Vascular organoids derived from human induced pluripotent stem cells (hiPSCs) recapitulate the cell type diversity and complex architecture of human vascular networks. This three-dimensional (3D) model holds substantial potential for vascular pathology modeling and in vitro drug screening. Despite recent advances, a key technical challenge remains in reproducibly generating organoids with consistent quality, which is crucial for downstream assays and applications.
View Article and Find Full Text PDFSoft tissue myoepithelial tumors (METs) are diagnostically challenging tumors that require careful histologic and immunohistochemical characterization for accurate classification. Nearly half of METs show recurrent EWSR1 or FUS gene rearrangements with a diverse set of fusion partners. The diversity of fusion partners and lack of known driver abnormalities in many cases raises the question of whether METs represent a uniformly distinct tumor entity.
View Article and Find Full Text PDFAs part of the advancement in therapeutic decision-making for brain tumor patients at St. Jude Children's Research Hospital (SJCRH), we developed three robust classifiers, a deep learning neural network (NN), k-nearest neighbor (kNN), and random forest (RF), trained on a reference series DNA-methylation profiles to classify central nervous system (CNS) tumor types. The models' performance was rigorously validated against 2054 samples from two independent cohorts.
View Article and Find Full Text PDFAim: To identify whether the introduction of low-low hospital beds resulted in changes in the incidence, associated patient harms and event characteristics of bed-related falls where implemented.
Design: This retrospective quality improvement study covered 36 months: 18 months pre-intervention and 18 months post-intervention.
Methods: Our analysis incorporated patient fall data from a hospital in upstate New York.