Effects of cilostazol on cognitive function and dementia risk: A systematic review and meta-analysis.

Background: Cilostazol is an antiplatelet drug and is used for stroke prevention and symptomatic peripheral vascular disease. Studies have reported the effects of cilostazol on cognitive function, but the results are inconsistent and have not been systematically assessed.

Methods: We systematically searched the PubMed, Embase, and Cochrane databases for relevant clinical studies.

Genetic variation and evolutionary characteristics of Echovirus 11: new variant within genotype D5 associated with neonatal death found in China.

Echovirus 11 (E11) has gained attention owing to its association with severe neonatal infections. From 2018 to 2023, a surge in severe neonatal cases and fatalities linked to a novel variant of genotype D5 was documented in China, France, and Italy. However, the prevention and control of E11 variants have been hampered by limited background data on the virus circulation and genetic variance.

Molecular Epidemiology and Evolution of Coxsackievirus A14.

As the proportion of non-enterovirus 71 and non-coxsackievirus A16 which proportion of composition in the hand, foot, and mouth pathogenic spectrum gradually increases worldwide, the attention paid to other enteroviruses has increased. As a member of the species enterovirus A, coxsackievirus A14 (CVA14) has been epidemic around the world until now since it has been isolated. However, studies on CVA14 are poor and the effective population size, evolutionary dynamics, and recombination patterns of CVA14 are not well understood.

A brain tumor computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy.

Objectives: Gliomas and brain metastases (Mets) are the most common brain malignancies. The treatment strategy and clinical prognosis of patients are different, requiring accurate diagnosis of tumor types. However, the traditional radiomics diagnostic pipeline requires manual annotation and lacks integrated methods for segmentation and classification.

Genetic characterization and molecular evolution of type 3 vaccine-derived polioviruses from an immunodeficient patient in China.

In 2013, a case of immunodeficiency vaccine-derived poliovirus (iVDPV) was identified in Jiangxi Province, China. In this study, we purified 14 type 3 original viral isolates from this case and characterized the molecular evolution of these iVDPVs for 298 days. Genetic variants were found in most of the original viral isolates, with complex genetic and evolutionary relationships among the variants.

Artificial Intelligence-Assisted Left Ventricular Diastolic Function Assessment and Grading: Multiview Versus Single View.

Background: Clinical assessment and grading of left ventricular diastolic function (LVDF) requires quantification of multiple echocardiographic parameters interpreted according to established guidelines, which depends on experienced clinicians and is time consuming. The aim of this study was to develop an artificial intelligence (AI)-assisted system to facilitate the clinical assessment of LVDF.

Methods: In total, 1,304 studies (33,404 images) were used to develop a view classification model to select six specific views required for LVDF assessment.

Examining Factors That Influence Learner Retention in MOOCs During the COVID-19 Pandemic Time.

Massive Open Online Courses have become a frequent platform for learners to acquire knowledge. This study aims to explore multiple factors influencing learner retention in MOOCs during the COVID-19 pandemic. To address this, we collected quantitative and qualitative data from questionnaires and qualitative data from interviews and then analyzed them through the Partial Least Square-Structural Equation Modeling to test 14 research hypotheses.

Qualitative and quantitative analyses of artificial intelligence ethics in education using VOSviewer and CitNetExplorer.

The new decade has been witnessing the wide acceptance of artificial intelligence (AI) in education, followed by serious concerns about its ethics. This study examined the essence and principles of AI ethics used in education, as well as the bibliometric analysis of AI ethics for educational purposes. The clustering techniques of VOSviewer ( = 880) led the author to reveal the top 10 authors, sources, organizations, and countries in the research of AI ethics in education.

A Two-Stage End-to-End Deep Learning Framework for Pathologic Examination in Skin Tumor Diagnosis.

Neurofibromas (NFs), Bowen disease (BD), and seborrheic keratosis (SK) are common skin tumors. Pathologic examination is the gold standard for diagnosis of these tumors. Current pathologic diagnosis is primarily based on microscopic observation, which is laborious and time-consuming.

A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection.

Echocardiography is the first-line diagnostic technique for heart diseases. Although artificial intelligence techniques have made great improvements in the analysis of echocardiography, the major limitations remain to be the built neural networks are normally adapted to a few diseases and specific equipment. Here, we present an end-to-end deep learning framework named AIEchoDx that differentiates four common cardiovascular diseases (Atrial Septal Defect, Dilated Cardiomyopathy, Hypertrophic Cardiomyopathy, prior Myocardial Infarction) from normal subjects with performance comparable to that of consensus of three senior cardiologists in AUCs (99.

Echocardiography-based AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction.

Objective: To compare the performance of a newly developed deep learning (DL) framework for automatic detection of regional wall motion abnormalities (RWMAs) for patients presenting with the suspicion of myocardial infarction from echocardiograms obtained with portable bedside equipment versus standard equipment.

Background: Bedside echocardiography is increasingly used by emergency department setting for rapid triage of patients presenting with chest pain. However, compared to images obtained with standard equipment, lower image quality from bedside equipment can lead to improper diagnosis.

Cardiac Adaptation to Prolonged High Altitude Migration Assessed by Speckle Tracking Echocardiography.

Objective: Exposure to high altitudes represents physiological stress that leads to significant changes in cardiovascular properties. However, long-term cardiovascular adaptions to high altitude migration of lowlanders have not been described. Accordingly, we measured changes in cardiovascular properties following prolonged hypoxic exposure in acclimatized Han migrants and Tibetans.

Development and Validation of an Artificial Intelligence-Based Image Classification Method for Pathological Diagnosis in Patients With Extramammary Paget's Disease.

Extramammary Paget's disease (EMPD) is a rare, malignant cutaneous adenocarcinoma with a high recurrence rate after surgical resection. Early diagnosis of EMPD is critical as 15%-40% of cases progress into an invasive form and resulting in a dismal prognosis. However, EMPD can be a diagnostic challenge to pathologists, especially in the grassroots hospital, because of its low incidence and nonspecific clinical presentation.

Automated Analysis of Doppler Echocardiographic Videos as a Screening Tool for Valvular Heart Diseases.

Objectives: This study sought to develop a deep learning (DL) framework to automatically analyze echocardiographic videos for the presence of valvular heart diseases (VHDs).

Background: Although advances in DL have been applied to the interpretation of echocardiograms, such techniques have not been reported for interpretation of color Doppler videos for diagnosing VHDs.

Methods: The authors developed a 3-stage DL framework for automatic screening of echocardiographic videos for mitral stenosis (MS), mitral regurgitation (MR), aortic stenosis (AS), and aortic regurgitation (AR) that classifies echocardiographic views, detects the presence of VHDs, and, when present, quantifies key metrics related to VHD severities.

Deep learning for digitizing highly noisy paper-based ECG records.

Electrocardiography (ECG) is essential in many heart diseases. However, some ECGs are recorded by paper, which can be highly noisy. Digitizing the paper-based ECG records into a high-quality signal is critical for further analysis.