Edvardsendjurhuus7519

2) Justice, uniqueness, and love; (3) Sign and language; (4) Dialogue and alterity; (5) Semiotic materiality; (6) Globalization and the trap of identity; (7) Human rights and rights of the other for a new humanism; (8) Ethics; (9) The World; (10) Outside the subject; (11) Responsibility and Substitution; (12) The face; (13) Fear of the other; (14) Alterity and justice; (15) Justice and proximity; (16) Literary writing; (17) Unjust justice; (18) Caring for the other.Over the last decade, humans have produced each year as much data as were produced throughout the entire history of humankind. These data, in quantities that exceed current analytical capabilities, have been described as "the new oil," an incomparable source of value. This is true for healthcare, as well. Conducting analyses of large, diverse, medical datasets promises the detection of previously unnoticed clinical correlations and new diagnostic or even therapeutic possibilities. However, using Big Data poses several problems, especially in terms of representing the uniqueness of each patient and expressing the differences between individuals, primarily gender and sex differences. The first two sections of the paper provide a definition of "Big Data" and illustrate the uses of Big Data in medicine. Subsequently, the paper explores the struggle to represent exhaustively the uniqueness of the patient through Big Data is highlighted prior to a deeper investigation of the digital representation of gender in personalized medicine. The final part of the paper put forward a series of recommendations for better approaching the complexity of gender in medical and clinical research involving Big Data for the creation or enhancement of personalized medicine services.

The online version contains supplementary material available at 10.1007/s00146-021-01234-9.

The online version contains supplementary material available at 10.1007/s00146-021-01234-9.

Due to the federally organized healthcare system in Switzerland, aortic interventions are performed in many different hospitals, presumably sometimes with avery small number of cases per institution. The aim of this study was to present the treatment reality of aortic diseases based on the aortic interventions recorded in Switzerland's vascular registry Swissvasc.

All interventions on the aorta that were carried out between January 2018 and December 2020 in Switzerland and entered in the Swissvasc registry were included in this overview. Interventions for the treatment of isolated pathologies of the iliac vessels as well as interventions on the ascending aorta and the proximal aortic arch were excluded.

Open aortic surgery was performed in 28 hospitals and endovascular aortic repair (EVAR) in 33. Just under half of these hospitals achieved the recommended minimum number of cases for abdominal aortic aneurysms (AAA) of 30open and endovascular interventions per year, which are currently discussed in the literature. In line with the literature 67% of the elective treatments for AAA were performed by EVAR. Corresponding to the current literature, 11% of the AAA were ruptured in the 3‑year observation period. In contrast to the recommendations of the current guidelines almost 60% of the ruptured AAA were treated by open repair. There was acertain tendency for aspontaneous centralization in the treatment of thoracic aortic pathologies as 87% of the interventions were carried out in only 5 hospitals.

This study shows that many clinics in Switzerland treat aortic pathologies, some with avery small caseload. Further investigations of the quality of care in the treatment of aortic pathologies are urgently needed.

This study shows that many clinics in Switzerland treat aortic pathologies, some with a very small caseload. Further investigations of the quality of care in the treatment of aortic pathologies are urgently needed.The prediction of hospital patients and outpatients with suspected arboviral infection individuals in research-limited settings of the urban areas is defined as a challenging process for clinicians. Dengue, Chikungunya, and Zika arboviruses have gained attention in recent years because of the high prevalence in the society and financial burden of major global health systems. selleckchem In this study, we proposed a machine learning algorithm based prediction model over retrospective medical records, which are named as SISA (the Severity Index for Suspected Arbovirus) and SISAL (the Severity Index for Suspected Arbovirus with Laboratory) datasets. Therefore, we aim to inform the clinicians about the use of machine learning with transfer learning success for diagnosis and comprehensive comparison of the classification performances over the SISA/SISAL datasets in the resource-limited settings that may cause to the small datasets of arboviral infection. In this study, Convolutional Neural Network and Long Short-Term Memory have achieved 100% accuracy and 1 of area under the curve (AUC) score, Fully Connected Deep Network has provided 92.86% accuracy and 0.969 AUC score in the SISAL dataset with transfer learning. Moreover, 98.73% accuracy and 0.988 AUC score were obtained by Convolutional Neural Network and Long Short-Term Memory for the SISA dataset. Furthermore, Linear Discriminant Analysis (shallow algorithm) has provided reaching up to 96.43% accuracy. Notably, deep learning based models have achieved improved performances compared to the previously reported study.Conventional methods for testing independence between two Gaussian vectors require sample sizes greater than the number of variables in each vector. Therefore, adjustments are needed for the high-dimensional situation, where the sample size is smaller than the number of variables in at least one of the compared vectors. It is critical to emphasize that the methods available in the literature are unable to control the Type I error probability under the nominal level. This fact is evidenced through an intensive simulation study presented in this paper. To cover this lack, we introduce a valid randomized test based on the Kronecker delta covariance matrices estimator. As an empirical application, based on a sample of companies listed on the stock exchange of Brazil, we test the independence between returns of stocks of different sectors in the COVID-19 pandemic context.