Citations

Abstract Cognitive impairment is among the most common non-motor symptoms in parkinsonism and Parkinson’s disease, ranging from subjective complaints to dementia. Although several predictive factors have been described, their prognostic relevance remains uncertain. This study aims to examine the predictive association between clinical features and the risk of dementia and determine the incidence of dementia in a population-based cohort of incident parkinsonism cases. Patients with parkinsonism onset between 1991 and 2020 were identified using the Rochester Epidemiology Project records-linkage system. Subjects with cognitive impairment preceding motor symptoms onset or concomitant Alzheimer’s disease were excluded. Diagnoses of dementia and mild cognitive impairment were established using neuropsychological testing or brief cognitive screening. Development of dementia was assessed using a Cox proportional hazards regression model. Associations between dementia and each risk factor were individually estimated in adjusted Cox models. All risk factors were also included in a least absolute shrinkage and selection operator (LASSO) penalized multivariable Cox model. A total of 1,104 patients were included: 153 (13.9%) developed mild cognitive impairment and 543 (49.2%) developed dementia during follow-up. The cumulative incidence of dementia was 30.3% at 5 years, 48.6% at 10 years, 61.4% at 15 years, and 69.3% at 20 years, indicating that a considerable portion of patients remained free of dementia after a prolonged follow-up. The majority of patients with dementia were males (61.5%), and older age at disease onset was associated with risk of dementia (hazard ratio [HR] = 1.32, P < 0.001 per 5-year increment). In the penalized model, dementia risk was significantly higher among individuals presenting with impaired postural reflexes (HR = 1.26, P = 0.014), consistent with a poorer prognosis in akinetic-rigid-predominant parkinsonism. Eye movement abnormalities including reduced ocular range of motion (HR = 1.86, P < 0.001), and smooth pursuit alterations (HR = 1.34, P = 0.024) were also associated with dementia. Non-motor symptoms, including REM sleep behavior disorder (HR = 1.51, P < 0.001), orthostatic hypotension (HR = 1.30, P = 0.009), and constipation (HR = 1.24, P = 0.022), were linked to dementia risk, supporting a potential involvement of non-dopaminergic and central autonomic pathways in cognitive decline. These findings were largely confirmed in the Parkinson’s disease/Parkinson’s disease with dementia group, in which 77 (13.5%) developed mild cognitive impairment and 249 (43.5%) developed dementia. Among these patients, smooth pursuit alterations, reduced ocular range of motion, orthostatic hypotension, and constipation were associated with dementia.

Importance Timely identification of aortic stenosis (AS) is essential for appropriate clinical management, yet screening remains limited by dependence on comprehensive echocardiography and trained imaging personnel. Objective To develop and validate a deep learning algorithm for detection of moderate or greater AS and prospectively evaluate its performance using artificial intelligence (AI)–guided focused cardiac ultrasound (FoCUS) acquired by novice operators. Design, Setting, and Participants This diagnostic study included retrospective algorithm development and validation and prospective evaluation of AI-guided FoCUS across Mayo Clinic sites in the Midwest, Arizona, and Florida. The model was developed using 6753 patients and evaluated in internal validation (n = 852), internal test (n = 844), and validation (n = 1912) cohorts. Performance was assessed on FoCUS acquired by experienced sonographers (n = 602) and prospectively by novice operators (n = 1302). The retrospective model development and validation cohorts comprised studies performed from January 2005 through September 2022. Prospective study was conducted in 2 enrollment periods from June to August 2024 and from June to September 2025. Participants from both periods were combined to comprise the final prospective cohort. Exposure AI-guided FoCUS acquisition and automated deep learning–based assessment for detection of moderate or greater AS. Main Outcomes and Measures Detection of moderate or greater AS. Performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value. Results The model demonstrated excellent discrimination in the internal test cohort (AUROC, 0.99; 95% CI, 0.98-1.00) and geographically distinct validation cohorts in Arizona (AUROC, 0.99; 95% CI, 0.97-1.00) and Florida (AUROC, 0.99; 95% CI, 0.96-1.00). Among FoCUS examinations acquired by experienced sonographers, sensitivity was 95% (95% CI, 82-99) and specificity was 97% (95% CI, 95-98). In the prospective novice-operator cohort, 1258 of 1302 examinations (96.6%) were suitable for automated analysis. Sensitivity was 93% (95% CI, 82-99) and specificity was 96% (95% CI, 95-97). Expert review of AI-positive and uninterpretable examinations increased the positive predictive value from 49.4% to 91.1%, with sensitivity of 85.4%. Conclusions and Relevance A deep learning algorithm accurately detected moderate or greater AS across validation cohorts in this study. In prospective evaluation, novice operators were able to acquire AI-guided FoCUS examinations that enabled accurate detection of moderate or greater AS. These findings support a scalable strategy that may expand access to AS detection in settings with limited echocardiography resources.

