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Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Background & Aims: In ACLF patients, an adequate risk stratification is essential, especially for liver transplant allocation, since ACLF is associated with high short-term mortality. The CLIF-C ACLF score is the best prognostic model to predict outcome in ACLF patients. While lung failure is generally regarded as signum malum in ICU care, this study aims to evaluate and quantify the role of pulmonary impairment on outcome in ACLF patients.
Methods: In this retrospective study, 498 patients with liver cirrhosis and admission to IMC/ICU were included. ACLF was defined according to EASL-CLIF criteria. Pulmonary impairment was classified into three groups: unimpaired ventilation, need for mechanical ventilation and defined pulmonary failure. These factors were analysed in different cohorts, including a propensity score-matched ACLF cohort.
Results: Mechanical ventilation and pulmonary failure were identified as independent risk factors for increased short-term mortality. In matched ACLF patients, the presence of pulmonary failure showed the highest 28-day mortality (83.7%), whereas mortality rates in ACLF with mechanical ventilation (67.3%) and ACLF without pulmonary impairment (38.8%) were considerably lower (p < .001). Especially in patients with pulmonary impairment, the CLIF-C ACLF score showed poor predictive accuracy. Adjusting the CLIF-C ACLF score for the grade of pulmonary impairment improved the prediction significantly.
Conclusions: This study highlights that not only pulmonary failure but also mechanical ventilation is associated with worse prognosis in ACLF patients. The grade of pulmonary impairment should be considered in the risk assessment in ACLF patients. The new score may be useful in the selection of patients for liver transplantation.
Intrahepatic cholangiocarcinoma (iCCA) is the most frequent subtype of cholangiocarcinoma (CCA), and the incidence has globally increased in recent years. In contrast to surgically treated iCCA, data on the impact of fibrosis on survival in patients undergoing palliative chemotherapy are missing. We retrospectively analyzed the cases of 70 patients diagnosed with iCCA between 2007 and 2020 in our tertiary hospital. Histopathological assessment of fibrosis was performed by an expert hepatobiliary pathologist. Additionally, the fibrosis-4 score (FIB-4) was calculated as a non-invasive surrogate marker for liver fibrosis. For overall survival (OS) and progression-free survival (PFS), Kaplan–Meier curves and Cox-regression analyses were performed. Subgroup analyses revealed a median OS of 21 months (95% CI = 16.7–25.2 months) and 16 months (95% CI = 7.6–24.4 months) for low and high fibrosis, respectively (p = 0.152). In non-cirrhotic patients, the median OS was 21.8 months (95% CI = 17.1–26.4 months), compared with 9.5 months (95% CI = 4.6–14.3 months) in cirrhotic patients (p = 0.007). In conclusion, patients with iCCA and cirrhosis receiving palliative chemotherapy have decreased OS rates, while fibrosis has no significant impact on OS or PFS. These patients should not be prevented from state-of-the-art first-line chemotherapy.
Objectives: Rising prevalence of multidrug-resistant organisms (MDRO) is a major health problem in patients with liver cirrhosis. The impact of MDRO colonization in liver transplantation (LT) candidates and recipients on mortality has not been determined in detail.
Methods: Patients consecutively evaluated and listed for LT in a tertiary German liver transplant center from 2008 to 2018 underwent screening for MDRO colonization including methicillin-resistant Staphylococcus aureus (MRSA), multidrug-resistant gram-negative bacteria (MDRGN), and vancomycin-resistant enterococci (VRE). MDRO colonization and infection status were obtained at LT evaluation, planned and unplanned hospitalization, three months upon graft allocation, or at last follow-up on the waiting list.
Results: In total, 351 patients were listed for LT, of whom 164 (47%) underwent LT after a median of 249 (range 0–1662) days. Incidence of MDRO colonization increased during waiting time for LT, and MRDO colonization was associated with increased mortality on the waiting list (HR = 2.57, p<0.0001. One patients was colonized with a carbapenem-resistant strain at listing, 9 patients acquired carbapenem-resistant gram-negative bacteria (CRGN) on the waiting list, and 4 more after LT. In total, 10 of these 14 patients died.
Conclusions: Colonization with MDRO is associated with increased mortality on the waiting list, but not in short-term follow-up after LT. Moreover, colonization with CRGN seems associated with high mortality in liver transplant candidates and recipients.
