Guía integral digital para la elaboración y (auto)evaluación del Trabajo de Fin de Grado en lenguas, literaturas y culturas modernas

FALSEsubmitte

Clinical and virological characteristics of hospitalised COVID-19 patients in a German tertiary care centre during the first wave of the SARS-CoV-2 pandemic: a prospective observational study.

Funder: Humboldt-Universität zu Berlin (1034)PURPOSE: Adequate patient allocation is pivotal for optimal resource management in strained healthcare systems, and requires detailed knowledge of clinical and virological disease trajectories. The purpose of this work was to identify risk factors associated with need for invasive mechanical ventilation (IMV), to analyse viral kinetics in patients with and without IMV and to provide a comprehensive description of clinical course. METHODS: A cohort of 168 hospitalised adult COVID-19 patients enrolled in a prospective observational study at a large European tertiary care centre was analysed. RESULTS: Forty-four per cent (71/161) of patients required invasive mechanical ventilation (IMV). Shorter duration of symptoms before admission (aOR 1.22 per day less, 95% CI 1.10-1.37, p < 0.01) and history of hypertension (aOR 5.55, 95% CI 2.00-16.82, p < 0.01) were associated with need for IMV. Patients on IMV had higher maximal concentrations, slower decline rates, and longer shedding of SARS-CoV-2 than non-IMV patients (33 days, IQR 26-46.75, vs 18 days, IQR 16-46.75, respectively, p < 0.01). Median duration of hospitalisation was 9 days (IQR 6-15.5) for non-IMV and 49.5 days (IQR 36.8-82.5) for IMV patients. CONCLUSIONS: Our results indicate a short duration of symptoms before admission as a risk factor for severe disease that merits further investigation and different viral load kinetics in severely affected patients. Median duration of hospitalisation of IMV patients was longer than described for acute respiratory distress syndrome unrelated to COVID-19

Clinical and virological characteristics of hospitalised COVID-19 patients in a German tertiary care centre during the first wave of the SARS-CoV-2 pandemic: a prospective observational study

Purpose: Adequate patient allocation is pivotal for optimal resource management in strained healthcare systems, and requires detailed knowledge of clinical and virological disease trajectories. The purpose of this work was to identify risk factors associated with need for invasive mechanical ventilation (IMV), to analyse viral kinetics in patients with and without IMV and to provide a comprehensive description of clinical course. Methods: A cohort of 168 hospitalised adult COVID-19 patients enrolled in a prospective observational study at a large European tertiary care centre was analysed. Results: Forty-four per cent (71/161) of patients required invasive mechanical ventilation (IMV). Shorter duration of symptoms before admission (aOR 1.22 per day less, 95% CI 1.10-1.37, p < 0.01) and history of hypertension (aOR 5.55, 95% CI 2.00-16.82, p < 0.01) were associated with need for IMV. Patients on IMV had higher maximal concentrations, slower decline rates, and longer shedding of SARS-CoV-2 than non-IMV patients (33 days, IQR 26-46.75, vs 18 days, IQR 16-46.75, respectively, p < 0.01). Median duration of hospitalisation was 9 days (IQR 6-15.5) for non-IMV and 49.5 days (IQR 36.8-82.5) for IMV patients. Conclusions: Our results indicate a short duration of symptoms before admission as a risk factor for severe disease that merits further investigation and different viral load kinetics in severely affected patients. Median duration of hospitalisation of IMV patients was longer than described for acute respiratory distress syndrome unrelated to COVID-19

Ergänzende Informationen zum Buchkapitel „Konzeption interprofessioneller Curricula in der Pflegeausbildung: wissenschaftsbasierte Empfehlungen für die Entwicklung und Umsetzung“

Onlinematerial zum Buchkapitel „Konzeption interprofessioneller Curricula in der Pflegeausbildung: wissenschaftsbasierte Empfehlungen für die Entwicklung und Umsetzung“ In: Brühe, Roland; von Gahlen-Hoops, Wolfgang (Hg.). Handbuch Pflegedidaktik II. Pflegedidaktisch denken. 2024. Bielefeld: utb / transcript. S. 189-218

The SRG/eROSITA All-Sky Survey: Weak-Lensing of eRASS1 Galaxy Clusters in KiDS-1000 and Consistency Checks with DES Y3 & HSC-Y3

