Communication skills of medical students: Evaluation of a new communication curriculum at the University of Augsburg.

Objectives: Teaching communication skills plays a pivotal role in medical curricula. The aim of this article is to describe and evaluate a new communication curriculum developed at the Faculty of Medicine, University of Augsburg (KomCuA), which was conceptualized by an interdisciplinary team based on recommended quality standards (i.e., helical, integrated, longitudinal). Methods: A total of 150 medical students enrolled in the 1st, 3rd, and ≥5th semester participated in the study. They completed an online survey (numerical rating scales and validated questionnaires) evaluating their current communication skills, how these developed across the curriculum in terms of quality and self-confidence, and how helpful they considered practicing in small group tutorials with simulated patients. The students' attitudes towards communication and empathy in the context of medical care were additionally assessed. The students' responses were compared across semesters using one-way univariate analysis of variance (ANOVA). Results: Overall, students reported improved communications skills due to attending the KomCuA and further considered practicing with simulated patients as being very helpful (large effect sizes). Compared to 1st semester students, 3rd and ≥5th semester students reported better communication skills (medium to large effect sizes). Additionally, ≥5th semester students showed stronger agreement towards the relevance of empathy in the context of medical care (medium effect size) compared to both 1st and 3rd semester students. Conclusion: The KomCuA has shown to be an effective communication curriculum to support medical students in the development of their communication skills and positive attitudes towards empathy. Additional studies assessing students' communication skills and empathic attitudes longitudinally are warranted to confirm the present results and to gain further knowledge on how these essential skills and attitudes develop across medical curricula.

Depression and vaccination behavior in patients with chronic physical illness - A cross-sectional survey.

OBJECTIVE: Chronically ill are vulnerable to vaccine preventable infections. Consequently, their vaccination behavior is highly relevant. Depressive comorbidities are frequent in these patients. Furthermore, these patients are mainly diagnosed, treated and vaccinated in primary care. Therefore, we aimed to investigate the associations between depression and vaccination behavior (COVID-19 and influenza) in adult chronically ill primary care patients. METHODS: In a cross-sectional survey, we examined depression (PHQ9), psychological antecedents of vaccinations (Confidence and Constraints), health care utilization, and vaccination status. Based on an effect model, descriptive statistics and mixed linear/logistic models were calculated. (German Clinical Trials Register, DRKS00030042). RESULTS: n = 795 patients were analyzed. Both psychological antecedents of vaccinations (Confidence and Constraints) mediated a negative association between depression and vaccination behavior, healthcare utilization mediated a positive association. The total effect of depression was negligible. CONCLUSIONS: As the effects of vaccination readiness and healthcare utilization are opposing, different total effects depending on the study population are possible. Further studies are needed to investigate additional predictors of vaccination behavior. PRACTICE IMPLICATIONS: We suggest tackling vaccine acceptance in chronically ill through increasing confidence using communication-based interventions, for which primary care is the suitable setting. Constraints might be reduced by reminder and recall systems.

Expectations of general practitioners on a practice based research network in Germany- a qualitative study within the Bavarian Research Practice Network (BayFoNet).

BACKGROUND: Despite general practitioners' (GPs') key role in Germany`s primary health care, clinical research in general practice is scarce. Clinical research is mainly conducted at inpatient facilities, although their results are rarely transferable. German GPs have no extra time or funding for research, as well as limited research training. To support clinical research in German primary health care, practice-based research networks (PBRNs) are developed. As they will be based on an active involvement of GPs, we need more information on GPs` participation-readiness. The aim of this study was to explore facilitators and barriers to participation in the Bavarian Research Practice Network (BayFoNet) from the GPs`perspective before clinical trials will be performed. METHODS: We have performed semi-structured qualitative interviews with a purposive sample of 20 Bavarian GPs in 2022 under the application of the consolidated framework for implementation research (CFIR). Transcriptions were analysed according to Kuckartz` qualitative content analysis. The five domains of the CFIR framework served as initial deductive codes. RESULTS: N = 14 interviewees already agreed to participate in BayFoNet, whereas n = 6 interviewees opted not to participate in BayFoNet at the time of data collection. Main facilitators to conduct clinical research within BayFoNet were the motivation to contribute to evidence strength and quality in general practice, professional development and training of practice staff, as well as networking. Barriers for an active participation were bad experiences with previous clinical studies and lack of resources. CONCLUSIONS: PBRNS in Germany have to be promoted and the entire practice team has to be involved at an early stage of development. Professional training of general practice staff and a living network might enhance engagement. Participatory approaches could help to develop acceptable and feasible study designs. Furthermore, PBRNs should support patient recruitment and data collection in general practices and disseminate the results of their research projects regularly to maintain GPs` engagement. TRIAL REGISTRATION: DRKS00028805, NCT05667207.

