Europe's Largest Research Infrastructure for Curated Medical Data Models with Semantic Annotations.

BACKGROUND: Structural metadata from the majority of clinical studies and routine health care systems is currently not yet available to the scientific community. OBJECTIVE: To provide an overview of available contents in the Portal of Medical Data Models (MDM Portal). METHODS: The MDM Portal is a registered European information infrastructure for research and health care, and its contents are curated and semantically annotated by medical experts. It enables users to search, view, discuss, and download existing medical data models. RESULTS: The most frequent keyword is "clinical trial" (n = 18,777), and the most frequent disease-specific keyword is "breast neoplasms" (n = 1,943). Most data items are available in English (n = 545,749) and German (n = 109,267). Manually curated semantic annotations are available for 805,308 elements (554,352 items, 58,101 item groups, and 192,855 code list items), which were derived from 25,257 data models. In total, 1,609,225 Unified Medical Language System (UMLS) codes have been assigned, with 66,373 unique UMLS codes. CONCLUSION: To our knowledge, the MDM Portal constitutes Europe's largest collection of medical data models with semantically annotated elements. As such, it can be used to increase compatibility of medical datasets and can be utilized as a large expert-annotated medical text corpus for natural language processing.

Unused potential of lipid-lowering therapy in very high-risk patients with atherosclerotic cardiovascular disease. A retrospective data analysis.

BACKGROUND: Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death in Europe. Although the 2019 European Society of Cardiology/European Atherosclerosis Society Guidelines for the management of dyslipidaemias claim a target low-density lipoprotein cholesterol (LDL-C) value of <55 mg/dL for very high-risk patients by use of lipid-lowering therapy (LLT) and lifestyle adaptations, the target level achievement is not satisfactory. We examined LLT use in ASCVD patients exceeding LDL-C target levels at admission and its adaptations at discharge. METHODS AND RESULTS: Between January 2017 and February 2020, 1091 patients with LDL-C >100 mg/dL and ASCVD defined as diagnosis of angina pectoris (AP, n = 179), acute myocardial infarction (AMI, n = 317), chronic ischemic heart disease (CHD, n = 195), or peripheral artery disease (PAD, n = 400) were extracted from hospital records. LLT use on admission and discharge as well as recommendations on lifestyle and nutrition were analysed. On admission, 51% of the patients were not taking LLT. At discharge, 91% were prescribed statins and 87% were advised on lifestyle adaptation and/or pharmacological treatment. High-intensity statin use at discharge was present in 63% of the AP-group, 92% of the AMI-group, 62% of the CHD-group and 71% of the PAD-group. Ezetimibe was present in 16% and proprotein convertase subtilisin/kexin 9 inhibitors (PCSK9i) in 1%. However, of those on high-intensity statin, 25% remained on insufficient statin dosage. CONCLUSION: Switch to high-intensity statins and use of ezetimibe and PCSK9i was low in chronic ASCVD patients. Even though statin intake was high in high-risk patients, target levels were still not reached.

Smartwatch Versus Routine Tremor Documentation: Descriptive Comparison.

We addressed the limitations of subjective clinical tremor assessment by comparing routine neurological evaluation with a Tremor Occurrence Score derived from smartwatch sensor data, among 142 participants with Parkinson disease and 77 healthy controls. Our findings highlight the potential of smartwatches for automated tremor detection as a valuable addition to conventional assessments, applicable in both clinical and home settings.

Systematic analysis of ChatGPT, Google search and Llama 2 for clinical decision support tasks.

