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We aimed to prospectively assess changes in chronic stress among young adults transitioning from high school to university or working life. A population-based cohort in Munich and Dresden (Germany) was followed from age 16–18 (2002–2003) to age 20–23 (2007–2009) (n = 1688). Using the Trier Inventory for the Assessment of Chronic Stress, two dimensions of stress at university or work were assessed: work overload and work discontent. In the multiple ordinal generalized estimating equations, socio-demographics, stress outside the workplace, and job history were additionally considered. At follow-up, 52% of the population were university students. Work overload increased statistically significantly from first to second follow-up, while work discontent remained constant at the population level. Students, compared to employees, reported a larger increase in work overload (adjusted odds ratio (OR): 1.33; 95% confidence interval (95% CI): 1.07, 1.67), while work discontent did not differ between the groups. In conclusion, work overload increases when young adults transition from school to university/job life, with university students experiencing the largest increase.
Aims: To analyze the relationship between exposure to chlorinated and aromatic organic solvents and malignant lymphoma in a multi-centre, population-based case-control study.
Methods: Male and female patients with malignant lymphoma (n=710) between 18 and 80 years of age were prospectively recruited in six study regions in Germany (Ludwigshafen /Upper Palatinate, Heidelberg/ Rhine-Neckar-County, Wurzburg/ Lower Frankonia, Hamburg, Bielefeld/ Gutersloh, and Munich). For each newly recruited lymphoma case, a gender, region and age-matched (+/- 1 year of birth) population control was drawn from the population registers. In a structured personal interview, we elicited a complete occupational history, including every occupational period that lasted at least one year. On the basis of job task-specific supplementary questionnaires, a trained occupational physician assessed the exposure to chlorinated hydrocarbons (trichloroethylene, tetrachloroethylene, dichloromethane, carbon tetrachloride) and aromatic hydrocarbons (benzene, toluene, xylene, styrene). Odds ratios (OR) and 95% confidence intervals (CI) were calculated using conditional logistic regression analysis, adjusted for smoking (in pack years) and alcohol consumption. To increase the statistical power, patients with specific lymphoma subentities were additionally compared with the entire control group using unconditional logistic regression analysis.
Results: We observed a statistically significant association between high exposure to chlorinated hydrocarbons and malignant lymphoma (Odds ratio = 2.1; 95% confidence interval 1.1-4.3). In the analysis of lymphoma subentities, a pronounced risk elevation was found for follicular lymphoma and marginal zone lymphoma. When specific substances were considered, the association between trichloroethylene and malignant lymphoma was of borderline statistical significance. Aromatic hydrocarbons were not significantly associated with the lymphoma diagnosis.
Conclusions: In accordance with the literature, this data point to a potential etiologic role of chlorinated hydrocarbons (particularly trichloroethylene) and malignant lymphoma. Chlorinated hydrocarbons might affect specific lymphoma subentities differentially. Our study does not support a strong association between aromatic hydrocarbons (benzene, toluene, xylene, or styrene) and the diagnosis of a malignant lymphoma.
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.
The charged particle community is looking for techniques exploiting proton interactions instead of X-ray absorption for creating images of human tissue. Due to multiple Coulomb scattering inside the measured object it has shown to be highly non-trivial to achieve sufficient spatial resolution. We present imaging of biological tissue with a proton microscope. This device relies on magnetic optics, distinguishing it from most published proton imaging methods. For these methods reducing the data acquisition time to a clinically acceptable level has turned out to be challenging. In a proton microscope, data acquisition and processing are much simpler. This device even allows imaging in real time. The primary medical application will be image guidance in proton radiosurgery. Proton images demonstrating the potential for this application are presented. Tomographic reconstructions are included to raise awareness of the possibility of high-resolution proton tomography using magneto-optics.
The title solvated salt, C29H41N2+·Br-·2CH2Cl2 was obtained from the reaction of the Arduengo-type carbene 1,3-bis(2,6-diisopropylphenyl)-1,3-dihydro-4,5-dimethyl-2H-imidazol-2-ylidene with Si2Br6 in dichloromethane. The complete cation is generated by a crystallographic mirror plane and the dihedral angle between the five-membered ring and the benzene ring is 89.8 (6)°; the dihedral angle between the benzene rings is 40.7 (2)°. The anion also lies on the mirror plane and both dichloromethane molecules are disordered across the mirror plane over two equally occupied orientations. In the crystal, the cations are linked to the anions via C-H...Br hydrogen bonds.
Using a data sample of e+e− collision data corresponding to an integrated luminosity of 2.93 fb−1 collected with the BESIII detector at a center-of-mass energy of s=3.773GeV, we search for the singly Cabibbo-suppressed decays D0→π0π0π0, π0π0η, π0ηη and ηηη using the double tag method. The absolute branching fractions are measured to be B(D0→π0π0π0)=(2.0±0.4±0.3)×10−4, B(D0→π0π0η)=(3.8±1.1±0.7)×10−4 and B(D0→π0ηη)=(7.3±1.6±1.5)×10−4 with the statistical significances of 4.8σ, 3.8σ and 5.5σ, respectively, where the first uncertainties are statistical and the second ones systematic. No significant signal of D0→ηηη is found, and the upper limit on its decay branching fraction is set to be B(D0→ηηη)<1.3×10−4 at the 90% confidence level.
The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.