Lessons learned from conducting a study of emotions and positive personality change in Syrian origin young adults who have recently resettled in the Netherlands

Post-traumatic growth is a compelling idea, yet extant research has often employed retrospective reports of change, rather than examining change over time. Research on samples of people that are traditionally seen as hard-to-reach are rare within personality psychology. In Karakter, we assessed a sample of Syrian origin young adults who recently resettled in the Netherlands (initial N = 168) four times over a 13-month period to examine experiences of adversity, emotions, and positive personality change. Here, we provide a detailed narrative of the research process, beginning with a description of how we incorporated open science practices in Karakter. We then turn to a discussion of the changes, challenges, and opportunities we encountered in the research. In doing so, we discuss conceptual and methodological considerations when examining personality change. We close with suggestions for researchers who are interested in conducting similar studies with populations that are underrecruited in the futur

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Artificial Intelligence in Cardiology—A Narrative Review of Current Status

Artificial intelligence (AI) is an integral part of clinical decision support systems (CDSS), offering methods to approximate human reasoning and computationally infer decisions. Such methods are generally based on medical knowledge, either directly encoded with rules or automatically extracted from medical data using machine learning (ML). ML techniques, such as Artificial Neural Networks (ANNs) and support vector machines (SVMs), are based on mathematical models with parameters that can be optimally tuned using appropriate algorithms. The ever-increasing compu-tational capacity of today’s computer systems enables more complex ML systems with millions of parameters, bringing AI closer to human intelligence. With this objective, the term deep learning (DL) has been introduced to characterize ML based on deep ANN (DNN) architectures with multiple layers of artificial neurons. Despite all of these promises, the impact of AI in current clinical practice is still limited. However, this could change shortly, as the significantly increased papers in AI, machine learning and deep learning in cardiology show. We highlight the significant achievements of recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take a central stage in the field. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Lessons learned from conducting a study of emotions and positive personality change in Syrian origin young adults who have recently resettled in the Netherlands

Post-traumatic growth is a compelling idea, yet extant research has often employed retrospective reports of change, rather than examining change over time. Research on samples of people that are traditionally seen as hard-to-reach are rare within personality psychology. In Karakter, we assessed a sample of Syrian origin young adults who recently resettled in the Netherlands (initial N = 168) four times over a 13-month period to examine experiences of adversity, emotions, and positive personality change. Here, we provide a detailed narrative of the research process, beginning with a description of how we incorporated open science practices in Karakter. We then turn to a discussion of the changes, challenges, and opportunities we encountered in the research. In doing so, we discuss conceptual and methodological considerations when examining personality change. We close with suggestions for researchers who are interested in conducting similar studies with populations that are underrecruited in the futur

Fetal MRI of the heart and brain in congenital heart disease

Antenatal assessment of congenital heart disease and associated anomalies by ultrasound has improved perinatal care. Fetal cardiovascular MRI and fetal brain MRI are rapidly evolving for fetal diagnostic testing of congenital heart disease. We give an overview on the use of fetal cardiovascular MRI and fetal brain MRI in congenital heart disease, focusing on the current applications and diagnostic yield of structural and functional imaging during pregnancy. Fetal cardiovascular MRI in congenital heart disease is a promising supplementary imaging method to echocardiography for the diagnosis of antenatal congenital heart disease in weeks 30–40 of pregnancy. Concomitant fetal brain MRI is superior to brain ultrasound to show the complex relationship between fetal haemodynamics in congenital heart disease and brain development.</p