clinically established mMKM using identical physical dose distributions across multiple disease sites. These findings will help assess their dosimetric equivalence and inform protocol development for carbon ion radiotherapy at MCF. Methods Monte Carlo simulations of the MCF carbon beamline were performed to generate physical (IDD, LET, lateral spread) and biological base data for integration of each RBE model into matRad . Treatment plans were generated for six patients, each corresponding to a different disease site, using clinical beam configurations with carbon‐reference dose prescriptions, and plans were optimized using the MCF MKM. To isolate differences attributable solely to the RBE model, the resulting physical dose distributions were held fixed and RBE‐weighted doses were recalculated using the mMKM. Dose volume histogram (DVH) metrics and spatial dose‐difference maps were used to compare target coverage and organ‐at‐risk doses between the two models. Results Across patient cases, RBE‐weighted dose distributions from MCF MKM and mMKM showed strong agreement. Differences in target coverage were small, with CTV D95% differing by less than 1.6% across all disease sites and maximum target dose differences not exceeding 0.88%. Organ‐at‐risk dose deviations were limited, with differences of 3.0% or less across evaluated DVH metrics. Spatial dose‐difference maps showed that the largest discrepancies occurred in regions of steep dose gradients near target to organ‐at‐risk interfaces, while overall dose conformity and plan quality remained comparable between the two models. Conclusions: This study served as the first systematic model comparison of the MCF MKM and mMKM within a treatment planning environment. These findings suggest that the MCF MKM and mMKM produce dosimetrically consistent RBE‐weighted dose predictions under realistic planning conditions using carbon‐reference parameters. Accordingly, the fractionation schemes developed from years of clinical experience with mMKM implementations may serve as a practical foundation for protocol development at MCF.

Introduction Proton dose driven continuous scanning (DDCS) is a form of proton pencil beam scanning (PBS) in which the beam may remain on during transitions between successive spots. Mayo Clinic Florida (MCF) is preparing to commission a synchrotron-based proton therapy system designed for DDCS, incorporating novel irradiation control features. Given the novelty and complexity of this system, a systematic risk-based analysis was performed to inform upcoming commissioning strategies. Methods A prospective risk analysis was conducted using the American Association of Physicists in Medicine (AAPM) Task Group 100 (TG 100) framework. A comprehensive process map of the DDCS irradiation control workflow was developed. Failure modes and effects analysis (FMEA) was performed to identify potential delivery system failure modes and to score each failure mode based on occurrence, severity, and lack of detectability. Targeted mitigation and commissioning strategies were then developed to address the highest-risk items. Results Twenty-nine delivery system failure modes were identified. The highest-risk failure modes were specific to DDCS operation. Targeted commissioning strategies were defined, including scan path measurement and modeling, validation of move and flap dose, dose monitor collection efficiency testing, spot position monitor performance evaluation, and verification of control system intervention behavior. Incorporation of these strategies into the commissioning plan reduced the mean RPN by more than 50%. Discussion This work provides a detailed description of the MCF proton DDCS irradiation control system and uses a TG 100 based approach for identifying system-specific risks. As new systems incorporate technologies for which limited guidance exists, institutions must perform their own risk analyses to determine appropriate quality management strategies.

ABSTRACT Novel β-lactam/β-lactamase inhibitors (βL/βLIs) are important therapies for carbapenem-resistant Pseudomonas aeruginosa (CRPA). However, the global extent of resistance to these agents and the impact of resistance on patient outcomes are unclear. We therefore evaluated patients with CRPA isolates at 35 hospitals (nine countries) from December 2018 to November 2019. Antimicrobial susceptibility testing was performed at a central laboratory by agar dilution for ceftolozane-tazobactam (C/T) and ceftazidime-avibactam (CZA) and by broth microdilution for imipenem-relebactam (I/R). Characteristics and outcomes, including desirability of outcome rankings (DOOR), were compared between patients infected with isolates not susceptible vs susceptible to each agent. Of 800 CRPA isolates, susceptibility to C/T, CZA, and I/R was 69%, 67%, and 33%, respectively. USA isolates ( n = 526) were more frequently susceptible to these agents than isolates from other countries ( n = 274; C/T: 83% vs 42%; CZA: 77% vs 47%; I/R: 37% vs 23%; P < 0.001 for each comparison) and isolates with carbapenemases ( n = 157) were less frequently susceptible than isolates without carbapenemases ( n = 643; C/T: 7% vs 84%; CZA: 24% vs 77%; I/R: 6% vs 39%; P < 0.001 for each comparison). Thirty-day mortality and DOOR were similar overall in patients infected with isolates not susceptible vs susceptible to each βL/βLI. However, the adjusted probability of a better DOOR outcome for a randomly selected patient with bacteremia due to a C/T-not susceptible vs -susceptible isolate was 38.2% (95% confidence interval, 25.6%–52.7%). Resistance to novel βL/βLIs, especially I/R, is common in CRPA, particularly outside the USA and in carbapenemase-producing isolates. Additional treatment options are needed for CRPA infections.

ABSTRACT Aims Exercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercise, body composition analysis can potentially be used to estimate cardiorespiratory fitness. We developed a body composition “fitness” score, then validated its utility in two external populations. Methods and Results We included patients from four sites undergoing single photon emission computed tomography (SPECT) and twelve sites undergoing positron emission tomography (PET). We quantified body composition using deep learning. We evaluated associations between body composition and good exercise capacity (defined as completing ≥7 minutes on a Bruce protocol) then developed a body composition “fitness” score. We then assessed the associations of “fitness” score with exercise capacity and all-cause mortality in external populations. In total, 36471 patients were included with median age 67 (interquartile range 58 – 74). Median skeletal muscle density was higher among patients with good exercise capacity. In the external SPECT population, the body composition “fitness” score had higher prediction performance for good exercise capacity (AUC 0.771, 95% CI 0.752 – 0.789) than age (AUC 0.717, p<0.01). In the external PET population, high body composition “fitness” score was associated with lower cardiovascular death (adjusted hazard ratio 0.70, 95% CI 0.62 – 0.79, p<0.001). Conclusions We demonstrated that a comprehensive body composition “fitness” score could identify patients with good cardiorespiratory fitness. This approach transforms routinely acquired CT data into a surrogate marker of fitness which can be applied in patients undergoing PET, or other CT imaging, where exercise testing is not performed. Graphical Abstract Overview of study design. The overall population (n=36471) was split as outlined above. Body composition was analyzed from available computed tomography imaging. The distribution of body composition measures were analyzed in the combined single photon emission computed tomography (SPECT) populations. A body composition “fitness” score was derived to predict good exercise capacity in the internal population, with associations assessed in the two external testing populations.