It is likely that individuals are turning to Large Language Models (LLMs) to seek health advice, much like searching for diagnoses on Google. We evaluate clinical accuracy of GPT-3·5 and GPT-4 for suggesting initial diagnosis, examination steps and treatment of 110 medical cases across diverse clinical disciplines. Moreover, two model configurations of the Llama 2 open source LLMs are assessed in a sub-study. For benchmarking the diagnostic task, we conduct a naïve Google search for comparison. Overall, GPT-4 performed best with superior performances over GPT-3·5 considering diagnosis and examination and superior performance over Google for diagnosis. Except for treatment, better performance on frequent vs rare diseases is evident for all three approaches. The sub-study indicates slightly lower performances for Llama models. In conclusion, the commercial LLMs show growing potential for medical question answering in two successive major releases. However, some weaknesses underscore the need for robust and regulated AI models in health care. Open source LLMs can be a viable option to address specific needs regarding data privacy and transparency of training.

Integration of Patient-Reported Outcome Data Collected Via Web Applications and Mobile Apps Into a Nation-Wide COVID-19 Research Platform Using Fast Healthcare Interoperability Resources: Development Study.

BACKGROUND: The Network University Medicine projects are an important part of the German COVID-19 research infrastructure. They comprise 2 subprojects: COVID-19 Data Exchange (CODEX) and Coordination on Mobile Pandemic Apps Best Practice and Solution Sharing (COMPASS). CODEX provides a centralized and secure data storage platform for research data, whereas in COMPASS, expert panels were gathered to develop a reference app framework for capturing patient-reported outcomes (PROs) that can be used by any researcher. OBJECTIVE: Our study aims to integrate the data collected with the COMPASS reference app framework into the central CODEX platform, so that they can be used by secondary researchers. Although both projects used the Fast Healthcare Interoperability Resources (FHIR) standard, it was not used in a way that data could be shared directly. Given the short time frame and the parallel developments within the CODEX platform, a pragmatic and robust solution for an interface component was required. METHODS: We have developed a means to facilitate and promote the use of the German Corona Consensus (GECCO) data set, a core data set for COVID-19 research in Germany. In this way, we ensured semantic interoperability for the app-collected PRO data with the COMPASS app. We also developed an interface component to sustain syntactic interoperability. RESULTS: The use of different FHIR types by the COMPASS reference app framework (the general-purpose FHIR Questionnaire) and the CODEX platform (eg, Patient, Condition, and Observation) was found to be the most significant obstacle. Therefore, we developed an interface component that realigns the Questionnaire items with the corresponding items in the GECCO data set and provides the correct resources for the CODEX platform. We extended the existing COMPASS questionnaire editor with an import function for GECCO items, which also tags them for the interface component. This ensures syntactic interoperability and eases the reuse of the GECCO data set for researchers. CONCLUSIONS: This paper shows how PRO data, which are collected across various studies conducted by different researchers, can be captured in a research-compatible way. This means that the data can be shared with a central research infrastructure and be reused by other researchers to gain more insights about COVID-19 and its sequelae.

Machine Learning in the Parkinson's disease smartwatch (PADS) dataset.

The utilisation of smart devices, such as smartwatches and smartphones, in the field of movement disorders research has gained significant attention. However, the absence of a comprehensive dataset with movement data and clinical annotations, encompassing a wide range of movement disorders including Parkinson's disease (PD) and its differential diagnoses (DD), presents a significant gap. The availability of such a dataset is crucial for the development of reliable machine learning (ML) models on smart devices, enabling the detection of diseases and monitoring of treatment efficacy in a home-based setting. We conducted a three-year cross-sectional study at a large tertiary care hospital. A multi-modal smartphone app integrated electronic questionnaires and smartwatch measures during an interactive assessment designed by neurologists to provoke subtle changes in movement pathologies. We captured over 5000 clinical assessment steps from 504 participants, including PD, DD, and healthy controls (HC). After age-matching, an integrative ML approach combining classical signal processing and advanced deep learning techniques was implemented and cross-validated. The models achieved an average balanced accuracy of 91.16% in the classification PD vs. HC, while PD vs. DD scored 72.42%. The numbers suggest promising performance while distinguishing similar disorders remains challenging. The extensive annotations, including details on demographics, medical history, symptoms, and movement steps, provide a comprehensive database to ML techniques and encourage further investigations into phenotypical biomarkers related to movement disorders.

