Nitroxoline upregulates low-density lipoprotein receptors expression, enhances lipid metabolism, and reduces hepatic steatosis and atherosclerosis in Apoe mice.

Low-density lipoprotein receptors (LDLRs) play a critical role in maintaining cholesterol homeostasis. Dysregulation of lipid metabolism contributes to atherosclerosis and steatohepatitis. This study investigated the effects of nitroxoline on LDLR expression and its protective role in lipid dysregulation, hepatic steatosis, and atherosclerosis.

Prognostic Significance of AI-Enhanced ECG for Emergency Department Patients.

: Artificial intelligence (AI)-enabled electrocardiogram (ECG) analysis may assist in objective and reproducible risk stratification. However, the prognostic utility of serial ECGs, particularly the follow-up ECG prior to discharge, has not been extensively studied. This study aimed to evaluate whether dynamic changes in AI-predicted ECG risk scores could enhance prediction of post-discharge outcomes.

Chalcone derivative 1m mitigates hepatic fibrosis and inflammation, enhances cholesterol efflux and reduces atherosclerosis.

Atherosclerosis, which is characterized by lipid dysregulation, remains a major concern in cardiovascular medicine because of its multifactorial nature. Although lipid-lowering therapies, particularly statins, form the cornerstone of atherosclerosis management, issues such as statin intolerance and inadequate responses necessitate the exploration of novel therapeutic modalities. This study aimed to evaluate the hepatoprotective effects of 1m, a chalcone derivative and potential anti-atherosclerotic drug.

Economic analysis of an AI-enabled ECG alert system: impact on mortality outcomes from a pragmatic randomized trial.

Findings from a previous study (ClinicalTrials.gov: NCT05118035) demonstrated that an AI-enabled electrocardiogram (AI-ECG), combining AI reports and physician alerts, effectively identified hospitalized patients at high risk of mortality and reduced all-cause mortality. This study evaluates its cost-effectiveness from the health payer's perspective in Taiwan over a 90-day post-intervention period.

Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial.

Background: Early diagnosis of low ejection fraction (EF) remains challenging despite being a treatable condition. This study aimed to evaluate the effectiveness of an electrocardiogram (ECG)-based artificial intelligence (AI)-assisted clinical decision support tool in improving the early diagnosis of low EF among inpatient patients under non-cardiologist care.

Methods: We conducted a pragmatic randomized controlled trial at an academic medical center in Taiwan.

Monitoring serum potassium concentration in patients with severe hyperkalemia: the role of bloodless artificial intelligence-enabled electrocardiography.

Background: Severe hyperkalemia is a life-threatening emergency requiring prompt management and close surveillance. Although artificial intelligence-enabled electrocardiography (AI-ECG) has been developed to rapidly detect hyperkalemia, its application to monitor potassium (K) levels remains unassessed. This study aimed to evaluate the effectiveness of AI-ECG for monitoring K levels in patients with severe hyperkalemia.

Development of an Artificial Intelligence-Enabled Electrocardiography to Detect 23 Cardiac Arrhythmias and Predict Cardiovascular Outcomes.

Arrhythmias are common and can affect individuals with or without structural heart disease. Deep learning models (DLMs) have shown the ability to recognize arrhythmias using 12-lead electrocardiograms (ECGs). However, the limited types of arrhythmias and dataset robustness have hindered widespread adoption.

Point-of-Care Potassium Measurement vs Artificial Intelligence-Enabled Electrocardiography for Hyperkalemia Detection.

Background: Hyperkalemia can be detected by point-of-care (POC) blood testing and by artificial intelligence- enabled electrocardiography (ECG). These 2 methods of detecting hyperkalemia have not been compared.

Objective: To determine the accuracy of POC and ECG potassium measurements for hyperkalemia detection in patients with critical illness.

Integrating VAI-Assisted Quantified CXRs and Multimodal Data to Assess the Risk of Mortality.

To address the unmet need for a widely available examination for mortality prediction, this study developed a foundation visual artificial intelligence (VAI) to enhance mortality risk stratification using chest X-rays (CXRs). The VAI employed deep learning to extract CXR features and a Cox proportional hazard model to generate a hazard score ("CXR-risk"). We retrospectively collected CXRs from patients visited outpatient department and physical examination center.

Pitavastatin attenuates hypercholesterolemia-induced decline in serotonin transporter availability.

Introduction: Hypercholesterolemia is associated with increased inflammation and impaired serotonin neurotransmission, potentially contributing to depressive symptoms. However, the role of statins, particularly pitavastatin, in modulating serotonin transporter (SERT) function within this context remains underexplored. This study aimed to investigate whether pitavastatin counteracts the neurobiological effects of hypercholesterolemia.