Background: Cumulative anticholinergic exposure, also known as anticholinergic burden, is associated with a variety of adverse outcomes. However, studies show that anticholinergic effects tend to be underestimated by prescribers, and anticholinergics are the most frequently prescribed potentially inappropriate medication in older patients. The grading systems and drugs included in existing scales to quantify anticholinergic burden differ considerably and do not adequately account for patients’ susceptibility to medications. Furthermore, their ability to link anticholinergic burden with adverse outcomes such as falls is unclear. This study aims to develop a prognostic model that predicts falls in older general practice patients, to assess the performance of several anticholinergic burden scales, and to quantify the added predictive value of anticholinergic symptoms in this context.
Methods: Data from two cluster-randomized controlled trials investigating medication optimization in older general practice patients in Germany will be used. One trial (RIME, n = 1,197) will be used for the model development and the other trial (PRIMUM, n = 502) will be used to externally validate the model. A priori, candidate predictors will be selected based on a literature search, predictor availability, and clinical reasoning. Candidate predictors will include socio-demographics (e.g. age, sex), morbidity (e.g. single conditions), medication (e.g. polypharmacy, anticholinergic burden as defined by scales), and well-being (e.g. quality of life, physical function). A prognostic model including sociodemographic and lifestyle-related factors, as well as variables on morbidity, medication, health status, and well-being, will be developed, whereby the prognostic value of extending the model to include additional patient-reported symptoms will be also assessed. Logistic regression will be used for the binary outcome, which will be defined as “no falls” vs. “≥1 fall” within six months of baseline, as reported in patient interviews. Discussion: As the ability of different anticholinergic burden scales to predict falls in older patients is unclear, this study may provide insights into their relative importance as well as into the overall contribution of anticholinergic symptoms and other patient characteristics. The results may support general practitioners in their clinical decision-making and in prescribing fewer medications with anticholinergic properties.
Cardiac rehabilitation (CR) is a multidisciplinary intervention including patient assessment and medical actions to promote stabilization, management of cardiovascular risk factors, vocational support, psychosocial management, physical activity counselling, and prescription of exercise training. Millions of people with cardiac implantable electronic devices live in Europe and their numbers are progressively increasing, therefore, large subsets of patients admitted in CR facilities have a cardiac implantable electronic device. Patients who are cardiac implantable electronic devices recipients are considered eligible for a CR programme. This is not only related to the underlying heart disease but also to specific issues, such as psychological adaptation to living with an implanted device and, in implantable cardioverter-defibrillator patients, the risk of arrhythmia, syncope, and sudden cardiac death. Therefore, these patients should receive special attention, as their needs may differ from other patients participating in CR. As evidence from studies of CR in patients with cardiac implantable electronic devices is sparse, detailed clinical practice guidelines are lacking. Here, we aim to provide practical recommendations for CR in cardiac implantable electronic devices recipients in order to increase CR implementation, efficacy, and safety in this subset of patients.
Resilience has been defined as the maintenance or quick recovery of mental health during and after times of adversity. How to operationalize resilience and to determine the factors and processes that lead to good long-term mental health outcomes in stressor-exposed individuals is a matter of ongoing debate and of critical importance for the advancement of the field. One of the biggest challenges for implementing an outcome-based definition of resilience in longitudinal observational study designs lies in the fact that real-life adversity is usually unpredictable and that its substantial qualitative as well as temporal variability between subjects often precludes defining circumscribed time windows of inter-individually comparable stressor exposure relative to which the maintenance or recovery of mental health can be determined. To address this pertinent issue, we propose to frequently and regularly monitor stressor exposure (E) and mental health problems (P) throughout a study's observation period [Frequent Stressor and Mental Health Monitoring (FRESHMO)-paradigm]. On this basis, a subject's deviation at any single monitoring time point from the study sample's normative E–P relationship (the regression residual) can be used to calculate that subject's current mental health reactivity to stressor exposure (“stressor reactivity,” SR). The SR score takes into account the individual extent of experienced adversity and is comparable between and within subjects. Individual SR time courses across monitoring time points reflect intra-individual temporal variability in SR, where periods of under-reactivity (negative SR score) are associated with accumulation of fewer mental health problems than is normal for the sample. If FRESHMO is accompanied by regular measurement of potential resilience factors, temporal changes in resilience factors can be used to predict SR time courses. An increase in a resilience factor measurement explaining a lagged decrease in SR can then be considered to index a process of adaptation to stressor exposure that promotes a resilient outcome (an allostatic resilience process). This design principle allows resilience research to move beyond merely determining baseline predictors of resilience outcomes, which cannot inform about how individuals successfully adjust and adapt when confronted with adversity. Hence, FRESHMO plus regular resilience factor monitoring incorporates a dynamic-systems perspective into resilience research.
Objective To explore factors that potentially impact external validation performance while developing and validating a prognostic model for hospital admissions (HAs) in complex older general practice patients.