Single-cell profiling reveals preferential reduction of memory B cell subsets in cladribine patients that correlates with treatment response.

Background: Cladribine is a highly effective immunotherapy that is applied in two short-term courses over 2 years and reduces relapse rate and disease progression in patients with relapsing multiple sclerosis (MS). Despite the short treatment period, cladribine has a long-lasting effect on disease activity even after recovery of lymphocyte counts, suggesting a yet undefined long-term immune modulating effect. Objectives: Our aim was to provide a more profound understanding of the detailed effects of cladribine, also with regard to the patients' therapy response. Design: We performed an open-labeled, explorative, prospective, single-arm study, in which we examined the detailed lymphocyte subset development of MS patients who received cladribine treatment over 2 years. Methods: We performed in-depth profiling of the effects of cladribine on peripheral blood lymphocytes by flow cytometry, bulk RNA sequencing of sorted CD4+ T cells, CD8+ T cells, and CD19+ B cells as well as single-cell RNA sequencing of peripheral blood mononuclear cells in a total of 23 MS patients before and at different time points up to 24 months after cladribine treatment. Data were correlated with clinical and cranial magnetic resonance imaging (MRI) disease activity. Results: Flow cytometry revealed a predominant and sustained reduction of memory B cells compared to other B cell subsets after cladribine treatment, whereas T cell subsets were slightly reduced in a more uniform pattern. The overall transcriptional profile of total blood B cells exhibited reduced expression of proinflammatory and T cell activating genes, while single-cell transcriptomics revealed that gene expression within each B cell cluster did not change over time. Stable patients displayed stronger reductions of selected memory B cell clusters as compared to patients with clinical or cerebral MRI disease activity. Conclusion: We describe a pronounced and sustained effect of cladribine on the memory B cell compartment, and the resulting change in B cell subset composition causes a significant alteration of B cell transcriptional profiles resulting in reduced proinflammatory and T cell activating capacities. The extent of reduction in selected memory B cell clusters by cladribine may predict treatment response.

Mouse models of pediatric high-grade gliomas with MYCN amplification reveal intratumoral heterogeneity and lineage signatures.

Pediatric high-grade gliomas of the subclass MYCN (HGG-MYCN) are highly aggressive tumors frequently carrying MYCN amplifications, TP53 mutations, or both alterations. Due to their rarity, such tumors have only recently been identified as a distinct entity, and biological as well as clinical characteristics have not been addressed specifically. To gain insights into tumorigenesis and molecular profiles of these tumors, and to ultimately suggest alternative treatment options, we generated a genetically engineered mouse model by breeding hGFAP-cre::Trp53Fl/Fl::lsl-MYCN mice. All mice developed aggressive forebrain tumors early in their lifetime that mimic human HGG-MYCN regarding histology, DNA methylation, and gene expression. Single-cell RNA sequencing revealed a high intratumoral heterogeneity with neuronal and oligodendroglial lineage signatures. High-throughput drug screening using both mouse and human tumor cells finally indicated high efficacy of Doxorubicin, Irinotecan, and Etoposide as possible therapy options that children with HGG-MYCN might benefit from.

GEFAAR: a generic framework for the analysis of antimicrobial resistance providing statistics and cluster analyses.

Easy access to antimicrobial resistance data and meaningful visualization is essential to guide the empirical antimicrobial treatment and to promote the rational use of antimicrobial agents. Currently available solutions are commonly externally hosted, centralized systems. However, there is a need for close monitoring by local analysis tools. To fill this gap, we developed GEFAAR-a generic framework for the analysis of antimicrobial resistance data. Following the example of the German Robert Koch Institute (RKI), an interactive web-application is provided to determine basic pathogen and resistance statistics. In addition to the RKI's externally maintained database, our application provides a generic framework to import tabular data and to analyze them safely in a local environment. Moreover, our application offers an intuitive web-based user interface to visualize resistance trend analysis as well as advanced cluster analyses on species- or clinic/unit level to generate alerts of potential transmission events.

