Citations

Objective: This study aims to develop a contrast-free, high-sensitivity ultrasound method, denoted as Radon transform-based flow measurement (R-Flow), for in vivo mapping of microvascular flow vectors and for establishing R-Flow-derived vector-field metrics to noninvasively quantify microcirculatory patterns in liver cirrhosis. Impact Statement: R-Flow enables robust, contrast-free imaging of microvascular dynamics and demonstrates translational feasibility in the human liver. Its direction-aware indices offer pilot in vivo quantification of flow redistribution and remodeling, providing unique insights into hepatic flow dynamics. Introduction: Microvascular dysfunction is a hallmark of many diseases, yet noninvasive visualization and quantitative assessment of abnormal microcirculation remain limited. Methods: R-Flow leverages Radon transform to decode red blood cell dynamics from the spatiotemporal domain of ultrasound flow signals, reconstructing velocity vectors at microvascular scale. From these vector maps, unique direction-aware indices are further derived to characterize flow distribution and heterogeneity. Results: Validated across simulations, phantoms, and in vivo studies, R-Flow provides robust velocity estimation across a wide range (1 to 60 mm/s). Notably, it enables high-sensitivity microvascular flow vector mapping of human liver, showing strong agreement with references ( r > 0.9). In a rat model, these direction-aware indices revealed a shift from healthy multipath perfusion in control livers to directionally biased vascular pattern in cirrhotic livers, demonstrating significant correlation with pathological indicators. Conclusion: R-Flow enables noninvasive, contrast-free mapping of microvascular blood flow velocity and offers a promising approach for high-resolution assessment of microvascular flow characteristics.

Introduction: Cerebral amyloid angiopathy (CAA) is a progressive cerebrovascular disease caused by amyloid-beta (Aβ) deposition in cerebral vasculature. While CAA is a recognized cause of intracerebral hemorrhage (ICH) and cognitive decline, emerging evidence suggests that seizures are among a broader array of CAA clinical manifestations. However, the risk of seizures and status epilepticus in CAA remains poorly characterized. This study aimed to estimate incidence of seizures and status epilepticus in CAA using real-world data. Methods: A retrospective, longitudinal study was conducted using a nationally representative sample of administrative claims from US Medicare and commercial insurance beneficiaries (2017–2024). Patients with CAA (aged > 50 years with > 1 physician encounter with a validated ICD-10 code (I68.0)) were matched 1:4 to age- and sex-matched controls. The primary outcome was occurrence of seizure, ascertained using validated ICD-10 codes during acute care; the secondary outcome was hospitalization for status epilepticus. Follow-up continued until the end of enrollment or death. Descriptive statistics summarized patient characteristics and Poisson regression estimated incidence rates with 95% CIs. Cox proportional hazards regression assessed associations between CAA and outcomes of interest vs controls, adjusted for chronic kidney disease, liver disease, prior ICH, and seizure history. Results: We included 7294 patients with CAA and 29176 controls (median age: 77 years; 53.7% women; median follow-up: 29.4 months). Patients with CAA had higher rates of prior ICH (32.4% vs 0.5%) and prior seizure (18.7% vs 1.8%) than controls. The incidence rate of seizures was higher in patients with CAA than controls: 8.1 per 100 person-years (PY; 95% CI: 7.6–8.6) vs 0.4 per 100 PY (95% CI: 0.4–0.5). In the CAA cohort, seizure incidence rate was higher for those with prior ICH than those with no ICH history: 13.6 per 100 PY (95% CI: 12.5–14.8) vs 5.7 per 100 PY (95% CI: 5.2–6.3). Status epilepticus rates were higher in patients with CAA than controls: 0.7 per 100 PY (95% CI: 0.6–0.9) vs 0.03 per 100 PY (95% CI: 0.02–0.04). Adjusted models showed that CAA was associated with higher risk of seizures (HR: 6.7, 95% CI: 5.8–7.7) and status epilepticus (HR: 8.1, 95% CI: 4.8–13.7) than controls (Table 1). Conclusions: Patients with CAA have an increased risk of seizures and status epilepticus. These findings broaden the possible clinical spectrum of CAA.