The use of foundational ontologies in biomedical research.

BACKGROUND: The FAIR principles recommend the use of controlled vocabularies, such as ontologies, to define data and metadata concepts. Ontologies are currently modelled following different approaches, sometimes describing conflicting definitions of the same concepts, which can affect interoperability. To cope with that, prior literature suggests organising ontologies in levels, where domain specific (low-level) ontologies are grounded in domain independent high-level ontologies (i.e., foundational ontologies). In this level-based organisation, foundational ontologies work as translators of intended meaning, thus improving interoperability. Despite their considerable acceptance in biomedical research, there are very few studies testing foundational ontologies. This paper describes a systematic literature mapping that was conducted to understand how foundational ontologies are used in biomedical research and to find empirical evidence supporting their claimed (dis)advantages. RESULTS: From a set of 79 selected papers, we identified that foundational ontologies are used for several purposes: ontology construction, repair, mapping, and ontology-based data analysis. Foundational ontologies are claimed to improve interoperability, enhance reasoning, speed up ontology development and facilitate maintainability. The complexity of using foundational ontologies is the most commonly cited downside. Despite being used for several purposes, there were hardly any experiments (1 paper) testing the claims for or against the use of foundational ontologies. In the subset of 49 papers that describe the development of an ontology, it was observed a low adherence to ontology construction (16 papers) and ontology evaluation formal methods (4 papers). CONCLUSION: Our findings have two main implications. First, the lack of empirical evidence about the use of foundational ontologies indicates a need for evaluating the use of such artefacts in biomedical research. Second, the low adherence to formal methods illustrates how the field could benefit from a more systematic approach when dealing with the development and evaluation of ontologies. The understanding of how foundational ontologies are used in the biomedical field can drive future research towards the improvement of ontologies and, consequently, data FAIRness. The adoption of formal methods can impact the quality and sustainability of ontologies, and reusing these methods from other fields is encouraged.

Psychological Determinants of Vaccination Readiness against COVID-19 and Seasonal Influenza of the Chronically Ill in Primary Care in Germany-A Cross-Sectional Survey.

Vaccines against COVID-19 and influenza are highly recommended for the chronically ill. They often suffer from co-morbid mental health issues. This cross-sectional observational study analyzes the associations between depression (PHQ-9) and anxiety (OASIS) with vaccination readiness (5C) against COVID-19 and influenza in chronically ill adults in primary care in Germany. Sociodemographic data, social activity (LSNS), patient activation measure (PAM), and the doctor/patient relationship (PRA) are examined as well. Descriptive statistics and linear mixed-effects regression models are calculated. We compare data from n = 795 study participants. The symptoms of depression are negatively associated with confidence in COVID-19 vaccines (p = 0.010) and positively associated with constraints to get vaccinated against COVID-19 (p = 0.041). There are no significant associations between symptoms of depression and vaccination readiness against influenza. Self-reported symptoms of a generalized anxiety disorder seem not to be associated with vaccination readiness. To address confidence in COVID-19 vaccines among the chronically ill, targeted educational interventions should be elaborated to consider mental health issues like depression. As general practitioners play a key role in the development of a good doctor/patient relationship, they should be trained in patient-centered communication. Furthermore, a standardized implementation of digital vaccination management systems might improve immunization rates in primary care.

"The measures taken by the government overburdened the daily practice" - insights of the PRICOV-19 study on German general practitioners in times of COVID-19.