Knockout of the Cardiac Transcription Factor NKX2-5 Results in Stem Cell-Derived Cardiac Cells with Typical Purkinje Cell-like Signal Transduction and Extracellular Matrix Formation.

The human heart controls blood flow, and therewith enables the adequate supply of oxygen and nutrients to the body. The correct function of the heart is coordinated by the interplay of different cardiac cell types. Thereby, one can distinguish between cells of the working myocardium, the pace-making cells in the sinoatrial node (SAN) and the conduction system cells in the AV-node, the His-bundle or the Purkinje fibres. Tissue-engineering approaches aim to generate hiPSC-derived cardiac tissues for disease modelling and therapeutic usage with a significant improvement in the differentiation quality of myocardium and pace-making cells. The differentiation of cells with cardiac conduction system properties is still challenging, and the produced cell mass and quality is poor. Here, we describe the generation of cardiac cells with properties of the cardiac conduction system, called conduction system-like cells (CSLC). As a primary approach, we introduced a CrispR-Cas9-directed knockout of the NKX2-5 gene in hiPSC. NKX2-5-deficient hiPSC showed altered connexin expression patterns characteristic for the cardiac conduction system with strong connexin 40 and connexin 43 expression and suppressed connexin 45 expression. Application of differentiation protocols for ventricular- or SAN-like cells could not reverse this connexin expression pattern, indicating a stable regulation by NKX2-5 on connexin expression. The contraction behaviour of the hiPSC-derived CSLCs was compared to hiPSC-derived ventricular- and SAN-like cells. We found that the contraction speed of CSLCs resembled the expected contraction rate of human conduction system cells. Overall contraction was reduced in differentiated cells derived from NKX2-5 knockout hiPSC. Comparative transcriptomic data suggest a specification of the cardiac subtype of CSLC that is distinctly different from ventricular or pacemaker-like cells with reduced myocardial gene expression and enhanced extracellular matrix formation for improved electrical insulation. In summary, knockout of NKX2-5 in hiPSC leads to enhanced differentiation of cells with cardiac conduction system features, including connexin expression and contraction behaviour.

Germ line variant GFI1-36N affects DNA repair and sensitizes AML cells to DNA damage and repair therapy.

ABSTRACT: Growth factor independence 1 (GFI1) is a DNA-binding transcription factor and a key regulator of hematopoiesis. GFI1-36N is a germ line variant, causing a change of serine (S) to asparagine (N) at position 36. We previously reported that the GFI1-36N allele has a prevalence of 10% to 15% among patients with acute myeloid leukemia (AML) and 5% to 7% among healthy Caucasians and promotes the development of this disease. Using a multiomics approach, we show here that GFI1-36N expression is associated with increased frequencies of chromosomal aberrations, mutational burden, and mutational signatures in both murine and human AML and impedes homologous recombination (HR)-directed DNA repair in leukemic cells. GFI1-36N exhibits impaired binding to N-Myc downstream-regulated gene 1 (Ndrg1) regulatory elements, causing decreased NDRG1 levels, which leads to a reduction of O6-methylguanine-DNA-methyltransferase (MGMT) expression levels, as illustrated by both transcriptome and proteome analyses. Targeting MGMT via temozolomide, a DNA alkylating drug, and HR via olaparib, a poly-ADP ribose polymerase 1 inhibitor, caused synthetic lethality in human and murine AML samples expressing GFI1-36N, whereas the effects were insignificant in nonmalignant GFI1-36S or GFI1-36N cells. In addition, mice that received transplantation with GFI1-36N leukemic cells treated with a combination of temozolomide and olaparib had significantly longer AML-free survival than mice that received transplantation with GFI1-36S leukemic cells. This suggests that reduced MGMT expression leaves GFI1-36N leukemic cells particularly vulnerable to DNA damage initiating chemotherapeutics. Our data provide critical insights into novel options to treat patients with AML carrying the GFI1-36N variant.

