Drohen und Versprechen

Vorliegende Dissertation befasst sich mit der Behandlung von Drohungen und Versprechen im geltenden Strafrecht. Die Ausgangsthese der Untersuchung lautet, dass eine bedingte Ankündigung nur als Drohung oder als Versprechen strafrechtlich relevant sein kann. In der Konsequenz kann eine bedingte Ankündigung somit bei Drohung mit einem empfindlichen Übel nur als Nötigung oder bei Versprechen eines Vorteils nur als Vorteilsgewährung bzw. Bestechlichkeit strafbar sein. Der Schwerpunkt der Arbeit liegt auf der Untersuchung der Strafbarkeit der Nötigung durch Drohung mit einem Unterlassen. Dabei werden zunächst die bereits vertretenen Meinungen dargestellt und bewertet, bevor – ausgehend von den gewonnenen Ergebnissen – ein eigener Lösungsansatz entwickelt wird. Dabei wird ein Hauptaugenmerk auf die trennscharfe Abgrenzung von Drohungen und Versprechen gelegt

Are students in teacher training interested in educational-scientific contents? A longitudinal study covering the first four semesters

Lehramtsstudierende interessieren sich zu Beginn ihres Studiums häufig stark für pädagogische Inhalte. Ob dieses hohe Interesse allerdings im Studienverlauf stabil ist, wurde bisher unzureichend empirisch untersucht. In der vorliegenden Studie wurde an N = 1169 Lehramtsstudierenden überprüft, wie sich das Interesse an bildungswissenschaftlichen Inhalten über vier Semester entwickelt. Zusätzlich wurden Eingangsmerkmale der Studierenden als Prädiktoren zur Erklärung interindividueller Unterschiede einbezogen. Ein zentrales Ergebnis latenter Wachstumskurvenmodelle ist, dass das Interesse an den Bildungswissenschaften zwar im Mittel über die Zeit stabil bleibt, sich aber bedeutsame Varianz im Veränderungswert zeigt. Diese kann durch die Sicherheit der Studienwahl und das angestrebte Lehramt erklärt werden, und zwar in die Richtung, dass Studierende mit hoher Entscheidungssicherheit sowie Studierende eines gymnasialen Lehramts tendenziell an Interesse dazugewinnen. (DIPF/Orig.)At the beginning of their studies, students in teacher training are often strongly interested in pedagogical contents. However, whether this strong interest remains stable throughout the course of studies has as yet hardly been examined empirically. On the basis of a sample of N = 1169 students enrolled in teacher training, the present study investigates how the interest in educational-scientific contents develops over a period of four semesters. In addition, enrollment characteristics of students are included as predictors for the explanation of inter-individual differences. A key result of latent growth curve models is that, on average, the interest in educational sciences remains stable over time, however, significant variance is revealed in the change value. This can be explained by the certainty of the choice of studies and the type of teaching post aimed at, namely, it can be shown that students who are to a high degree sure that they made the right choice and students choosing an academic track program tend to build up interest. (DIPF/Orig.

An overview and a roadmap for artificial intelligence in hematology and oncology

BACKGROUND Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals. METHODS In this article, we provide an expert-based consensus statement by the joint Working Group on "Artificial Intelligence in Hematology and Oncology" by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology. RESULTS First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology. CONCLUSION Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future

An overview and a roadmap for artificial intelligence in hematology and oncology.

Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals

High‐resolution MRI of mummified tissues using advanced short‐T2 methodology and hardware

Purpose: Evolutionary medicine aims to study disease development from a long-term perspective, and through the analysis of mummified tissue, timescales of several thousand years are unlocked. Due to the status of mummies as ancient relics, noninvasive techniques are preferable, and, currently, CT imaging is the most widespread method. However, CT images lack soft-tissue contrast, making complementary MRI data desirable. Unfortunately, the dehydrated nature and short T2 times of mummified tissues render them practically invisible to standard MRI techniques. Specialized short-T2 approaches have therefore been used, but currently suffer severe resolution limitations. The purpose of the present study is to improve resolution in MRI of mummified tissues. Methods: The zero-TE-based hybrid filling technique, together with a high-performance magnetic field gradient, was used to image three ancient Egyptian mummified human body parts: a hand, a foot, and a head. A similar pairing has already been shown to increase resolution and image quality in MRI of short-T2 tissues. Results: MRI images of yet unparalleled image quality were obtained for all samples, reaching isotropic resolutions of 0.6 mm and SNR values above 100. The same general features as present in CT images were depicted but with different contrast, particularly for regions containing embalming substances. Conclusion: Mummy MRI is a potentially valuable tool for (paleo)pathological studies, as well as for investigations into ancient mummification processes. The results presented here show sufficient improvement in the depiction of mummified tissues to clear new paths for the exploration of this field. Keywords: HYFI; ZTE; ancient Egyptian mummy; high resolution; high-performance gradient; short T2

Long-Term Results of Allogeneic Stem Cell Transplantation in Adult Ph- Negative High-Risk Acute Lymphoblastic Leukemia

Allogeneic hematopoietic stem cell transplantation (HCT) is standard treatment for adult high-risk (HR) acute lymphoblastic leukemia (ALL) and contributed to the overall improved outcome. We report a consecutive cohort of prospectively defined HR patients treated on German Multicenter Acute Lymphoblastic Leukemia trials 06/99-07/03 with similar induction/consolidation therapy and HCT in first remission. A total of 542 patients (15-55 years) with BCR-ABL-negative ALL were analyzed. Sixty-seven percent received HCT from matched unrelated donors (MUD) and 32% from matched sibling donors (MSD). The incidence of non-relapse mortality (NRM) was 20% at 5 years. NRM occurred after median 6.6 months; the leading cause (46%) was infection. NRM after MUD decreased from 39% in trial 06/99 to 16% in trial 07/03 (P < .00001). Patient age was the strongest predictor of NRM. The 5-year relapse incidence was 23% using MSD and 25% using MUD. Minimal residual disease (MRD) was the strongest predictor of relapse (45% for molecular failure versus 6% for molecular CR; P < .0001). The median follow-up was 67 months, and the 5-year survival rate was 58%. Age, subtype/high risk feature, MRD status, trial and acute GvHD were significant prognostic factors. We provide a large reference analysis with long follow-up confirming a similar outcome of MSD and MUD HCT and improved NRM for MUD HCT over years. MRD has a strong impact on relapse risk, whereas age was the strongest predictor of NRM. New adapted conditioning strategies should be considered for older patients combined with the goal to reduce the MRD level before stem cell transplantation.(c) 2022 The American Society for Transplantation and Cellular Therapy. Published by Elsevier Inc. All rights reserved