BACKGROUND: The international study PRICOV-19 aims to assess the impact of the COVID-19 pandemic on the organisation of primary health care. The German part focuses on German general practitioners during the second wave of the COVID-19 pandemic. This paper addresses the following research questions: (1) How were changes in tasks on primary care and patient treatment perceived by GPs?, (2) What was the role of GPs during the pandemic, and how was their wellbeing?, (3) How did GPs perceive health policy measures?, and, (4) What influenced the attitudes of GPs on health policy measures? METHODS: This study pursues a multi-country cross-sectional design. Data collection took place throughout Germany from 01.02. to 28.02.2021 with a quantitative online questionnaire consisting of 53 items. The questionnaire was analysed through descriptive and inferential analyses using correlation and multiple regression models. RESULTS: The response rate was 20.4% (n = 349). The respondents were mainly GPs (59.6%) in single practices (62.5%) with a mean work experience of 15 to 20 years. GPs experienced a change in their work and practice organisation (80.3%). They felt a high responsibility (70.6%) and found their work has become more meaningful to them (76%). They also saw a lack of political support (75.2%) and that the measures taken by the government overburdened the daily practice (66.4%). Not many GPs were at risk of being distressed (53.4%) but rated the health policies rather negatively (60%). The multiple regression showed, the more GPs were exposed to risk of distress, the worse they assessed the government's measures. CONCLUSION: GPs perceived their work as relevant and felt confident they could fulfil their tasks, but noticed that health policy initially hardly supported the outpatient sector. Health policies should increase their competence in relation to primary care, ensure its needs and consider an active inclusion of GPs in preparedness plans.

Process evaluation in practice based research networks: a study protocol for a mixed-methods implementation study.

INTRODUCTION: General practitioners often criticise clinical trials for their poor applicability in primary care, which may at least partially explain why their engagement in primary care research remains limited. In order to enhance primary care research, the German government has funded six regional practice based research networks (PBRNs). Within the Bavarian PBRN (BayFoNet), two cluster-randomised pilot trials will be conducted. This paper presents the protocol of the process evaluation accompanying both trials, which aims to explore relevance, feasibility, acceptability and credibility of clinical research in primary care from the perspectives of BayFoNet researchers, general practitioners, and patients. METHODS AND ANALYSIS: The BayFoNet will be established by recruiting general practices (GPs) as prospective research collaborators in two cluster randomised pilot trials. Research teams will provide training in good clinical practice, and support practices in patient recruitment, data collection and documentation. Our process evaluation explores barriers and facilitators in the set up of the BayFoNet PBRN and both cluster randomised pilot trials, under the application of the consolidated framework for implementation research and the theoretical domains framework. In a mixed-methods concept, we will use qualitative and quantitative approaches to evaluate both pilot cluster-randomised trials as well as the BayFoNet itself: focus groups with researchers, semi-structured interviews with general practitioners and questionnaires for patients participating in the pilot cluster-randomised trials at three different time points. ETHICS AND DISSEMINATION: Research ethical approval for this study was granted by the Ethics Committee of the Medical Department, Ludwig-Maximilians-University Munich (AZ 21-1135). Results will be published in international peer-reviewed journals and summaries will be provided to the funders of the study as well as other PBRNs, GP teams and patients. TRIAL REGISTRATION NUMBERS: DRKS00028805, NCT05667207.

Machine learning in Huntington's disease: exploring the Enroll-HD dataset for prognosis and driving capability prediction.