Study design and setting Using individual participant data from four cluster-randomised trials conducted in the Netherlands and Germany, we used logistic regression to develop a prognostic model to predict all-cause HAs within a 6-month follow-up period. A stratified intercept was used to account for heterogeneity in baseline risk between the studies. The model was validated both internally and by using internal-external cross-validation (IECV).
Results Prior HAs, physical components of the health-related quality of life comorbidity index, and medication-related variables were used in the final model. While achieving moderate discriminatory performance, internal bootstrap validation revealed a pronounced risk of overfitting. The results of the IECV, in which calibration was highly variable even after accounting for between-study heterogeneity, agreed with this finding. Heterogeneity was equally reflected in differing baseline risk, predictor effects and absolute risk predictions.
Conclusions Predictor effect heterogeneity and differing baseline risk can explain the limited external performance of HA prediction models. With such drivers known, model adjustments in external validation settings (eg, intercept recalibration, complete updating) can be applied more purposefully.
Trial registration number PROSPERO id: CRD42018088129.
DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma
(2021)
Background: Therapies based on targeting immune checkpoints have revolutionized the treatment of metastatic melanoma in recent years. Still, biomarkers predicting long-term therapy responses are lacking. Methods: A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled us to develop a machine learning classifier based on CpG sites, specific for latent methylation components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data were processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP). Results: We provide evidence that DNA methylation signatures of tumor tissue from cutaneous metastases are predictive for therapy response to immune checkpoint inhibition in patients with stage IV metastatic melanoma. Conclusions: These results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy.
Plants, fungi and algae are important components of global biodiversity and are fundamental to all ecosystems. They are the basis for human well-being, providing food, materials and medicines. Specimens of all three groups of organisms are accommodated in herbaria, where they are commonly referred to as botanical specimens.The large number of specimens in herbaria provides an ample, permanent and continuously improving knowledge base on these organisms and an indispensable source for the analysis of the distribution of species in space and time critical for current and future research relating to global biodiversity. In order to make full use of this resource, a research infrastructure has to be built that grants comprehensive and free access to the information in herbaria and botanical collections in general. This can be achieved through digitization of the botanical objects and associated data.The botanical research community can count on a long-standing tradition of collaboration among institutions and individuals. It agreed on data standards and standard services even before the advent of computerization and information networking, an example being the Index Herbariorum as a global registry of herbaria helping towards the unique identification of specimens cited in the literature.In the spirit of this collaborative history, 51 representatives from 30 institutions advocate to start the digitization of botanical collections with the overall wall-to-wall digitization of the flat objects stored in German herbaria. Germany has 70 herbaria holding almost 23 million specimens according to a national survey carried out in 2019. 87% of these specimens are not yet digitized. Experiences from other countries like France, the Netherlands, Finland, the US and Australia show that herbaria can be comprehensively and cost-efficiently digitized in a relatively short time due to established workflows and protocols for the high-throughput digitization of flat objects.Most of the herbaria are part of a university (34), fewer belong to municipal museums (10) or state museums (8), six herbaria belong to institutions also supported by federal funds such as Leibniz institutes, and four belong to non-governmental organizations. A common data infrastructure must therefore integrate different kinds of institutions.Making full use of the data gained by digitization requires the set-up of a digital infrastructure for storage, archiving, content indexing and networking as well as standardized access for the scientific use of digital objects. A standards-based portfolio of technical components has already been developed and successfully tested by the Biodiversity Informatics Community over the last two decades, comprising among others access protocols, collection databases, portals, tools for semantic enrichment and annotation, international networking, storage and archiving in accordance with international standards. This was achieved through the funding by national and international programs and initiatives, which also paved the road for the German contribution to the Global Biodiversity Information Facility (GBIF).Herbaria constitute a large part of the German botanical collections that also comprise living collections in botanical gardens and seed banks, DNA- and tissue samples, specimens preserved in fluids or on microscope slides and more. Once the herbaria are digitized, these resources can be integrated, adding to the value of the overall research infrastructure. The community has agreed on tasks that are shared between the herbaria, as the German GBIF model already successfully demonstrates.We have compiled nine scientific use cases of immediate societal relevance for an integrated infrastructure of botanical collections. They address accelerated biodiversity discovery and research, biomonitoring and conservation planning, biodiversity modelling, the generation of trait information, automated image recognition by artificial intelligence, automated pathogen detection, contextualization by interlinking objects, enabling provenance research, as well as education, outreach and citizen science.We propose to start this initiative now in order to valorize German botanical collections as a vital part of a worldwide biodiversity data pool.