BACKGROUND: Cerebral hemodynamic impairment in patients with asymptomatic high-grade (>70%) internal carotid artery (ICA) stenosis is associated with risk of stroke and cognitive decline, but correlations between the degree of stenosis and hemodynamic impairment are derived from small case series. Using baseline data from 242 participants in the CREST-H study (Carotid Revascularization and Medical Management for Asymptomatic Carotid Stenosis–Hemodynamics), we hypothesized that the degree of stenosis in the ICA would correlate with time-to-peak (TTP) delay in ipsilateral cerebral blood flow using MR/CT perfusion scans, adjusting for demographic and cardiovascular risk variables. METHODS: From all participants, ICAs were insonated using a 7 to 10 MHz probe for peak systolic velocity (PSV) and end diastolic velocity. Dynamic contrast perfusion imaging was standardized across 61 CREST-H sites, using standard sequential T2*-weighted perfusion imaging. CT perfusion used standard clinical protocols. TTP delay was calculated in the ipsilateral versus contralateral hemispheres. In cross-sectional analysis, linear regression was used to model TTP delay as the outcome variable and PSV, end diastolic velocity, and PSV ICA/common carotid artery ratio on the index and nonindex side as primary predictor variables in 6 separate models, adjusting for covariates, followed by automated backward elimination model reduction. RESULTS: Among 392 CREST-H participants, the 242 with complete data for all variables were included in the regression analysis (age, 70±7.6; 62% M). End diastolic velocity on the index side correlated with TTP delay (β=0.003, P =0.005). PSV had a similar correlation but did not reach significance (β=0.001, P =0.099), nor did ICA/common carotid artery ratio (β=0.003, P =0.126). Nonindex side PSV, end diastolic velocity, and ICA/common carotid artery ratio showed no correlation with TTP delay ( P =0.268, P =0.495, P =0.380, respectively). Circle of Willis completeness did not correlate with TTP. CONCLUSIONS: In this large cohort of patients with high-grade asymptomatic carotid stenosis, higher end-diastolic flow velocities correlated with greater TTP delays, supporting a definition of hemodynamically significant stenosis. Our findings have implications for the management of asymptomatic carotid stenosis, which will be tested in the CREST-H study. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03121209.

INTRODUCTION: Reliable biomarkers for the diagnosis of chronic pancreatitis (CP) and pancreatic fibrosis severity are lacking, hindering effective treatment and management. Histologic fibrosis is a hallmark of late-stage CP, but noninvasive methods to evaluate fibrosis progression are limited. We used urine proteomics to discover biomarkers that identify patients with CP and predict fibrosis severity. METHODS: We performed a cross-sectional study of 130 total subjects (CP n = 50) selected based on clinical criteria in a tertiary care setting. Urine proteomics samples were quantified using data-independent acquisition mass spectrometry. Differential biomarker candidates were identified with false discovery rate-corrected pairwise comparisons. These proteins were validated with an independent paired urine and plasma sample cohort (n = 36). Machine learning was used to develop a protein panel that predicted Ammann scores for patients with histologic fibrosis. RESULTS: We found 34 proteins consistently differentially expressed between CP and controls in pairwise false discovery rate-controlled tests. Of these, 25 urine proteins outperformed 19 previously suggested CP blood-based biomarkers in an independent validation cohort. Isocitrate dehydrogenase (IDH1), calcyphosin (CAPS), synuclein gamma (SNCG), and protein S100-P (S100P) all produced receiver operator curve area under the curve values >0.95, while the best plasma marker was interleukin 2 receptor subunit alpha (receiver operator curve area under the curve = 0.80). A 12-protein panel of identified markers predicted fibrosis severity with a linear correlation R 2 value of 0.61. DISCUSSION: We identified a panel of proteins that may diagnose CP in children and developed a model to predict pancreatic fibrosis severity, offering promising tools for improving diagnostics and patient care.

Background The role of dose escalation of radiotherapy (RT) in unresectable stage III NSCLC followed by immunotherapy is unclear. The objective of the current study is to investigate if intermediate dose escalation (IDE) is beneficial in stage III NSCLC patients who receive definitive concurrent chemoradiation (dcCRT) followed by immunotherapy. Materials and methods The study used data from the National Cancer database. Multivariable Cox regression analysis was used to assess the all-cause mortality of patients who received standard RT dose (SD) (60 Gy ± 10%) and IDE (64–74 Gy). Results 47,315 patients were diagnosed in the era before immunotherapy and received dcCRT only, while 4,947 patients were treated with dcCRT and immunotherapy. In the cohort with dcCRT only, patients who received SD had statistically significant worse mortality but clinically minimal difference compared to patients with IDE (HR: 1.09, 95% CI: 1.07-1.12; p<0.0001). In the era of immunotherapy, SD was still associated with worse mortality compared to IDE (HR: 1.17, 95% CI: 1.03-1.33; p=0.02). However, the survival benefit associated with IDE was only restricted to patients who started immunotherapy within six weeks after RT completion (HR: 1.26, 95% CI: 1.05-1.6; p=0.01). There was no difference in mortality between SD and IDE among patients who started immunotherapy between 7–10 weeks (HR: 1.13, 95% CI: 0.88-1.45; p=0.35) or >10 weeks after RT completion (HR: 0.74, 95% CI: 0.51-1.07; p=0.11). Conclusion IDE of RT is not needed for patients diagnosed with stage III unresectable NSCLC who receive immunotherapy > six weeks after dcCRT.