Purpose Radioligand therapy (RLT) with [ 177 Lu] Lu-PSMA-617 has shown significant benefits in metastatic castration-resistant prostate cancer (mCRPC), including prolonged overall survival (OS) and progression-free survival (PFS), as well as improved quality of life. For patients previously treated with [ 177 Lu]Lu-PSMA-617 who subsequently experience disease progression, retreatment with [ 177 Lu] Lu-PSMA-617 has emerged as a potential approach, yet data on the outcomes of RLT retreatment remains limited. This study aimed to assess the efficacy and safety of retreatment RLT in mCRPC. Methods Clinical and imaging data of patients who underwent at least two cycles of [ 177 Lu] Lu-PSMA-617, followed by a second course of treatment with [ 177 Lu] Lu-PSMA-617 between March 2018 and December 2024, were retrospectively reviewed. Endpoints included biochemical response, imaging response, toxicity, PSA-PFS, rPFS, and OS. Radiologic and biochemical responses were evaluated using RECIST 1.1 criteria and PSA levels, respectively. Results A total of 589 patients underwent [ 177 Lu] Lu-PSMA-617 RLT, of whom 20 (3.4%, 20/589) received RLT retreatment. The median age was 71.3 years (range: 63.7–95.2), and the median follow-up was 73.3 months (95% CI, 35.7–77.2 months) after the initial treatment and 21.7 months (95% CI, 16.9–26.0 months) after retreatment. The initial treatment involved a median of 4.5 cycles (range: 2–6). Following the first course of RLT, 15 patients (75%, 15/20) had exhibited a >50% decline in PSA levels, including 9 who achieved a >90% decrease. Additionally, 12 patients (60%, 12/20) had demonstrated a complete or partial imaging response, while 3 (15%, 3/20) patients had stable disease, and 5 (25%, 5/20) patients had progressive radiologic disease. Patients received a median of 4 retreatment cycles (range, 1–6), and the median interval between initial RLT and retreatment RLT was 33.1 months (IQR, 16.2–48.9 months; range, 10.7–58.8 months). A PSA decline of ≥50% was observed in 9 out of 20 patients (45%, 9/20). An imaging response was observed in 10 patients (50%, 10/20), with 4 achieving a complete response and 6 achieving a partial response, whereas 10 patients (50%, 10/20) showed progression. The median PSA-PFS was 9.8 months (95% CI, 4.1–17.9 months), and the median rPFS was 8.2 months (95% CI, 3.3–17.9 months). Eight deaths occurred during follow-up. The estimated Kaplan Meier OS rates were 83.6% at 12 months (95% CI, 57.3%–94.4%) and 53.7% at 24 months (95% CI, 28.3%–73.7%). Grade 2 renal toxicity was observed in one patient (5%, 1/20). Three patients (15%, 3/20) developed Grade 2 anemia. Notably, no grade 3 or grade 4 adverse events were observed during either treatment period. Conclusion In our cohort, retreatment with [¹ 77 Lu]Lu-PSMA-617 RLT was associated with biochemical and imaging responses in some patients, with observed survival outcomes that support further investigation. However, given the retrospective single-arm design and small sample size, large prospective studies are needed to more rigorously evaluate the safety, efficacy, and optimal patient selection for retreatment RLT.

