Disease- and sex-specific differences in patients with heart valve disease: a proteome study

Pressure overload in patients with aortic valve stenosis and volume overload in mitral valve regurgitation trigger specific forms of cardiac remodeling; however, little is known about similarities and differences in myocardial proteome regulation. We performed proteome profiling of 75 human left ventricular myocardial biopsies (aortic stenosis = 41, mitral regurgitation = 17, and controls = 17) using high-resolution tandem mass spectrometry next to clinical and hemodynamic parameter acquisition. In patients of both disease groups, proteins related to ECM and cytoskeleton were more abundant, whereas those related to energy metabolism and proteostasis were less abundant compared with controls. In addition, disease group-specific and sex-specific differences have been observed. Male patients with aortic stenosis showed more proteins related to fibrosis and less to energy metabolism, whereas female patients showed strong reduction in proteostasis-related proteins. Clinical imaging was in line with proteomic findings, showing elevation of fibrosis in both patient groups and sex differences. Disease- and sex-specific proteomic profiles provide insight into cardiac remodeling in patients with heart valve disease and might help improve the understanding of molecular mechanisms and the development of individualized treatment strategies

HPI-DHC @ BC8 SympTEMIST Track: Detection and Normalization of Symptom Mentions with SpanMarker and xMEN

<h3><strong>Abstract</strong></h3><p>Signs and symptoms of patients are frequently reported in clinical text documents. Therefore, accurate automated extraction of symptom information is essential for their integration into downstream clinical applications. In this work, we describe our contribution to the BioCreative VIII SympTEMIST shared task, a benchmark for the detection and normalization of symptom mentions in Spanish-language clinical case reports. Our systems for subtasks 1 and 2 are built upon two state-of-the-art, open-source information extraction tools: (1) SpanMarker for named entity recognition with document-level context and (2) xMEN for normalizing symptom mentions to their corresponding SNOMED CT code. For subtask 1, our best submitted run achieves an F1 score of 0.7363, which exceeds the median across all submissions by more than 3pp. Our experiments underline the positive impact of including document-level context for named entity taggers. For subtask 2, our best system for entity normalization obtains an accuracy of 0.6070, an improvement of more than 8pp over the median.</p><p> </p><p>This article is part of the <a href="https://zenodo.org/doi/10.5281/zenodo.10103190">Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models</a>.</p&gt

High-Performance In-Memory Genome Data AnalysisHow In-Memory Database Technology Accelerates Personalized Medicine /

XXI, 223 p. 78 illus.online resource

Towards an integrated health research process: A cloud-based approach

Today, health research and health care generate a steadily increasing amount of data. Making these available for secondary use cases is essential for efficiency gains in health research, e.g. by reducing time-and costs-intensive acquisition of data. In this contribution, we introduce our SAHRA software platform enabling reproducible research, e.g. by combining multiple data sources, performing data de-identification, and content filtering. We define an innovative research process combining retrospective and prospective research for the first time. Thus, authorized users, e.g. clinical researchers, are able to gain access through our system relevant research data and to perform interactive analyses. As a result, existing sensitive health data is securely transformed into de-identified research data, which can be used to improve future health research

Knowledge bases and software support for variant interpretation in precision oncology

Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process