BACKGROUND: In biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of different diseases, including cancer and neurodegenerative disorders. For rare diseases, however, the requirement for large datasets often prevents this approach. Huntington's disease (HD) is a rare neurodegenerative disorder caused by a CAG repeat expansion in the coding region of the huntingtin gene. The world's largest observational study for HD, Enroll-HD, describes over 21,000 participants. As such, Enroll-HD is amenable to ML methods. In this study, we pre-processed and imputed Enroll-HD with ML methods to maximise the inclusion of participants and variables. With this dataset we developed models to improve the prediction of the age at onset (AAO) and compared it to the well-established Langbehn formula. In addition, we used recurrent neural networks (RNNs) to demonstrate the utility of ML methods for longitudinal datasets, assessing driving capabilities by learning from previous participant assessments. RESULTS: Simple pre-processing imputed around 42% of missing values in Enroll-HD. Also, 167 variables were retained as a result of imputing with ML. We found that multiple ML models were able to outperform the Langbehn formula. The best ML model (light gradient boosting machine) improved the prognosis of AAO compared to the Langbehn formula by 9.2%, based on root mean squared error in the test set. In addition, our ML model provides more accurate prognosis for a wider CAG repeat range compared to the Langbehn formula. Driving capability was predicted with an accuracy of 85.2%. The resulting pre-processing workflow and code to train the ML models are available to be used for related HD predictions at: https://github.com/JasperO98/hdml/tree/main . CONCLUSIONS: Our pre-processing workflow made it possible to resolve the missing values and include most participants and variables in Enroll-HD. We show the added value of a ML approach, which improved AAO predictions and allowed for the development of an advisory model that can assist clinicians and participants in estimating future driving capability.

[Clinical quality governance-a practical approach in general practice with the example of influenza vaccination]. / Klinische Qualitätssteuerung ­ ein praktischer Versuch in der Hausarztpraxis am Beispiel der Influenzaimpfung.

We understand clinical quality governance (CQG) as quality management in the clinical domain. In 2020, presumably due to the coronavirus pandemic, more patients requested to be vaccinated against influenza as compared to previous years so that it became apparent that there would be a shortage for high-risk patients. To meet the problem, we started a CQG process. This article is explicitly not a research article but an exemplary description of a CQG process intended as a stimulus and for discussion. We initiated the following process: (1) evaluation of the present state, (2) patients who already had requested a vaccination were prioritized and vaccinated first, and (3) contacting via telephone and vaccination of high-risk patients not on the list. We chose patients with chronic obstructive pulmonary disease (COPD) older than 60 years as an indicator for the group of highest priority. In the beginning only 3 (8%) of our 38 patients with COPD were vaccinated against influenza. After prioritization and vaccination of the high-risk collective in the list of those who had requested to be vaccinated, 25 (66%) of our 38 patients with COPD were vaccinated. After a phone call of high-risk patients not on the list, 28 (74%) patients were vaccinated. This represents an increase of vaccination coverage from 8% to 74% which is close to the rate recommended by the World Health Organization (WHO). In times of a pandemic, family physicians occasionally have to deal with a scarcity of resources and have to develop strategies for fair resource allocation. Not only in this context is CQG worth the effort. The generation of list queries could be improved by the providers of electronic patient records.

Personal Genomes in Practice: Exploring Citizen and Healthcare Professionals' Perspectives on Personalized Genomic Medicine and Personal Health Data Spaces Using a Mixed-Methods Design.

Ongoing health challenges, such as the increased global burden of chronic disease, are increasingly answered by calls for personalized approaches to healthcare. Genomic medicine, a vital component of these personalization strategies, is applied in risk assessment, prevention, prognostication, and therapeutic targeting. However, several practical, ethical, and technological challenges remain. Across Europe, Personal Health Data Space (PHDS) projects are under development aiming to establish patient-centered, interoperable data ecosystems balancing data access, control, and use for individual citizens to complement the research and commercial focus of the European Health Data Space provisions. The current study explores healthcare users' and health care professionals' perspectives on personalized genomic medicine and PHDS solutions, in casu the Personal Genetic Locker (PGL). A mixed-methods design was used, including surveys, interviews, and focus groups. Several meta-themes were generated from the data: (i) participants were interested in genomic information; (ii) participants valued data control, robust infrastructure, and sharing data with non-commercial stakeholders; (iii) autonomy was a central concern for all participants; (iv) institutional and interpersonal trust were highly significant for genomic medicine; and (v) participants encouraged the implementation of PHDSs since PHDSs were thought to promote the use of genomic data and enhance patients' control over their data. To conclude, we formulated several facilitators to implement genomic medicine in healthcare based on the perspectives of a diverse set of stakeholders.