Health care organizations are rapidly adopting artificial intelligence (AI)-enabled tools, yet nurse leaders often lack a clear framework to evaluate, govern, and safely scale these technologies. This article presents a practical, system-level AI governance model grounded in risk mitigation, interdisciplinary partnership, and nursing leadership decision rights commonly enacted through nursing professional governance structures. We outline core components of an effective governance structure, including clinical usefulness, safety and reliability, fairness and inclusiveness, transparency and explainability, privacy and security, and organizational accountability. Real-world examples illustrate how nurse executives can guide responsible AI adoption to reduce cognitive load, improve documentation efficiency, and enhance patient outcomes. The framework equips nursing leaders with actionable strategies and practical workflow checklist to lead AI transformation in complex health care environments.

Tissue harmonic and spatial compound imaging, widely used US techniques, improve image quality by reducing noise and artifacts through complementary mechanisms.

Outcomes of kidney-alone transplantation (KAT) in former simultaneous liver-kidney (SLK) transplant candidates after liver waitlist delisting are not well defined. Using Scientific Registry of Transplant Recipients (SRTR) data, we identified adult recipients who underwent KAT between 2005 and 2025 after prior SLK listing (RLD-KAT), which was matched 1:5 to primary KAT recipients without liver disease by age, transplant year, and diabetes status. Patient and graft survival were evaluated using the Kaplan-Meier method and multivariable Cox proportional hazards models. We identified 163 RLD-KAT recipients matched to 815 controls. Unadjusted Kaplan-Meier analyses demonstrated lower patient survival in RLD-KAT (p = 0.020), whereas death-censored and overall graft survival were similar (p > 0.05). In multivariable models, death-censored graft survival (HR 0.89; 95% CI 0.51–1.53), overall graft survival (HR 1.00; 95% CI 0.73–1.36), and patient survival (HR 1.07; 95% CI 0.76–1.5) did not differ significantly. Six RLD-KAT recipients (3.7%) subsequently underwent liver transplantation. Kidney-alone transplantation in prior SLK candidates was associated with acceptable patient and graft outcomes and a low incidence of subsequent liver transplantation.

Abstract Identification of gene expression changes in post-mortem brain tissue of Alzheimer’s disease donors compared to controls have implicated numerous biological pathways for Alzheimer’s disease pathophysiology. Nonetheless, there is still limited understanding of how gene expression dysregulation underpins specific proteinopathies core to Alzheimer’s disease. Here we investigate brain transcriptomic changes in a well characterized cohort of Alzheimer’s disease donors to identify genes and networks that associate with Alzheimer’s disease endophenotypes including neuropathology measures (Braak stage, Thal phase and cerebral amyloid angiopathy score) and Alzheimer’s disease-related brain protein levels (Apolipoprotein E, Amyloid-β 40, Amyloid-β 42, tau, and phospho-Tau). Bulk transcriptome measures were collected from the temporal cortex tissue of 477 Alzheimer’s disease donors. Following quality control, transcriptome-wide association studies were performed for each endophenotype. We used weighted gene co-expression network analysis to build co-expression networks and integrated transcriptome with epigenetic and genetic data from the same donors. We detected a total of 5,740 Bonferroni significant temporal cortex gene associations with Alzheimer’s disease endophenotypes, most of which were with brain tau levels. We discovered tau-associated co-expression modules enriched in known and novel Alzheimer’s disease pathways. We found that a beneficial (or neutral) brain biochemical state of higher total tau and lower phospho-Tau are associated with increased levels of synaptic, DNA damage/repair, nucleic acid metabolism and myelin processes. In contrast, in a detrimental state of lower total and higher phospho-Tau, there is upregulation of vascular and immune, and downregulation of mitochondrial and myelin pathways. There are brain gene expression perturbations that are associated with Alzheimer’s disease endophenotypes. While some of these associations are common across multiple endophenotypes, many are distinct for different Alzheimer’s disease-related proteins. Based on these findings, we propose a hypothetical model of dynamic brain gene expression changes that track with progressive Alzheimer’s disease proteostasis. These expression changes hold potential to serve as dynamic, precision biomarkers of brain Alzheimer’s disease progression. This study demonstrates the potential of integrative multiomics and deep Alzheimer’s disease endophenotype analyses in well-characterized brain tissues to uncover with precision the complex biology of Alzheimer’s disease.