Citations

Background Public awareness and regional engagement with cardiorenal syndrome (CRS) remain poorly understood. We examined temporal trends and geographic patterns of U.S. public interest in CRS and their relationship with disease burden. Methods Google Trends™ data (2004–2025) annual relative search interest (RSI) for CRS. CRS-related mortality was derived from CDC WONDER (2007–2020) using hypertensive heart and chronic kidney disease deaths. Temporal trends were assessed via augmented Dickey-Fuller tests and linear regression. Associations between RSI and mortality were evaluated using Pearson correlation, AR(1) modeling, first-difference, and cumulative change analysis. State-level correlations were examined with and without adjustment for income, education, and insurance coverage. Results Both RSI and mortality exhibited strong non-stationary upward trend ( p = 0.98 and 0.42). Raw data showed a strong correlation analysis (r = 0.79, p = 0.001); but first-differenced year-over-year changes were not significant (r = −0.36, p = 0.23), consistent with AR(1) modeling ( β =1.58, p = 0.11). Cumulative changes remained strongly correlated (r = 0.80, p = 0.001), indicating parallel long-term trajectories. Findings were consistent with varying ICD-10 code definitions for CRS-related mortality. Geographically, RSI was highest in West Virginia and lowest in Texas and Georgia. State-level RSI showed no correlation with age-adjusted mortality, both unadjusted (r = −0.09, p = 0.52) and after adjustment for median income, educational, and insurance coverage ( β =0.03, p = 0.75). Conclusion Public interest in CRS has grown in parallel with mortality, reflecting its increasing epidemiological relevance. However, the lack of geographic correlation between search interest and mortality—even after socioeconomic adjustment—suggests awareness gaps and highlights opportunities for targeted education and resource allocation.

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.

Abstract Anthracycline-induced cardiotoxicity (AIC) remains a dose-limiting complication of cancer treatment, often leading to irreversible heart failure, yet effective cardioprotective therapies remain limited. Graph-driven drug repurposing offers a rapid pathway to identify cardioprotective therapies, but different computational models often generate divergent candidate lists that exceed experimental validation capacity. We applied a novel "Union-then-Filter" framework to prioritize therapeutics for AIC using AI-based approaches and evaluated candidates in zebrafish models. Three state-of-the-art graph learning models (TxGNN, CompGCN, and RLR) were trained on the PrimeKG to generate an initial candidate pool. A Large Language Model (LLM) agent automated evidence synthesis across 4,286 PubMed abstracts for the top 50 drugs from each model. Candidates were stratified using dual decision criteria and filtered against existing clinical trials. The pipeline narrowed 150 initial candidates to four novel candidates: Cysteine, Dasatinib, Tranilast, and Tretinoin. Prospective in vivo validation was subsequently conducted using an adult AIC (aAIC) zebrafish model. Phenotypic screening via echocardiography revealed that Cysteine, Tranilast, and Tretinoin significantly restored ejection fraction (EF) (overall one-way ANOVA: \(\:F\left(\text{4,95}\right)=47.66,\:\:p<0.0001\); Tukey's post hoc: \(\:\:\:p<0.01\) for all three treatments vs. DOX+ control) and normalized cardiac remodeling markers in doxorubicin-treated subjects (Kruskal-Wallis: \(\:{\chi\:}^{2}\left(4\right)=17.21,\:\:p=0.0018\); Dunn’s post hoc: \(\:\:\:p<0.01\) for all three comparisons vs. DOX control). This study demonstrates the translational utility of an LLM-integrated AI pipeline for drug repurposing and highlights zebrafish AIC as an efficient bridge between in silico prediction and in vivo phenotypic validation in cardio-oncology.

