Refine
Year of publication
Language
- English (19)
Has Fulltext
- yes (19)
Is part of the Bibliography
- no (19)
Keywords
- Kollisionen schwerer Ionen (3)
- Quark Gluon Plasma (2)
- hadronic (2)
- heavy ion collisions (2)
- 5-lipoxygenase (1)
- ARDS (1)
- Atomic and Molecular Physics (1)
- Berlin Affective Word List (BAWL) (1)
- COVID-19 (1)
- Critical care (1)
Institute
- Physik (10)
- Medizin (7)
- Biochemie und Chemie (1)
- Biowissenschaften (1)
- Frankfurt Institute for Advanced Studies (FIAS) (1)
- Pharmazie (1)
- Psychologie (1)
- Sportwissenschaften (1)
Nonequilibrium models (three-fluid hydrodynamics, UrQMD, and quark molecular dynamics) are used to discuss the uniqueness of often proposed experimental signatures for quark matter formation in relativistic heavy ion collisions from the SPS via RHIC to LHC. It is demonstrated that these models - although they do treat the most interesting early phase of the collisions quite differently (thermalizing QGP vs. coherent color fields with virtual particles) -- all yield a reasonable agreement with a large variety of the available heavy ion data. Hadron/hyperon yields, including J/Psi meson production/suppression, strange matter formation, dileptons, and directed flow (bounce-off and squeeze-out) are investigated. Observations of interesting phenomena in dense matter are reported. However, we emphasize the need for systematic future measurements to search for simultaneous irregularities in the excitation functions of several observables in order to come close to pinning the properties of hot, dense QCD matter from data. The role of future experiments with the STAR and ALICE detectors is pointed out.
In power systems, flow allocation (FA) methods enable to allocate the usage and costs of the transmission grid to each single market participant. Based on predefined assumptions, the power flow is split into isolated generator-specific or producer-specific sub-flows. Two prominent FA methods, Marginal Participation (MP) and Equivalent Bilateral Exchanges (EBEs), build upon the linearized power flow and thus on the Power Transfer Distribution Factors (PTDFs). Despite their intuitive and computationally efficient concepts, they are restricted to networks with passive transmission elements only. As soon as a significant number of controllable transmission elements, such as high-voltage direct current (HVDC) lines, operate in the system, they lose their applicability. This work reformulates the two methods in terms of Virtual Injection Patterns (VIPs), which allows one to efficiently introduce a shift parameter q to tune contributions of net sources and net sinks in the network. In this work, major properties and differences in the methods are pointed out, and it is shown how the MP and EBE algorithms can be applied to generic meshed AC-DC electricity grids: by introducing a pseudo-impedance ω¯ , which reflects the operational state of controllable elements and allows one to extend the PTDF matrix under the assumption of knowing the current flow in the system. Basic properties from graph theory are used to solve for the pseudo-impedance in dependence of the position within the network. This directly enables, e.g., HVDC lines to be considered in the MP and EBE algorithms. The extended methods are applied to a low-carbon European network model (PyPSA-EUR) with a spatial resolution of 181 nodes and an 18% transmission expansion compared to today’s total transmission capacity volume. The allocations of MP and EBE show that countries with high wind potentials profit most from the transmission grid expansion. Based on the average usage of transmission system expansion, a method of distributing operational and capital expenditures is proposed. In addition, it is shown how injections from renewable resources strongly drive country-to-country allocations and thus cross-border electricity flows.
Since the domestication of the urus, 10.000 years ago, mankind utilizes bovine milk for different purposes. Besides usage as a nutrient also the external application of milk on skin has a long tradition going back to at least the ancient Aegypt with Cleopatra VII as a great exponent. In order to test whether milk has impact on skin physiology, cultures of human skin fibroblasts were exposed to commercial bovine milk. Our data show significant induction of proliferation by milk (max. 2,3-fold, EC50: 2,5% milk) without toxic effects. Surprisingly, bovine milk was identified as strong inducer of collagen 1A1 synthesis at both, the protein (4-fold, EC50: 0,09% milk) and promoter level. Regarding the underlying molecular pathways, we show functional activation of STAT6 in a p44/42 and p38-dependent manner. More upstream, we identified IGF-1 and insulin as key factors responsible for milk-induced collagen synthesis. These findings show that bovine milk contains bioactive molecules that act on human skin cells. Therefore, it is tempting to test the herein introduced concept in treatment of atrophic skin conditions induced e.g. by UV light or corticosteroids.
