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The neutron capture cross section of 58Ni was measured at the neutron time of flight facility n_TOF at CERN, from 27 meV to 400 keV neutron energy. Special care has been taken to identify all the possible sources of background, with the so-called neutron background obtained for the first time using high-precision GEANT4 simulations. The energy range up to 122 keV was treated as the resolved resonance region, where 51 resonances were identified and analyzed by a multilevel R-matrix code SAMMY. Above 122 keV the code SESH was used in analyzing the unresolved resonance region of the capture yield. Maxwellian averaged cross sections were calculated in the temperature range of kT = 5 – 100 keV, and their astrophysical implications were investigated.
The slow neutron capture process (s-process) is responsible for producing about half of the elemental abundances heavier than iron in the universe. Neutron capture cross sections on stable isotopes are a key nuclear physics input for s-process studies. The 72Ge(n, γ) cross section has an important influence on production of isotopes between Ge and Zr during s-process in massive stars and therefore experimental data are urgently required. 72Ge(n, γ) was measured at the neutron time-of-flight facility n_TOF (CERN) for the first time at stellar energies. The measurement was performed using an enriched 72GeO2 sample at a flight path of 185m with a set of liquid scintillation detectors (C6D6). The motivation, experiment and current status of the data analysis are reported.
Formalin‐fixed, paraffin‐embedded (FFPE ), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here, we developed a pressure cycling technology (PCT )‐SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PC a) and diffuse large B‐cell lymphoma (DLBCL ) samples. We show that the proteome patterns of FFPE PC a tissue samples and their analogous fresh‐frozen (FF ) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PC a tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored between 1 and 15 years in a biobank and show a high degree of the proteome pattern similarity between two types of histological regions in small FFPE samples, that is, punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of a certain degree of biological variations. Applying the method to two independent DLBCL cohorts, we identified myeloperoxidase, a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PC a and DLBCL have been discovered.
Epigenetic neural glioblastoma enhances synaptic integration and predicts therapeutic vulnerability
(2023)
Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is nascent. We present an epigenetically defined neural signature of glioblastoma that independently affects patients survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals high abundance of stem cell-like malignant cells classified as oligodendrocyte precursor and neural precursor cell-like in high-neural glioblastoma. High-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature associates with decreased survival as well as increased functional connectivity and can be detected via DNA analytes and brain-derived neurotrophic factor in plasma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant.
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.
Accurate measurement of the standard 235U(n,f) cross section from thermal to 170 keV neutron energy
(2020)
An accurate measurement of the 235U(n,f) cross section from thermal to 170 keV of neutron energy has recently been performed at n_TOF facility at CERN using 6Li(n,t)4He and 10B(n,α)7Li as references. This measurement has been carried out in order to investigate a possible overestimation of the 235U fission cross section evaluation provided by most recent libraries between 10 and 30 keV. A custom experimental apparatus based on in-beam silicon detectors has been used, and a Monte Carlo simulation in GEANT4 has been employed to characterize the setup and calculate detectors efficiency. The results evidenced the presence of an overestimation in the interval between 9 and 18 keV and the new data may be used to decrease the uncertainty of 235U(n,f) cross section in the keV region.
233U is the fissile nuclei in the Th-U fuel cycle with a particularily small neutron capture cross setion which is on average about one order of magnitude lower than its fission cross section. Hence, the measurement of the 233U(n, γ) cross section relies on a method to accurately distinguish between capture and fission γ-rays. A measurement of the 233U α-ratio has been performed at the n_TOF facility at CERN using a so-called fission tagging setup, coupling n_TOF 's Total Absorption Calorimeter with a novel fission chamber to tag the fission γ-rays. The experimental setup is described and essential parts of the analysis are discussed. Finally, a preliminary 233U α-ratio is presented.
We have measured the capture cross section of the 155Gd and 157Gd isotopes between 0.025 eV and 1 keV. The capture events were recorded by an array of 4 C6D6 detectors, and the capture yield was deduced exploiting the total energy detection system in combination with the Pulse Height Weighting Techniques. Because of the large cross section around thermal neutron energy, 4 metallic samples of different thickness were used to prevent problems related to self-shielding. The samples were isotopically enriched, with a cross contamination of the other isotope of less than 1.14%. The capture yield was analyzed with an R-Matrix code to describe the cross section in terms of resonance parameters. Near thermal energies, the results are significantly different from evaluations and from previous time-of-flight experiments. The data from the present measurement at n_TOF are publicly available in the experimental nuclear reaction database EXFOR.
233U is of key importance among the fissile nuclei in the Th-U fuel cycle. A particularity of 233U is its small neutron capture cross-section, which is on average about one order of magnitude lower than the fission cross-section. The accuracy in the measurement of the 233U capture cross-section depends crucially on an efficient capture-fission discrimination, thus a combined set-up of fission and γ-detectors is needed. A measurement of the 233U capture cross-section and capture-to-fission ratio was performed at the CERN n_TOF facility. The Total Absorption Calorimeter (TAC) of n_TOF was employed as γ-detector coupled with a novel compact ionization chamber as fission detector. A brief description of the experimental set-up will be given, and essential parts of the analysis procedure as well as the preliminary response of the set-up to capture are presented and discussed.
New neutron cross section measurements of minor actinides have been performed recently in order to reduce the uncertainties in the evaluated data, which is important for the design of advanced nuclear reactors and, in particular, for determining their performance in the transmutation of nuclear waste. We have measured the 241Am(n,γ) cross section at the n_TOF facility between 0.2 eV and 10 keV with a BaF2 Total Absorption Calorimeter, and the analysis of the measurement has been recently concluded. Our results are in reasonable agreement below 20 eV with the ones published by C. Lampoudis et al. in 2013, who reported a 22% larger capture cross section up to 110 eV compared to experimental and evaluated data published before. Our results also indicate that the 241Am(n,γ) cross section is underestimated in the present evaluated libraries between 20 eV and 2 keV by 25%, on average, and up to 35% for certain evaluations and energy ranges.