Abstract Introduction Clozapine-induced constipation may occur in at least 60% of patients, with first-line options typically involving polyethylene glycol or sennosides. However, there may be adherence barriers to an over-the-counter bowel regimen due to the dosage form, lack of insurance coverage, or lack of benefit. One possible solution to address these adherence barriers may include prescribing a secretagogue laxative. However, there is limited literature supporting the use of secretagogue laxatives in clozapine-associated constipation. A case series is presented in which adherence barriers impeded the use of over-the-counter laxatives, and a secretagogue laxative was implemented to manage clozapine-induced constipation. Case Report In 4 cases, lubiprostone or linaclotide was initiated, either as monotherapy or in combination with over-the-counter laxatives. Reasons for secretagogue laxative initiation included: (1) reducing a complex over-the-counter laxative regimen, (2) a lack of insurance coverage of over-the-counter laxatives, (3) dissatisfaction with polyethylene glycol’s palatability, and (4) cognitive impairment leading to decreased adherence to polyethylene glycol. Discussion It is important that clozapine-induced constipation is managed adequately to prevent severe consequences of gastrointestinal hypomotility. In addition to timely initiation and titration of constipation pharmacotherapy, adherence should be assessed. When adherence barriers to the use of over-the-counter laxatives are present, the use of a secretagogue laxative may be an option, recognizing there is limited evidence to guide this decision. Conclusion In these cases, adherence barriers to over-the-counter laxatives were identified. A secretagogue laxative was implemented, well-tolerated, and able to be continued for the duration of follow-up.

ABSTRACT The recent inclusion of the Merlin clinicopathologic–gene expression profile (CP‐GEP) assay in the National Comprehensive Cancer Network (NCCN) Melanoma Guidelines represents an important milestone in the clinical integration of molecular testing for cutaneous melanoma. Unlike earlier melanoma gene expression profile (GEP) assays, which focused primarily on prognostic risk stratification, CP‐GEP was specifically developed to predict sentinel lymph node (SLN) metastasis risk by identifying early metastatic competence within primary melanomas, with additional prognostic utility. This review discusses the biological evidence supporting CP‐GEP and examines how unbiased transcriptomic discovery converged with established insights from cancer cell biology, integrin signaling, focal adhesions, and extracellular matrix (ECM) remodeling to shape its conceptual framework. We further discuss how CP‐GEP captures a transformation‐associated biological state centered on an integrin‐ and TGF‐β–dependent signaling axis ( ITGB3 , TGFBR1 ) that promotes pericellular proteolysis and ECM remodeling ( PLAT , SERPINE2 , LOXL4 ), inflammatory and angiogenic signaling ( CXCL8 , GDF15 ), and melanocytic lineage identity ( MLANA ). Collectively, these genes identify a dissemination‐competent phenotype that is detectable within primary tumors before clinically apparent metastasis. Overall, the biological framework supporting CP‐GEP reinforces the concept that altered adhesion signaling and ECM remodeling are central drivers of early melanoma metastasis and represent clinically actionable biomarkers for individualized melanoma management. image

Importance Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies. Objectives To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure Population-level LDL-C concentrations. Main Outcomes and Measures Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

Abstract How glial cells sense and regulate neuronal hypoactivity remains poorly understood. Using in vivo imaging in anesthetized, sleeping, and behaving mice, we identified a glial purinergic signaling mechanism that converts neuronal hypoactivity into local circuit regulation. Cortical hypoactivity increased spatially confined ATP release through astrocytic pannexin-1 hemichannels, which were structurally enriched near perisomatic parvalbumin-positive boutons. Local ATP release directed microglial process movement and stabilized bulbous endings through microglial P2Y12 signaling, accompanied by localized Ca²⁺ activity in microglial processes. Disruption of astrocytic pannexin-1 or microglial P2Y12 impaired bulbous-ending stabilization and abolished rebound increases in neuronal activity during emergence from anesthesia. Similar ATP release and bulbous-ending formation were observed during chemogenetic neuronal silencing or natural sleep in freely behaving mice. These findings identify astrocyte-to-microglia purinergic signaling as a mechanism that converts neuronal hypoactivity into synapse-selective microglial regulation of circuit activity.