Best-practice data models harmonize semantics and data structure of medical variables in clinical or epidemiological studies. While there exist several published data sets, it remains challenging to find and reuse published eligibility criteria or other data items that match specific needs of a newly planned study or registry. A novel Internet-based method for rapid comparison of published data models was implemented to enable reuse, customization, and harmonization of item catalogs for the early planning and development phase of research databases.

A thorough analysis of clonal evolution commonly requires integration of diverse sources of data (e.g., karyotyping, next-generation sequencing, and clinical information). Subsequent to actual reconstruction of clonal evolution, detailed analysis and interpretation of the results are essential. Often, however, only few tumor samples per patient are available. Thus, information on clonal development and therapy effect may be incomplete. Furthermore, analysis of biallelic events-considered of high relevance with respect to disease course-can commonly only be realized by time-consuming analysis of the raw results and even raw sequencing data.

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.

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.

Information systems are a key success factor for medical research and healthcare. Currently, most of these systems apply heterogeneous and proprietary data models, which impede data exchange and integrated data analysis for scientific purposes. Due to the complexity of medical terminology, the overall number of medical data models is very high. At present, the vast majority of these models are not available to the scientific community. The objective of the Portal of Medical Data Models (MDM, https://medical-data-models.org) is to foster sharing of medical data models.

The application of next-generation sequencing in research and particularly in clinical routine requires valid variant calling results. However, evaluation of several commonly used tools has pointed out that not a single tool meets this requirement. False positive as well as false negative calls necessitate additional experiments and extensive manual work. Intelligent combination and output filtration of different tools could significantly improve the current situation.

Pediatric low-grade gliomas (pLGG) show heterogeneous responses to MAPK inhibitors (MAPKi) in clinical trials. Thus, more complex stratification biomarkers are needed to identify patients likely to benefit from MAPKi therapy. Here, we identify MAPK-related genes enriched in MAPKi-sensitive cell lines using the GDSC dataset and apply them to calculate class-specific MAPKi sensitivity scores (MSSs) via single-sample gene set enrichment analysis. The MSSs discriminate MAPKi-sensitive and non-sensitive cells in the GDSC dataset and significantly correlate with response to MAPKi in an independent PDX dataset. The MSSs discern gliomas with varying MAPK alterations and are higher in pLGG compared to other pediatric CNS tumors. Heterogenous MSSs within pLGGs with the same MAPK alteration identify proportions of potentially sensitive patients. The MEKi MSS predicts treatment response in a small set of pLGG patients treated with trametinib. High MSSs correlate with a higher immune cell infiltration, with high expression in the microglia compartment in single-cell RNA sequencing data, while low MSSs correlate with low immune infiltration and increased neuronal score. The MSSs represent predictive tools for the stratification of pLGG patients and should be prospectively validated in clinical trials. Our data supports a role for microglia in the response to MAPKi.

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.

Standardization in clinical documentation can increase efficiency and can save time and resources.

Artifact chimeric reads are enriched in next-generation sequencing data generated from formalin-fixed paraffin-embedded (FFPE) samples. Previous work indicated that these reads are characterized by erroneous split-read support that is interpreted as evidence of structural variants. Thus, a large number of false-positive structural variants are detected. To our knowledge, no tool is currently available to specifically call or filter structural variants in FFPE samples. To overcome this gap, we developed 2 R packages: SimFFPE and FilterFFPE.

Spiral drawings on paper are used as routine measures in hospitals to assess Parkinson's Disease motor deficiencies. In the age of emerging mobile health tools and Artificial Intelligence a comprehensive digital setup enables granular biomarker analyses and improved differential diagnoses in movement disorders. This study aims to evaluate on discriminatory features among Parkison's Disease patients, healthy subjects and diverse movement disorders. Overall, 24 Parkinson's Disease patients, 27 healthy controls and 26 patients with similar differential diagnoses were assessed with a novel tablet-based system. It utilizes an integrative assessment by combining a structured symptoms questionnaire-the Parkinson's Disease Non-Motor Scale-and 2-handed spiral drawing captured on a tablet device. Three different classification tasks were evaluated: Parkinson's Disease patients versus healthy control group (Task 1), all Movement disorders versus healthy control group (Task 2) and Parkinson's Disease patients versus diverse other movement disorder patients (Task 3). To systematically study feature importances of digital biomarkers a Machine Learning classifier is cross-validated and interpreted with SHapley Additive exPlanations (SHAP) values. The number of non-motor symptoms differed significantly for Tasks 1 and 2 but not for Task 3. The proposed drawing features partially differed significantly for all three tasks. The diagnostic accuracy was on average 94.0% in Task 1, 89.4% in Task 2, and 72% in Task 3. While the accuracy in Task 3 only using the symptom questionnaire was close to the baseline, it greatly improved when including the tablet-based features from 60 to 72%. The accuracies for all three tasks were significantly improved by integrating the two modalities. These results show that tablet-based drawing features can not only be captured by consumer grade devices, but also capture specific features to Parkinson's Disease that significantly improve the diagnostic accuracy compared to the symptom questionnaire. Therefore, the proposed system provides an objective type of disease characterization of movement disorders, which could be utilized for home-based assessments as well.Clinicaltrials.gov Study-ID: NCT03638479.