A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension.

The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug development in the past decade. The application of Artificial Intelligence for the detection of elevated pulmonary arterial pressure (ePAP) was reported recently. Artificial Intelligence (AI) has demonstrated the capability to identify ePAP and its association with hospitalization due to heart failure when analyzing chest X-rays (CXR).

Enhancing the Central Venous Catheterization Competency of Medical Students through a Specialized Team and an Interactive Response System: A pre-post study.

Background: Central venous catheterization (CVC) is a critical clinical procedure. To avoid complications, possessing good knowledge regarding the CVC care bundle and skills for the proper insertion and maintenance of CVC are important.

Objectives: To evaluate the effectiveness of an educational intervention and the use of an interactive response system in enhancing the CVC bundle care and insertion skills of medical students undergoing critical care medicine training.

Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events.

Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker implantation (PMI) is commonly caused by sick sinus syndrome or conduction disorders, predicting PMI is challenging, as patients often experience related symptoms. This study was designed to create a deep learning model (DLM) for predicting future PMI from ECG data and assess its ability to predict future cardiovascular events.

Osteoporotic Precise Screening Using Chest Radiography and Artificial Neural Network: The OPSCAN Randomized Controlled Trial.

Background Diagnosing osteoporosis is challenging due to its often asymptomatic presentation, which highlights the importance of providing screening for high-risk populations. Purpose To evaluate the effectiveness of dual-energy x-ray absorptiometry (DXA) screening in high-risk patients with osteoporosis identified by an artificial intelligence (AI) model using chest radiographs. Materials and Methods This randomized controlled trial conducted at an academic medical center included participants 40 years of age or older who had undergone chest radiography between January and December 2022 without a history of DXA examination.

Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases.

Background: Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) changes may be related to multiple VHDs, and (AI)-enabled ECG has been able to detect some VHDs. We aimed to develop five deep learning models (DLMs) to identify aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation.

AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial.

The early identification of vulnerable patients has the potential to improve outcomes but poses a substantial challenge in clinical practice. This study evaluated the ability of an artificial intelligence (AI)-enabled electrocardiogram (ECG) to identify hospitalized patients with a high risk of mortality in a multisite randomized controlled trial involving 39 physicians and 15,965 patients. The AI-ECG alert intervention included an AI report and warning messages delivered to the physicians, flagging patients predicted to be at high risk of mortality.

Artificial intelligence-enabled electrocardiography contributes to hyperthyroidism detection and outcome prediction.

Background: Hyperthyroidism is frequently under-recognized and leads to heart failure and mortality. Timely identification of high-risk patients is a prerequisite to effective antithyroid therapy. Since the heart is very sensitive to hyperthyroidism and its electrical signature can be demonstrated by electrocardiography, we developed an artificial intelligence model to detect hyperthyroidism by electrocardiography and examined its potential for outcome prediction.

Ca3.1 T-type calcium channel blocker NNC 55-0396 reduces atherosclerosis by increasing cholesterol efflux.

Calcium channel blockers (CCBs) are commonly used as antihypertensive agents. While certain L-type CCBs exhibit antiatherogenic effects, the impact of Ca3.1 T-type CCBs on antiatherogenesis and lipid metabolism remains unexplored.

Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk.

A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis.

Opportunistic Screening for Asymptomatic Left Ventricular Dysfunction With the Use of Electrocardiographic Artificial Intelligence: A Cost-Effectiveness Approach.

Background: The burden of asymptomatic left ventricular dysfunction (LVD) is greater than that of heart failure; however, a cost-effective tool for asymptomatic LVD screening has not been well validated. We aimed to prospectively validate an artificial intelligence (AI)-enabled electrocardiography (ECG) algorithm for asymptomatic LVD detection and evaluate its cost-effectiveness for opportunistic screening.

Methods: In this prospective observational study, patients undergoing ECG at outpatient clinics or health check-ups were enrolled in 2 hospitals in Taiwan.

A Deep-Learning Algorithm-Enhanced Electrocardiogram Interpretation for Detecting Pulmonary Embolism.

Background: The early diagnosis of pulmonary embolism (PE) remains a challenge. Electrocardiograms (ECGs) and D-dimer levels are used to screen potential cases.

Objective: To develop a deep learning model (DLM) to detect PE using ECGs and investigate the clinical value of false detections in patients without PE.

An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration.

Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from the initial assessment is limited, hindering the risk identification accuracy of triage. Therefore, we sought to develop a 'dynamic' triage system as secondary screening, using artificial intelligence (AI) techniques to integrate information from initial assessment data and subsequent examinations.