"What else to say?"-Primary health care in times of COVID-19 from the perspective of German general practitioners: An exploratory analysis of the open text field in the PRICOV-19 study.

BACKGROUND: The international collaboration study PRICOV-19 -Primary Health Care in times of COVID-19 aims to assess the impact of the COVID-19 pandemic on the organisation of primary health care. The German part focuses on the subjective perceptions of general practitioners on primary health care and the impact of political measures during the second wave of the COVID-19 pandemic. Within this survey, the "open text field" of the questionnaire was utilised remarkably frequently and extensively by the respondents. It became clear that the content that was named needed to be analysed in an exploratory manner. Accordingly, this paper addresses the following question: What preoccupies general practitioners in Germany during COVID-19 that we have not yet asked them enough? METHODS: The data collection took place throughout Germany from 01.02.2021 to 28.02.2021with a quantitative online questionnaire consisting of 53 items arranged across six topics as well as an "open text field" for further comments. The questionnaire's open text field was analysed following the premises of the qualitative content analysis. RESULTS: The topics discussed by the respondents were: insufficient support from health policies, not being prioritised and involved in the vaccination strategy, feeling insufficient prepared, that infrastructural changes and financial concerns threatened the practice, and perceiving the own role as important, as well as that health policies affected the wellbeing of the respondents. One of the main points was the way general practitioners were not sufficiently acknowledged for their contribution to ensuring high-quality care during the pandemic. DISCUSSION: German general practitioners perceived their work and role as highly relevant during the COVID-19 pandemic. In controversy with their perception, they described political conditions in which they were the ones who contributed significantly to the fight against the pandemic but were not given enough recognition.

Towards FAIRification of sensitive and fragmented rare disease patient data: challenges and solutions in European reference network registries.

INTRODUCTION: Rare disease patient data are typically sensitive, present in multiple registries controlled by different custodians, and non-interoperable. Making these data Findable, Accessible, Interoperable, and Reusable (FAIR) for humans and machines at source enables federated discovery and analysis across data custodians. This facilitates accurate diagnosis, optimal clinical management, and personalised treatments. In Europe, twenty-four European Reference Networks (ERNs) work on rare disease registries in different clinical domains. The process and the implementation choices for making data FAIR ('FAIRification') differ among ERN registries. For example, registries use different software systems and are subject to different legal regulations. To support the ERNs in making informed decisions and to harmonise FAIRification, the FAIRification steward team was established to work as liaisons between ERNs and researchers from the European Joint Programme on Rare Diseases. RESULTS: The FAIRification steward team inventoried the FAIRification challenges of the ERN registries and proposed solutions collectively with involved stakeholders to address them. Ninety-eight FAIRification challenges from 24 ERNs' registries were collected and categorised into "training" (31), "community" (9), "modelling" (12), "implementation" (26), and "legal" (20). After curating and aggregating highly similar challenges, 41 unique FAIRification challenges remained. The two categories with the most challenges were "training" (15) and "implementation" (9), followed by "community" (7), and then "modelling" (5) and "legal" (5). To address all challenges, eleven types of solutions were proposed. Among them, the provision of guidelines and the organisation of training activities resolved the "training" challenges, which ranged from less-technical "coffee-rounds" to technical workshops, from informal FAIR Games to formal hackathons. Obtaining implementation support from technical experts was the solution type for tackling the "implementation" challenges. CONCLUSION: This work shows that a dedicated team of FAIR data stewards is an asset for harmonising the various processes of making data FAIR in a large organisation with multiple stakeholders. Additionally, multi-levelled training activities are required to accommodate the diverse needs of the ERNs. Finally, the lessons learned from the experience of the FAIRification steward team described in this paper may help to increase FAIR awareness and provide insights into FAIRification challenges and solutions of rare disease registries.

One year of general practice during the COVID-19 pandemic - presentation and evaluation of digital medical education.