Comparison of German Translations of the System Usability Scale - Which to Take?

The System Usability Scale (SUS) is a reliable tool for usability measurement and evaluation. Since its original language is English, a translation is required before a target group can answer it in their native language. The challenge of translating questionnaires lies in the preservation of its original properties. Different versions of a German SUS have been proposed and are currently in use. Objective of this work is to find and compare available German translations. Four versions were found and compared in terms of the translation process and the exact wording of the translation. Only the version of Gao et al. has been systematically validated, but has an unnatural wording. Although not validated yet, the proposed version of Rummel et al. is a good compromise between wording and methodically clean development. The version of Lohmann and Schäffer is the close runner up, as it may improve the wording at the expense of methodological accuracy. Since the version of Rauer gives no information about its translation process, it is considered least preferred of the four compared translations.

ChatGPT: The transformative influence of generative AI on science and healthcare.

In an age where technology is evolving at a sometimes incomprehensibly rapid pace, the liver community must adjust and learn to embrace breakthroughs with an open mind in order to benefit from potentially transformative influences on our science and practice. The Journal of Hepatology has responded to novel developments in artificial intelligence (AI) by recruiting experts in the field to serve on the Editorial Board. Publications introducing novel AI technology are no longer uncommon in our journal and are among the most highly debated and possibly practice-changing papers across a broad range of scientific disciplines, united by their focus on liver disease. As AI is rapidly evolving, this expert paper will focus on educating our readership on large language models and their possible impact on our research practice and clinical outlook, outlining both challenges and opportunities in the field. "To improve is to change; to be perfect is to change often." - Winston S. Churchill.

Which risk factors significantly influence the outcome of traumatic brain injured patients with alcohol use disorder?

PURPOSE: Every year, approximately 10 million people worldwide suffer a traumatic brain injury that leads to hospitalization or mortality. Chronic and acute alcohol intoxication increase the risk of developing traumatic brain injury. Alcohol use disorder (AUD) as a predictor of outcome in neurosurgical patients and the definition of risk factors have been sparsely addressed so far. This study aims to improve the understanding of the effects of alcohol use disorder in the context of neurosurgical therapy. METHODS: This study included patients admitted to Münster University Hospital with a traumatic brain injury and alcohol use disorder from January 1, 2010, to December 31, 2018. Univariate and multivariate analyses were performed to identify risk factors for a poorer outcome, assessed by the Glasgow Outcome Score. RESULTS: Of the 197 patients included, 156 (79%) were male, and 41 (21%) were female, with a median age of 49 years (IQR 38-58 years). In multivariate analyses, age (p < 0.001), the occurrence of a new neurologic deficit (p < 0.001), the development of hydrocephalus (p = 0.005), and CT-graphic midline shift due to intracerebral hemorrhage (p = 0.008) emerged as significant predictors of a worse outcome (GOS 1-3). In addition, the level of blood alcohol concentration correlated significantly with the occurrence of seizures (p = 0.009). CONCLUSIONS: Early identification of risk factors in patients with alcohol use disorder and traumatic brain injury is crucial to improve the outcome. In this regard, the occurrence of hydrocephalus or seizures during the inpatient stay should be considered as cause of neurological deterioration in this patient group.

The tumor suppressor CREBBP and the oncogene MYCN cooperate to induce malignant brain tumors in mice.

