The Involvement of Cx43 in JNK1/2-Mediated Endothelial Mechanotransduction and Human Plaque Progression.

Atherosclerotic 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"). Phosphorylated levels of MAPKs (c-Jun N-terminal kinase (JNK1/2), extracellular signal-regulated kinase (ERK), and p38 kinase (p38K)) were quantified by flow cytometry, showing the activation of JNK1/2 and ERK. THP-1 cell adhesion under non-uniform SS was suppressed by the inhibition of JNK1/2, not of ERK. Immunofluorescence staining and quantitative real-time PCR demonstrated an induction of c-Jun and c-Fos and of Cx43 in endothelial cells by non-uniform SS, and the latter was abolished by JNK1/2 inhibition. Furthermore, plaque inflammation was analyzed in human carotid plaques (n = 40) using immunohistochemistry and quanti-gene RNA-assays, revealing elevated Cx43+ cell counts in vulnerable compared to stable plaques. Cx43+ cell burden in the plaque shoulder correlated with intraplaque neovascularization and lipid core size, while an inverse correlation was observed with fibrous cap thickness. Our results constitute the first report that JNK1/2 mediates Cx43 mechanoinduction in endothelial cells by atheroprone shear stress and that Cx43 is expressed in human carotid plaques. The correlation of Cx43+ cell counts with markers of plaque vulnerability implies its contribution to plaque progression.

[Responses of German-speaking voice assistants to questions about health issues]. / Antwortverhalten von deutschsprachigen Sprachassistenten auf Gesundheitsfragen.

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. METHODS: Common VAs received nine standardized health questions. Responses were evaluated in terms of the categories "Recognizing the question," "Respectful responsiveness," and "Referring to support services." RESULTS: Fifty-one VAs were tested on 44 devices. Mental health issues were mostly recognized and answered with respect. Regarding expressed suicidal thoughts, only one VA did not refer to a specific crisis line. With respect to "interpersonal violence," only one VA recognized the expressed problems. In terms of physical health, only one VA showed respectful responses in all three tested healthcare areas, leading help seekers to additional healthcare services (e.g., hospitals, pharmacies). For some complaints, the VAs gave behavioral advice. CONCLUSION: VAs are able to recognize health issues, to respond respectably, and to guide those in need of care to specific healthcare and counselling services. However, responsiveness is insufficient and inconsistent. Future research should investigate how the responsiveness of VAs can be improved to provide the best possible support to people in crisis.

Genetic studies in clonal haematopoiesis, myelodysplastic neoplasms and acute myeloid leukaemia - a practical guide to WHO-HAEM5.

In 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. This guide navigates through the genetic abnormalities underlying myeloid neoplasms which are required to be detected for classification according to WHO-HAEM5 and provides diagnostic algorithms.

Overview on WHO-HAEM5 and the diagnostic relevance of genetic alterations for the classification.

The landscape of haematological malignancies is constantly evolving, driven by advances in our understanding of their genetic basis. This has cumulated within the 5th Edition of the World Health Organization (WHO) Classification of Haematolymphoid Tumours published in short form in 2022 [1, 2] and being available in full length both as "Blue Book" (in print expected early 2024) as well as web-based classification (see: https://tumourclassification.iarc.who.int/welcome/). Similarly, the importance of genetic alterations for the classification is highlighted in other classification systems related to haematologic neoplasms [3-5]. In this special issue of the Medizinische Genetik, we present a comprehensive overview of the genetic alterations contributing to the classification of haematolymphoid neoplasms in the 5th Edition of the WHO classification (WHO-HAEM5) and its diagnostic relevance in the context of various haematological malignancies.

Genetic alterations in myeloproliferative and myelodysplastic/myeloproliferative neoplasms - a practical guide to WHO-HAEM5.

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. This guide provides an overview of the overlapping commonalities among these entities, as well as their unique characteristics.

How artificial intelligence might disrupt diagnostics in hematology in the near future.

Artificial 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. In the last years, substantial progress has been made in automated image classification, reaching even superhuman level in some instances. Despite the increasing awareness of the importance of the genetic context, the diagnosis in hematology is still mainly based on the evaluation of the phenotype. Either by the analysis of microscopic images of cells in cytomorphology or by the analysis of cell populations in bidimensional plots obtained by flow cytometry. Here, AI algorithms not only spot details that might escape the human eye, but might also identify entirely new ways of interpreting these images. With the introduction of high-throughput next-generation sequencing in molecular genetics, the amount of available information is increasing exponentially, priming the field for the application of machine learning approaches. The goal of all the approaches is to allow personalized and informed interventions, to enhance treatment success, to improve the timeliness and accuracy of diagnoses, and to minimize technically induced misclassifications. The potential of AI-based applications is virtually endless but where do we stand in hematology and how far can we go?