Background and teaching situation: The SARS-CoV-2 pandemic had a substantial didactic impact on medical teaching. In Erlangen, the lecture "General Practice" was offered asynchronously and digitally in an inverted-classroom concept. Contents were available via a learning platform. The lecture was presented using annotated videos, consolidation materials and control questions. A forum encouraged for discussions and feedback and collected in-depth aspects for a case-based video consultation. The aim of this work is to evaluate and critically examine the digital teaching concept during the SARS-CoV-2 pandemic. Methodology: Two semester cohorts evaluated the lecture. Overall impression of the lecture, didactic elements, suitability and the desired future lecture format were surveyed quantitatively. Free text answers were evaluated by means of qualitative content synthesis. Results: In terms of overall impression, the students (N=199) rated the lecture on average as "very good" (M=1.41, SD=.57). Digital methods were perceived as suitable for supporting self-study, and digital usage was rated as unproblematically (M=1.18, SD=.50). Desired future teaching formats were blended learning concepts (79.4%). Organisation, structure and content presentation were highly appreciated. The time for completing the course was perceived critically. The students urged for more practical and consolidating lecture work. Discussion and implications: The results illustrate high acceptance of digital teaching and underline the demand for future blended learning concepts. It is particularly important to better consider the students' time investment and practical relevance of digital self-learning mechanisms.

FAIR Digital Twins for Data-Intensive Research.

Although all the technical components supporting fully orchestrated Digital Twins (DT) currently exist, what remains missing is a conceptual clarification and analysis of a more generalized concept of a DT that is made FAIR, that is, universally machine actionable. This methodological overview is a first step toward this clarification. We present a review of previously developed semantic artifacts and how they may be used to compose a higher-order data model referred to here as a FAIR Digital Twin (FDT). We propose an architectural design to compose, store and reuse FDTs supporting data intensive research, with emphasis on privacy by design and their use in GDPR compliant open science.

The "Seminartage Weiterbildung Allgemeinmedizin" (SemiWAM®) - development, implementation and evaluation of a five-year, competence-based postgraduate programme in Bavaria.

Introduction: Starting in 2013, a five-year, competence-based postgraduate programme, the "Seminartage Weiterbildung Allgemeinmedizin" (SemiWAM®) for continuing education in general practice, was developed and offered in Bavaria. This evaluation reports on the experiences of SemiWAM® after a first cycle. Material and methods: Process reflection based on the cycle of Kern: In addition to qualitative findings, results of the evaluation forms (mean values with standard deviation) are presented. The evaluation form contained questions on organisational issues, content of presentation, didactic preparation of the supervisor, transfer to real life practice as well as demographic variables. All questions were voted on a six-point Likert scale from "1=very satisfied" to "6=very dissatisfied". Results: The reflection showed three crucial entry points: Choosing "reason for encounter" as a content precondition to ensure target audience needs, the close didactic supervision of supervisor, and the continuous growth of supervisor team with newly qualified GP. The evaluation results for the overall assessment (MW 1.11-1.60), the didactic concept (MW 1.30-1.87), as well as the transfer into daily life practice (MW 1.48-2.35) reflect the high quality of the SemiWAM®. Discussion: The SemiWAM® curriculum presented can be easily transferred to comparable structures in Germany that accompany specialty training, such as the competence centres for residency training in general practice. The process evaluation based on the core cycle also provides important support for the agile implementation of these or similar programmes.

Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic.

BACKGROUND: The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data 'silos' that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR. RESULTS: In this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors' research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital. CONCLUSIONS: Our work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR Digital Objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.

Semantic modelling of common data elements for rare disease registries, and a prototype workflow for their deployment over registry data.