International audienceWe aim to participate in the calibration of the X-ray photon count rate to halo mass scaling relation of galaxy clusters selected in the first eROSITA All-Sky Survey on the Western Galactic Hemisphere (eRASS1) using KiDS-1000 weak-lensing (WL) data. We measure the radial shear profiles around eRASS1 galaxy clusters using background galaxies in KiDS-1000, as well as the cluster member contamination. Furthermore we provide consistency checks with the other stage-III WL surveys who take part in the eRASS1 mass calibration, DES Y3 and HSC-Y3. We determine the cluster member contamination of eRASS1 clusters present in KiDS-1000 based on source number density profiles, where we account for the obscuration caused by cluster galaxies. The extracted shear profiles, together with the contamination model and the lens sample selection, are then analysed through a Bayesian population model. We calibrate the WL mass bias parameter by analysing realistic synthetic shear profiles from mock cluster catalogues. Our consistency checks between KiDS-1000 and DES Y3 & HSC-Y3 include the comparison of contamination-corrected density contrast profiles employing the union of background sources around common clusters, as well as the individual scaling relation results. We present a global contamination model for eRASS1 clusters in KiDS-1000 and the calibration results of the X-ray photon count rate to halo mass relation. The results of the WL mass bias parameter show that the uncertainty of the multiplicative shear bias dominates the systematic error budget at low clusters redshifts while the uncertainty of our contamination model does at high ones. The cross-checks between the three WL surveys show that they are statistically consistent with each other. This enables for the first time cosmological constraints from clusters calibrated by three state-of-the-art WL surveys. (abridged

Critical Illness and Systemic Inflammation Are Key Risk Factors of Severe Acute Kidney Injury in Patients With COVID-19

Introduction: Acute kidney injury (AKI) is an important complication in COVID-19, but its precise etiology has not fully been elucidated. Insights into AKI mechanisms may be provided by analyzing the temporal associations of clinical parameters reflecting disease processes and AKI development. Methods: We performed an observational cohort study of 223 consecutive COVID-19 patients treated at 3 sites of a tertiary care referral center to describe the evolvement of severe AKI (Kidney Disease: Improving Global Outcomes stage 3) and identify conditions promoting its development. Descriptive statistics and explanatory multivariable Cox regression modeling with clinical parameters as time-varying covariates were used to identify risk factors of severe AKI. Results: Severe AKI developed in 70 of 223 patients (31%) with COVID-19, of which 95.7% required kidney replacement therapy. Patients with severe AKI were older, predominantly male, had more comorbidities, and displayed excess mortality. Severe AKI occurred exclusively in intensive care unit patients, and 97.3% of the patients developing severe AKI had respiratory failure. Mechanical ventilation, vasopressor therapy, and inflammatory markers (serum procalcitonin levels and leucocyte count) were independent time-varying risk factors of severe AKI. Increasing inflammatory markers displayed a close temporal association with the development of severe AKI. Sensitivity analysis on risk factors of AKI stage 2 and 3 combined confirmed these findings. Conclusion: Severe AKI in COVID-19 was tightly coupled with critical illness and systemic inflammation and was not observed in milder disease courses. These findings suggest that traditional systemic AKI mechanisms rather than kidney-specific processes contribute to severe AKI in COVID-19

Clinical and virological characteristics of hospitalised COVID-19 patients in a German tertiary care centre during the first wave of the SARS-CoV-2 pandemic: a prospective observational study

Purpose!#!Adequate patient allocation is pivotal for optimal resource management in strained healthcare systems, and requires detailed knowledge of clinical and virological disease trajectories. The purpose of this work was to identify risk factors associated with need for invasive mechanical ventilation (IMV), to analyse viral kinetics in patients with and without IMV and to provide a comprehensive description of clinical course.!##!Methods!#!A cohort of 168 hospitalised adult COVID-19 patients enrolled in a prospective observational study at a large European tertiary care centre was analysed.!##!Results!#!Forty-four per cent (71/161) of patients required invasive mechanical ventilation (IMV). Shorter duration of symptoms before admission (aOR 1.22 per day less, 95% CI 1.10-1.37, p &amp;lt; 0.01) and history of hypertension (aOR 5.55, 95% CI 2.00-16.82, p &amp;lt; 0.01) were associated with need for IMV. Patients on IMV had higher maximal concentrations, slower decline rates, and longer shedding of SARS-CoV-2 than non-IMV patients (33 days, IQR 26-46.75, vs 18 days, IQR 16-46.75, respectively, p &amp;lt; 0.01). Median duration of hospitalisation was 9 days (IQR 6-15.5) for non-IMV and 49.5 days (IQR 36.8-82.5) for IMV patients.!##!Conclusions!#!Our results indicate a short duration of symptoms before admission as a risk factor for severe disease that merits further investigation and different viral load kinetics in severely affected patients. Median duration of hospitalisation of IMV patients was longer than described for acute respiratory distress syndrome unrelated to COVID-19

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac