Within the World Health Organization (WHO) classification of haematopoietic neoplasms, particularly its fifth version from 2022 (WHO-HAEM5), myeloid neoplasms are not only grouped into myeloproliferative (MPN) and myelodysplastic neoplasms (MDS). There is also a group of haematological disorders that share features of both categories termed myelodysplastic /myeloproliferative neoplasms (MDS/MPN). In this article, we aim to provide a comprehensive and practical guide to WHO-HAEM5 highlighting the genetic alterations that underlie MPN and MDS/MPN.
View Article and Find Full Text PDFIn recent years, technology developments and increase in knowledge have led to profound changes in the diagnostics of haematologic neoplasms, particularly myeloid neoplasms. Therefore an updated, fifth edition of the World Health Organization (WHO) classification of haematolymphoid neoplasms (WHO-HAEM5) will be issued in 2024. In this context, we present a practical guide for analysing the genetic aspects of clonal haematopoiesis of indeterminate potential (CHIP), clonal cytopenia of undetermined significance (CCUS), myelodysplastic neoplasms (MDS), and acute myeloid leukaemia (AML) based on WHO-HAEM5.
View Article and Find Full Text PDFAtherosclerotic lesions preferentially develop at bifurcations, characterized by non-uniform shear stress (SS). The aim of this study was to investigate SS-induced endothelial activation, focusing on stress-regulated mitogen-activated protein kinases (MAPK) and downstream signaling, and its relation to gap junction proteins, Connexins (Cxs). Human umbilical vein endothelial cells were exposed to flow ("mechanical stimulation") and stimulated with TNF-α ("inflammatory stimulation").
View Article and Find Full Text PDFArtificial intelligence (AI) is about to make itself indispensable in the health care sector. Examples of successful applications or promising approaches range from the application of pattern recognition software to pre-process and analyze digital medical images, to deep learning algorithms for subtype or disease classification, and digital twin technology and in silico clinical trials. Moreover, machine-learning techniques are used to identify patterns and anomalies in electronic health records and to perform ad-hoc evaluations of gathered data from wearable health tracking devices for deep longitudinal phenotyping.
View Article and Find Full Text PDFBundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz
August 2019
Background: In Germany, there is widespread use of smartphones that can be operated via voice assistants (VAs). Due to their increasing distribution, they hold the potential to influence health behavior at a population level.
Objectives: This study examines the response behavior of German-speaking VAs to questions on mental and physical health as well as interpersonal violence.