Background: The treatment of different skin conditions with spa waters is a long tradition dating back to at least late Hellenism. Interestingly, independent scientific examinations studying the effect of spa waters are scarce.
Objective: In the present in vitro study, we compared the effect of culture media supplemented with (a) thermal spa waters (La Roche-Posay, Avène) and (b) two natural mineral drinking waters (Heppinger, Adelholzener) on physiological parameters in HaCaT keratinocytes.
Methods: The different medium preparations were investigated with regard to cell proliferation and cell damage. Moreover, the impact on inflammation parameters with and without ultraviolet B (UVB) irradiation was examined.
Results: Two popular thermal spring waters were found to suppress cell proliferation and cell damage. Moreover, these waters reversed the induction of interleukin-6, as measured using enzyme-linked immunosorbent assay and promoter transactivation, and the formation of reactive oxygen species after UVB stimulation. Of note, the two natural mineral waters, which are distributed as drinking waters, had some effect on the above-mentioned parameters but to a lesser extent.
Conclusion: In summary, our results show that spa waters, and particularly those derived from thermal springs, reduce parameters associated with inflammation. It seems likely that trace elements such as selenium and zinc are critical for the observed effects.
Background & Aims: NAFLD is a growing health concern. The aim of the Fatty Liver Assessment in Germany (FLAG) study was to assess disease burden and provide data on the standard of care from secondary care. Methods: The FLAG study is an observational real-world study in patients with NAFLD enrolled at 13 centres across Germany. Severity of disease was assessed by non-invasive surrogate scores and data recorded at baseline and 12 months. Results: In this study, 507 patients (mean age 53 years; 47% women) were enrolled. According to fibrosis-4 index, 64%, 26%, and 10% of the patients had no significant fibrosis, indeterminate stage, and advanced fibrosis, respectively. Patients with advanced fibrosis were older, had higher waist circumferences, and higher aspartate aminotransferase and gamma-glutamyltransferase as well as ferritin levels. The prevalence of obesity, arterial hypertension, and type 2 diabetes increased with fibrosis stages. Standard of care included physical exercise >2 times per week in 17% (no significant fibrosis), 19% (indeterminate), and 6% (advanced fibrosis) of patients. Medication with either vitamin E, silymarin, or ursodeoxycholic acid was reported in 5%. Approximately 25% of the patients received nutritional counselling. According to the FibroScan-AST score, 17% of patients presented with progressive non-alcoholic steatohepatitis (n = 107). On follow-up at year 1 (n = 117), weight loss occurred in 47% of patients, of whom 17% lost more than 5% of body weight. In the weight loss group, alanine aminotransferase activities were reduced by 20%. Conclusions: This is the first report on NAFLD from a secondary-care real-world cohort in Germany. Every 10th patient presented with advanced fibrosis at baseline. Management consisted of best supportive care and lifestyle recommendations. The data highlight the urgent need for systematic health agenda in NAFLD patients. Lay summary: FLAG is a real-world cohort study that examined the liver disease burden in secondary and tertiary care. Herein, 10% of patients referred to secondary care for NAFLD exhibited advanced liver disease, whilst 64% had no significant liver scarring. These findings underline the urgent need to define patient referral pathways for suspected liver disease.
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
We predict the formation of highly dense baryon-rich resonance matter in Au+Au collisions at AGS energies. The final pion yields show observable signs for resonance matter. The Delta1232 resonance is predicted to be the dominant source for pions of small transverse momenta. Rescattering e ects consecutive excitation and deexcitation of Delta's lead to a long apparent life- time (> 10 fm/c) and rather large volumina (several 100 fm3) of the Delta-matter state. Heavier baryon resonances prove to be crucial for reaction dynamics and particle production at AGS.
Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behavior. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of >9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ~2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the Xchromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, p = 5.87×10-9; odds ratio = 1.12) and markers within ERBB2 (rs2517959, p = 4.53×10-9; odds ratio = 1.13). No significant X-chromosome associations were detected and X-linked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.
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.