Recent studies suggest that BRAFV600-mutated melanomas in particular respond to dual anti-programmed cell death protein 1 (PD-1) and anti-cytotoxic T lymphocyte-associated protein 4 (CTLA-4) immune checkpoint inhibition (ICI). Here we identified an over-representation of interleukin (IL)-17-type 17 helper T (TH17) gene expression signatures (GES) in BRAFV600-mutated tumors. Moreover, high baseline IL-17 GES consistently predicted clinical responses in dual-ICI-treated patient cohorts but not in mono anti-CTLA-4 or anti-PD-1 ICI cohorts. High IL-17 GES corresponded to tumor infiltration with T cells and neutrophils. Accordingly, high neutrophil infiltration correlated with clinical response specifically to dual ICI, and tumor-associated neutrophils also showed strong IL-17-TH17 pathway activity and T cell activation capacity. Both the blockade of IL-17A and the depletion of neutrophils impaired dual-ICI response and decreased T cell activation. Finally, high IL-17A levels in the blood of patients with melanoma indicated a higher global TH17 cytokine profile preceding clinical response to dual ICI but not to anti-PD-1 monotherapy, suggesting a future role as a biomarker for patient stratification.

To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.

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.

This study assessed the efficacy and tolerability of intravenous ibuprofen in the improvement of post-operative pain control and the reduction of opioid usage. Patients were randomly divided into placebo, ibuprofen 400 mg and ibuprofen 800 mg groups. All patients received patient-controlled intravenous morphine analgesia after surgery. The first dose of study drugs was administered intravenously 30 min before the end of surgery and then every 6 hours, for a total of 8 doses after surgery. The primary endpoint of this study was the mean amount of morphine used during the first 24 hours after surgery. Morphine use was reduced significantly in the ibuprofen 800 mg group compared with the placebo group (P = 0.04). Tramadol use was reduced significantly in the ibuprofen 400 mg and ibuprofen 800 mg groups compared with the placebo group (P < 0.01). The area under the curve of visual analog scale pain ratings was not different between groups. Safety assessments and side effects were not different between the three groups. Intravenous ibuprofen 800 mg was associated with a significant reduction in morphine requirements, and it was generally well tolerated for postoperative pain management in patients undergoing radical cervical cancer surgery.

StudyPortal was implemented as the first multilingual search platform for geographic visualization of clinical trials and scientific articles. The platform queries information from ClinicalTrials.gov, PubMed, a geodatabase and geographic maps to enable geospatial study search and real-time rendering of study locations or research networks on a map. Thus, disease-specific clinical studies or whole research networks can be shown in a geographic proximity. Moreover, a semantic layer enables multilingual disease input and autosuggestion of medical terms based on the Unified Medical Language System. The portal is accessible on https://studyportal.uni-muenster.de. This paper presents details on implementation of the novel search platform, its search evaluation and future work.

During viral infection, tight regulation of CD8+ T-cell functions determines the outcome of the disease. Recently, others and we determined that the natural killer (NK) cells kill hyperproliferative CD8+ T cells in the context of viral infection, but molecules that are involved in shaping the regulatory capability of NK cells remain virtually unknown. Here we used mice lacking the Fc-receptor common gamma chain (FcRγ, FcεRIγ, Fcer1g-/- mice) to determine the role of Fc-receptor and NK-receptor signaling in the process of CD8+ T-cell regulation. We found that the lack of FcRγ on NK cells limits their ability to restrain virus-specific CD8+ T cells and that the lack of FcRγ in Fcer1g-/- mice leads to enhanced CD8+ T-cell responses and rapid control of the chronic docile strain of the lymphocytic choriomeningitis virus (LCMV). Mechanistically, FcRγ stabilized the expression of NKp46 but not that of other killer cell-activating receptors on NK cells. Although FcRγ did not influence the development or activation of NK cell during LCMV infection, it specifically limited their ability to modulate CD8+ T-cell functions. In conclusion, we determined that FcRγ plays an important role in regulating CD8+ T-cell functions during chronic LCMV infection.

