Acute promyelocytic leukemia (APL) is driven by the specific fusion gene PML-RARA produced by chromosomal translocation. Three classic isoforms, L, V, and S, are found in more than 95% of APL patients. However, atypical PML-RARA isoforms are usually associated with uncertain disease progression and treatment prognosis.
View Article and Find Full Text PDFBone metastases can disseminate to secondary sites and promote breast cancer progression creating additional clinical challenges. The mechanisms contributing to secondary metastasis are barely understood. Here, we evaluate the prediction power of Her2-expressing (Her2E) circulating tumor cells (CTCs) after analyzing over 13,000 CTCs from a cohort of 137 metastatic breast cancer (MBC) patients with initial HR+/Her2- status and employ preclinical models of bone metastasis (BM) to validate the role of Her2E CTCs in multi-organ metastases.
View Article and Find Full Text PDFAim: The aim of our study was to assess the impact of high body mass index (BMI) on type 2 diabetes mellitus (T2DM) in different Socio-Demographic Development Index (SDI) regions using data from the Global Burden of Disease (GBD) 2021 study.
Methods: Using data from the GBD study, the burden of disease for T2DM was measured by analyzing the age-standardized disability-adjusted life year rate (ASDR) and age-standardized mortality rate (ASMR) for type 2 diabetes due to high BMI and the associated estimated annual percentage change (EAPC). Decomposition analyses, frontier analyses, and predictive models were used to analyze changes and influencing factors for each metric.
Background: Alzheimer's disease (AD) is a prevalent neurodegenerative disease (ND). In recent years, multiple clinical and animal studies have shown that mitochondrial dysfunction may be involved in the pathogenesis of AD. In addition, short-chain fatty acids (SCFA) produced by intestinal microbiota metabolism have been considered to be important factors affecting central nervous system (CNS) homeostasis.
View Article and Find Full Text PDFStudy Objectives: This study aimed to identify the risk factors associated with falls in hospitalized patients, develop a predictive risk model using machine learning algorithms, and evaluate the validity of the model's predictions.
Study Design: A cross-sectional design was employed using data from the DRYAD public database.
Research Methods: The study utilized data from the Fukushima Medical University Hospital Cohort Study, obtained from the DRYAD public database.