The tumor suppressor and chromatin modifier cAMP response element-binding protein binding protein (CREBBP) and v-myc avian myelocytomatosis viral oncogene neuroblastoma derived homolog (MYCN), a member of the MYC oncogene family, are critically involved in brain development. Both genes are frequently mutated in the same tumor entities, including high-grade glioma and medulloblastoma. Therefore, we hypothesized that alterations in both genes cooperate to induce brain tumor formation. For further investigation, hGFAP-cre::CrebbpFl/Fl::lsl-MYCN mice were generated, which combine Crebbp deletion with overexpression of MYCN in neural stem cells (NSCs). Within eight months, these animals developed aggressive forebrain tumors. The first tumors were detectable in the olfactory bulbs of seven-day-old mice. This location raises the possibility that presumptive founder cells are derived from the ventricular-subventricular zone (V-SVZ). To examine the cellular biology of these tumors, single-cell RNA sequencing was performed, which revealed high intratumoral heterogeneity. Data comparison with reference CNS cell types indicated the highest similarity of tumor cells with transit-amplifying NSCs or activated NSCs of the V-SVZ. Consequently, we analyzed V-SVZ NSCs of our mouse model aiming to confirm that the tumors originate from this stem cell niche. Mutant V-SVZ NSCs showed significantly increased cell viability and proliferation as well as reduced glial and neural differentiation in vitro compared to control cells. In summary, we demonstrate the oncogenic potential of a combined loss of function of CREBBP and overexpression of MYCN in this cell population. hGFAP-cre::CrebbpFl/Fl::lsl-MYCN mice thus provide a valuable tool to study tumor-driving mechanisms in a key neural stem/ progenitor cell niche.

Mitochondrial DNA mutations in Medulloblastoma.

To date, several studies on genomic events underlying medulloblastoma (MB) biology have expanded our understanding of this tumour entity and led to its division into four groups-WNT, SHH, group 3 (G3) and group 4 (G4). However, there is little information about the relevance of pathogenic mitochondrial DNA (mtDNA) mutations and their consequences across these. In this report, we describe the case of a female patient with MB and a mitochondriopathy, followed by a study of mtDNA variants in MB groups. After being diagnosed with G4 MB, the index patient was treated in line with the HIT 2000 protocol with no indications of relapse after five years. Long-term side effects of treatment were complemented by additional neurological symptoms and elevated lactate levels ten years later, resulting in suspected mitochondrial disease. This was confirmed by identifying a mutation in the MT-TS1 gene which appeared homoplasmic in patient tissue and heteroplasmic in the patient's mother. Motivated by this case, we explored mtDNA mutations across 444 patients from ICGC and HIT cohorts. While there was no statistically significant enrichment of mutations in one MB group, both cohorts encompassed a small group of patients harbouring potentially deleterious mtDNA variants. The case presented here highlights the possible similarities between sequelae caused by MB treatment and neurological symptoms of mitochondrial dysfunction, which may apply to patients across all MB groups. In the context of the current advances in characterising and interpreting mtDNA aberrations, recognising affected patients could enhance our future knowledge regarding the mutations' impact on carcinogenesis and cancer treatment.

Dose-dependent expression of GFI1 alters metabolism in the haematopoietic progenitors and MLL::AF9-induced leukaemic cells.

Growth factor independence 1 (GFI1) is a transcriptional repressor protein that plays an essential role in the differentiation of myeloid and lymphoid progenitors. We and other groups have shown that GFI1 has a dose-dependent role in the initiation, progression, and prognosis of acute myeloid leukaemia (AML) patients by inducing epigenetic changes. We now demonstrate a novel role for dose-dependent GFI1 expression in regulating metabolism in haematopoietic progenitor and leukaemic cells. Using in-vitro and ex-vivo murine models of MLL::AF9-induced human AML and extra-cellular flux assays, we now demonstrate that a lower GFI1 expression enhances oxidative phosphorylation rate via upregulation of the FOXO1- MYC axis. Our findings underscore the significance of therapeutic exploitation in GFI1-low-expressing leukaemia cells by targeting oxidative phosphorylation and glutamine metabolism.