The multimodal Munich Clinical Deep Phenotyping study to bridge the translational gap in severe mental illness treatment research

Introduction: Treatment of severe mental illness (SMI) symptoms, especially negative symptoms and cognitive dysfunction in schizophrenia, remains a major unmet need. There is good evidence that SMIs have a strong genetic background and are characterized by multiple biological alterations, including disturbed brain circuits and connectivity, dysregulated neuronal excitation-inhibition, disturbed dopaminergic and glutamatergic pathways, and partially dysregulated inflammatory processes. The ways in which the dysregulated signaling pathways are interconnected remains largely unknown, in part because well-characterized clinical studies on comprehensive biomaterial are lacking. Furthermore, the development of drugs to treat SMIs such as schizophrenia is limited by the use of operationalized symptom-based clusters for diagnosis. Methods: In line with the Research Domain Criteria initiative, the Clinical Deep Phenotyping (CDP) study is using a multimodal approach to reveal the neurobiological underpinnings of clinically relevant schizophrenia subgroups by performing broad transdiagnostic clinical characterization with standardized neurocognitive assessments, multimodal neuroimaging, electrophysiological assessments, retinal investigations, and omics-based analyzes of blood and cerebrospinal fluid. Moreover, to bridge the translational gap in biological psychiatry the study includes in vitro investigations on human-induced pluripotent stem cells, which are available from a subset of participants. Results: Here, we report on the feasibility of this multimodal approach, which has been successfully initiated in the first participants in the CDP cohort; to date, the cohort comprises over 194 individuals with SMI and 187 age and gender matched healthy controls. In addition, we describe the applied research modalities and study objectives. Discussion: The identification of cross-diagnostic and diagnosis-specific biotype-informed subgroups of patients and the translational dissection of those subgroups may help to pave the way toward precision medicine with artificial intelligence-supported tailored interventions and treatment. This aim is particularly important in psychiatry, a field where innovation is urgently needed because specific symptom domains, such as negative symptoms and cognitive dysfunction, and treatment-resistant symptoms in general are still difficult to treat

Combining Design Patterns and Elements of Social Computing for the Design of User Centered Online Help Systems

Many current (online) help systems fail because users refuse to use them or, even if they do so, they do not perceive them as helpful. There is an obvious gap between the intentions of the help content authors and the achievement of objectives concerning the perceived usefulness by help users. Problems may be divided into psychological and implementation issues. On the psychological side users are often seriously challenged with understanding the instructions given by the system, which usually is not adequately adapted to user's prior knowledge or the vocabulary of a lay person. This problem of expert-lay communication is strengthened by the implementation problem of missing feedback channels. As a result, help systems do often leave users in isolation with their problems. The current article aims to address these issues by presenting an information architecture for an online help system which addresses aspects of communication between authors and users. The approach combines earlier models of design patterns with features for user contribution from social software and design principles in multimedia learning

"Das ist ja wie bei den Heinzelmännchen!" - Unterstützung der Digitalisierung der medizinischen Lehre durch ein interdisziplinäres E-Tutor*innen-Team

Background: The forced and time-critical changeover to digital teaching and learning formats in the summer semester 2020 brought about numerous new challenges for the teaching staff of the Faculty of Medicine at the University of Regensburg. Didactic and personnel support of clinical lecturers for the preparation, creation, and supervision of digital teaching materials became necessary.Project description: Since interdisciplinary teams seem to be superior in finding creative solutions, an interdisciplinary student e-tutor team was established at the Faculty of Medicine to support the digitalization of the range of courses. After their initial basic training the e-tutors had regular team meetings and internal mini-training sessions to ensure their continuous professional development. The e-tutors could be "requested" by clinical teaching staff and then accompanied the respective course preparation and implementation as required. Results and discussion: Both clinical teachers and students perceived the student e-tutors' support to be very positive. The e-tutors described the interdisciplinarity of the team as an important learning resource and their work as an exciting and instructive task. Conclusion and outlook: Due to the positive experiences with the e-tutors, the faculty is striving to establish sustainable digital teaching and learning services in the coming semesters.Hintergrund: Die erzwungene und zeitkritische Umstellung auf digitale Lehr-Lernformate im Sommersemester 2020 brachte für die Lehrenden der Fakultät für Medizin an der Universität Regensburg zahlreiche neue Herausforderungen mit sich. Didaktische und personelle Unterstützung der Lehrenden zur Vorbereitung, Erstellung und Betreuung digitaler Angebote wurde notwendig.Projektbeschreibung: Da interdisziplinäre Teams in der kreativen Lösungsfindung überlegen scheinen, wurde ein interdisziplinäres studentisches E-Tutor*innen-Team an der Fakultät für Medizin etabliert, um die Digitalisierung des Lehrangebots zu unterstützen. Nach einem initialen Basistraining der E-Tutor*innen erfolgten zur kontinuierlichen Weiterentwicklung regelmäßige Teamtreffen und interne Mini-Schulungen. Die E-Tutor*innen konnten von den Lehrenden "angefordert" werden und begleiteten dann bedarfsorientiert die jeweiligen Kursvorbereitungen und -durchführungen. Ergebnisse und Diskussion: Die Unterstützung durch die studentischen E-Tutor*innen wurde von Lehrenden und Studierenden sehr positiv wahrgenommen. Die E-Tutor*innen beschreiben die Interdisziplinarität des Teams als wichtige Lernressource und ihre Tätigkeit als spannende und lehrreiche Aufgabe. Fazit und Ausblick: Aufgrund der positiven Erfahrungen mit den E-Tutor*innen wird zur nachhaltigen Verankerung digitaler Lehr-Lernangebote an der Fakultät für die kommenden Semester eine Verstetigung dieser Unterstützungsleistung angestrebt