BACKGROUND: The European Platform on Rare Disease Registration (EU RD Platform) aims to address the fragmentation of European rare disease (RD) patient data, scattered among hundreds of independent and non-coordinating registries, by establishing standards for integration and interoperability. The first practical output of this effort was a set of 16 Common Data Elements (CDEs) that should be implemented by all RD registries. Interoperability, however, requires decisions beyond data elements - including data models, formats, and semantics. Within the European Joint Programme on Rare Diseases (EJP RD), we aim to further the goals of the EU RD Platform by generating reusable RD semantic model templates that follow the FAIR Data Principles. RESULTS: Through a team-based iterative approach, we created semantically grounded models to represent each of the CDEs, using the SemanticScience Integrated Ontology as the core framework for representing the entities and their relationships. Within that framework, we mapped the concepts represented in the CDEs, and their possible values, into domain ontologies such as the Orphanet Rare Disease Ontology, Human Phenotype Ontology and National Cancer Institute Thesaurus. Finally, we created an exemplar, reusable ETL pipeline that we will be deploying over these non-coordinating data repositories to assist them in creating model-compliant FAIR data without requiring site-specific coding nor expertise in Linked Data or FAIR. CONCLUSIONS: Within the EJP RD project, we determined that creating reusable, expert-designed templates reduced or eliminated the requirement for our participating biomedical domain experts and rare disease data hosts to understand OWL semantics. This enabled them to publish highly expressive FAIR data using tools and approaches that were already familiar to them.

[The importance of the learning environment and reduction of burnout in clinical training: an analysis of the Competence Centre for Residency Training in Family Medicine Bavaria (CCRTB)]. / Bedeutung der Lernumgebung und Reduzierung von Burnout in der stationären Weiterbildung ­ Analyse des Kompetenzzentrums Weiterbildung Allgemeinmedizin Bayern (KWAB).

BACKGROUND: The Competence Centre for Residency Training in Family Medicine Bavaria (CCRTB) was established to improve the quality of postgraduate medical training by offering additional seminars and mentoring programmes as well as regular 'train-the-trainer' courses for educating physicians. In addition, residents have the opportunity to participate in a regional training network. OBJECTIVE: The aim was to assess the burden of burnout and the importance of the learning environment in the clinical training phase. METHODS: We conducted a cross-sectional study. Burnout was assessed using the Maslach Burnout Inventory (MBI), which comprises the scales "Emotional Exhaustion", "Depersonalisation" and "Personal Accomplishment". The quality of the learning environment was recorded using the German version of the Dutch Residency Educational Climate Test (D-RECT German). In addition, multivariable linear regressions were performed to estimate the impact of learning environment, year of training and participation in a regional network on the level of burnout. RESULTS: 129 clinical residents enrolled in the CCRTB were invited to participate in the study, 78 (61%) of whom submitted a response. 76 (59%) of these residents were included in the analyses. The present study discloses an increased burden of burnout among residents in the clinical training phase, with approx. 40% reaching a critical burnout score. A higher quality of the learning environment was associated with significantly milder burnout symptoms on the majority of the D-RECT scales. CONCLUSION: Family medicine residents in the clinical training phase show a high burden of burnout. Therefore, increasing the quality of the learning environment appears to be an effective key element in achieving a reduction of burnout in clinical training. This might contribute to an increase in professional satisfaction, which finally may also prevent migration from the medical profession.

Huntington Disease Gene Expression Signatures in Blood Compared to Brain of YAC128 Mice as Candidates for Monitoring of Pathology.

While the genetic cause of Huntington disease (HD) is known since 1993, still no cure exists. Therapeutic development would benefit from a method to monitor disease progression and treatment efficacy, ideally using blood biomarkers. Previously, HD-specific signatures were identified in human blood representing signatures in human brain, showing biomarker potential. Since drug candidates are generally first screened in rodent models, we aimed to identify HD signatures in blood and brain of YAC128 HD mice and compare these with previously identified human signatures. RNA sequencing was performed on blood withdrawn at two time points and four brain regions from YAC128 and control mice. Weighted gene co-expression network analysis was used to identify clusters of co-expressed genes (modules) associated with the HD genotype. These HD-associated modules were annotated via text-mining to determine the biological processes they represented. Subsequently, the processes from mouse blood were compared with mouse brain, showing substantial overlap, including protein modification, cell cycle, RNA splicing, nuclear transport, and vesicle-mediated transport. Moreover, the disease-associated processes shared between mouse blood and brain were highly comparable to those previously identified in human blood and brain. In addition, we identified HD blood-specific pathology, confirming previous findings for peripheral pathology in blood. Finally, we identified hub genes for HD-associated blood modules and proposed a strategy for gene selection for development of a disease progression monitoring panel.