Screening for eligible patients continues to pose a great challenge for many clinical trials. This has led to a rapidly growing interest in standardizing computable representations of eligibility criteria (EC) in order to develop tools that leverage data from electronic health record (EHR) systems. Although laboratory procedures (LP) represent a common entity of EC that is readily available and retrievable from EHR systems, there is a lack of interoperable data models for this entity of EC. A public, specialized data model that utilizes international, widely-adopted terminology for LP, e.g. Logical Observation Identifiers Names and Codes (LOINC®), is much needed to support automated screening tools.

GFI1 is a transcriptional repressor and plays a pivotal role in regulating the differentiation of hematopoietic stem cells (HSCs) towards myeloid and lymphoid cells. Serial transplantation of Gfi1 deficient HSCs repopulated whole hematopoietic system but in a competitive setting involving wild-type HSCs, they lose this ability. The underlying mechanisms to this end are poorly understood. To better understand this, we used different mouse strains that express either loss of both Gfi1 alleles (Gfi1-KO), with reduced expression of GFI1 (GFI1-KD) or wild-type Gfi1/GFI1 (Gfi1-/GFI1-WT; corresponding to the mouse and human alleles). We observed that loss of Gfi1 or reduced expression of GFI1 led to a two to four fold lower number of HSCs (defined as Lin-Sca1+c-Kit+CD150+CD48-) compared to GFI1-WT mice. To study the functional influence of different levels of GFI1 expression on HSCs function, HSCs from Gfi1-WT (expressing CD45.1 + surface antigens) and HSCs from GFI1-KD or -KO (expressing CD45.2 + surface antigens) mice were sorted and co-transplanted into lethally irradiated host mice. Every 4 weeks, CD45.1+ and CD45.2 + on different lineage mature cells were analyzed by flow cytometry. At least 16 weeks later, mice were sacrificed, and the percentage of HSCs and progenitors including GMPs, CMPs and MEPs in the total bone marrow cells was calculated as well as their CD45.1 and CD45.2 expression. In the case of co-transplantation of GFI1-KD with Gfi1-WT HSCs, the majority of HSCs (81% ± 6%) as well as the majority of mature cells (88% ± 10%) originated from CD45.2 + GFI1-KD HSCs. In the case of co-transplantation of Gfi1-KO HSCs with Gfi1-WT HSCs, the majority of HSCs originated from CD45.2+ and therefore from Gfi1-KO (61% ± 20%); however, only a small fraction of progenitors and mature cells originated from Gfi1-KO HSCs (<1%). We therefore in summary propose that GFI1 has a dose-dependent role in the self-renewal and differentiation of HSCs.

Growth Factor Independence 1 (GFI1) is a transcription factor with an important role in the regulation of development of myeloid and lymphoid cell lineages and was implicated in the development of myelodysplastic syndrome (MDS) and acute myeloid leukaemia (AML). Reduced expression of GFI1 or presence of the GFI1-36N (serine replaced with asparagine) variant leads to epigenetic changes in human and murine AML blasts and accelerated the development of leukaemia in a murine model of human MDS and AML. We and other groups previously showed that the GFI1-36N allele or reduced expression of GFI1 in human AML blasts is associated with an inferior prognosis. Using GFI1-36S, -36N -KD, NUP98-HOXD13-tg mice and curcumin (a natural histone acetyltransferase inhibitor (HATi)), we now demonstrate that expansion of GFI1-36N or -KD, NUP98-HODXD13 leukaemic cells can be delayed. Curcumin treatment significantly reduced AML progression in GFI1-36N or -KD mice and prolonged AML-free survival. Of note, curcumin treatment had no effect in GFI1-36S, NUP98-HODXD13 expressing mice. On a molecular level, curcumin treatment negatively affected open chromatin structure in the GFI1-36N or -KD haematopoietic cells but not GFI1-36S cells. Taken together, our study thus identified a therapeutic role for curcumin treatment in the treatment of AML patients (homo or heterozygous for GFI1-36N or reduced GFI1 expression) and possibly improved therapy outcome.