Abstract INTRODUCTION The vascular endothelial growth factor (VEGF) signaling family plays a role in neurodegenerative diseases, including Alzheimer's disease (AD). Previous work has shown widespread effects of the members FLT1 , FLT4 , and VEGFB on AD outcomes. However, these analyses have focused within the non‐Hispanic White (NHW) population. METHODS The goal of this study was to analyze the effects of the VEGF family in underrepresented populations, leveraging large and diverse bulk RNA sequencing and tandem mass tag–mass spectrometry (TMT‐MS) proteomic data. Outcomes included measures of AD pathology and diagnosis. RESULTS Within underrepresented populations, we replicated previously reported effects of FLT1 and FLT4 , whereby higher protein abundance was observed in the AD brain and was associated with higher neuropathology burden. In stratified analyses, these associations were largely consistent across race and ethnicity. DISCUSSION This multi‐omic study on the role of the VEGF family in AD emphasizes the need for more representative studies focused on therapeutic targets for AD. Highlights Vascular endothelial growth factor (VEGF) genes and proteins were quantified in four different brain regions. Samples included participants from four different populations. Previously observed effects were replicated in diverse populations. This study is the largest multi‐omic study of the vascular endothelial growth factor (VEGF) genes among Alzheimer's disease (AD) participants from diverse populations.

Background While delivery of supplemental oxygen is a life-saving therapy, exposure to high oxygen, called hyperoxia, leads to increased intensive care unit mortality. Hyperoxia induces oxidant-mediated acute lung injury (ALI) and pulmonary cell death, called hyperoxic ALI (HALI). Elucidating molecular mechanisms in HALI could identify therapeutic targets in ALI. Methods In the current study, we examined in vivo effects of HALI on Beclin-1 (BCN1), which regulates autophagy, and modulation of BCN1 by epidermal growth factor receptor (EGFR). Effects of HALI on BCN1 and autophagy were examined in mice with genetically decreased EGFR (EGFR Wa5/+ ). Wildtype (WT) and EGFR Wa5/+ mice were exposed to 100% oxygen for 24–72 hours along with normoxia controls (eight groups; n=4–6/group), and analysis of pulmonary BCN1 and autophagy was completed. Results In WT, HALI led to increased BCN1 (59% increased total BCN1/β-Actin; p<0.01) in lung and alveolar epithelium (484% increased H-score; p<0.001). HALI led to decreased microtubule-associated protein 1B-light chain (LC3B)-II/-I ratios (43% decrease; p<0.05) and increased p62 (93% increase; p<0.05), suggesting reduced autophagic flux. In human alveolar type-II cells derived from induced pluripotent stem cells (AT2s iPSC ), HALI caused increased LDH release (130% increase; p<0.0001) and decreased LC3B-II/-I ratios (32% decrease; p<0.05). We previously showed that EGFR Wa5/+ mice are protected in HALI. In the current study, EGFR Wa5/+ mice showed increased pulmonary BCN1 (122% increase; p<0.05), reduced phosphorylated-(p-)/total BCN1 (47% decrease; p<0.05) and decreased LC3B-II/-I ratios (70% decrease; p<0.01) in HALI compared with WT. In addition, wortmannin (1 mg/kg), which decreases BCN1-mediated autophagy, increased mortality in HALI compared with vehicle control (n=10/group; 18% decreased hours survived; p<0.001). Conclusions These data delineate a novel cell death pathway in HALI involving BCN1 and EGFR with therapeutic potential.

Abstract Early-onset Alzheimer’s disease (EOAD) and Late-onset AD (LOAD) differ in clinical presentations and rates of progression. We aimed to compare baseline and longitudinal tau PET burden, and their relationship with clinical variables in amyloid-PET positive, cognitively impaired participants from the Longitudinal Early-Onset Alzheimer’s Disease Study (EOAD; n=390) and Alzheimer’s Disease Neuroimaging Initiative (LOAD; n=211). Patients with EOAD showed higher baseline tau PET retention, broader neuroanatomical involvement and faster accumulation rates over time compared to LOAD, after adjusting for amyloid load and clinical stage. Tau PET showed stronger correlations with baseline amyloid burden and clinical measures of global cognition and function in EOAD than LOAD. We conclude that earlier age of onset in AD is linked to a more aggressive tauopathy, which in turn is a primary driver of clinical decline. These findings suggest that optimal therapeutic targets and strategies may differ between EOAD and LOAD. One Sentence Summary Younger patients with Alzheimer’s disease show more aggressive tau spread, suggesting age of onset defines distinct disease pathways with key clinical implications.

Abstract Generative artificial intelligence (AI) is rapidly populating medical records with synthetic or partially AI-generated content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathological variability and diagnostic reliability of medical data at population scale. By analysing more than 800,000 synthetic data points across clinical text generation, vision–language reporting, and medical image synthesis, we find that models progressively converge toward generic phenotypes regardless of the model architectures. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. Crucially, this degradation is masked by false diagnostic confidence. Models continue to issue reassuring reports while failing to detect life-threatening pathology, with false reassurance rates tripling to 40%. Blinded physician evaluation confirms that this decoupling of confidence and accuracy renders AI-generated documentation clinically useless after just two generations. We systematically evaluate three mitigation strategies that can be easily integrated into existing clinical workflows, finding that while synthetic volume scaling fails to prevent collapse, mixing real data with quality-aware filtering effectively preserves diversity. Ultimately, our results suggest that without policy-mandated human oversight, the deployment of generative AI threatens to degrade the very healthcare data ecosystems it relies upon.