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AbstractThe EyeMatics project, embedded as a clinical use case in Germany’s Medical Informatics Initiative, is a large digital health initiative in ophthalmology. The objective is to improve the understanding of the treatment effects of intravitreal injections, the most frequent procedure to treat eye diseases. To achieve this, valuable patient data will be meaningfully integrated and visualized from different IT systems and hospital sites. EyeMatics emphasizes a governance framework that actively involves patient representatives, strictly implements interoperability standards, and employs artificial intelligence methods to extract biomarkers from tabular and clinical data as well as raw retinal scans. In this perspective paper, we delineate the strategies for user-centered implementation and health care–based evaluation in a multisite observational technology study.

IntroductionObtaining real-world data from routine clinical care is of growing interest for scientific research and personalized medicine. Despite the abundance of medical data across various facilities — including hospitals, outpatient clinics, and physician practices — the intersectoral exchange of information remains largely hindered due to differences in data structure, content, and adherence to data protection regulations. In response to this challenge, the Medical Informatics Initiative (MII) was launched in Germany, focusing initially on university hospitals to foster the exchange and utilization of real-world data through the development of standardized methods and tools, including the creation of a common core dataset. Our aim, as part of the Medical Informatics Research Hub in Saxony (MiHUBx), is to extend the MII concepts to non-university healthcare providers in a more seamless manner to enable the exchange of real-world data among intersectoral medical sites.MethodsWe investigated what services are needed to facilitate the provision of harmonized real-world data for cross-site research. On this basis, we designed a Service Platform Prototype that hosts services for data harmonization, adhering to the globally recognized Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) international standard communication format and the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Leveraging these standards, we implemented additional services facilitating data utilization, exchange and analysis. Throughout the development phase, we collaborated with an interdisciplinary team of experts from the fields of system administration, software engineering and technology acceptance to ensure that the solution is sustainable and reusable in the long term.ResultsWe have developed the pre-built packages “ResearchData-to-FHIR,” “FHIR-to-OMOP,” and “Addons,” which provide the services for data harmonization and provision of project-related real-world data in both the FHIR MII Core dataset format (CDS) and the OMOP CDM format as well as utilization and a Service Platform Prototype to streamline data management and use.ConclusionOur development shows a possible approach to extend the MII concepts to non-university healthcare providers to enable cross-site research on real-world data. Our Service Platform Prototype can thus pave the way for intersectoral data sharing, federated analysis, and provision of SMART-on-FHIR applications to support clinical decision making.

Abstract Human cytomegalovirus (HCMV) frequently causes congenital infections, resulting in birth defects and developmental disorders. A vaccine is needed, but unavailable. We analyzed the potential of CMV mutants, lacking their STAT2 antagonists to serve as live attenuated vaccine viruses in mice. Infections with attenuated viruses elicited strong ELISA-reactive binding IgG responses and induced neutralizing antibodies as well as antibodies stimulating cellular Fcγ receptors, including the antibody-dependent cellular cytotoxicity (ADCC)-eliciting receptors FcγRIII/CD16 and FcγRIV. Accordingly, vaccinated mice were fully protected against challenge infections. Female mice vaccinated prior to gestation transmitted CMV-specific IgG to their offspring, which protected the progeny from perinatal infections in a mouse model for congenital CMV disease. To define the role of maternal antibodies, female mice either capable or incapable of producing antibodies were vaccinated and subsequently bred to males of the opposite genotype. Challenge infections of the genotypically identical F1 generation revealed the indispensability of maternal antibodies for vaccine-induced protection against cytomegaloviruses.

The transfer of new insights from basic or clinical research into clinical routine is usually a lengthy and time-consuming process. Conversely, there are still many barriers to directly provide and use routine data in the context of basic and clinical research. In particular, no coherent software solution is available that allows a convenient and immediate bidirectional transfer of data between concrete treatment contexts and research settings. Here, we present a generic framework that integrates health data (e.g., clinical, molecular) and computational analytics (e.g., model predictions, statistical evaluations, visualizations) into a clinical software solution which simultaneously supports both patient-specific healthcare decisions and research efforts, while also adhering to the requirements for data protection and data quality. Specifically, our work is based on a recently established generic data management concept, for which we designed and implemented a web-based software framework that integrates data analysis, visualization as well as computer simulation and model prediction with audit trail functionality and a regulation-compliant pseudonymization service. Within the front-end application, we established two tailored views: a clinical (i.e., treatment context) perspective focusing on patient-specific data visualization, analysis and outcome prediction and a research perspective focusing on the exploration of pseudonymized data. We illustrate the application of our generic framework by two use-cases from the field of haematology/oncology. Our implementation demonstrates the feasibility of an integrated generation and backward propagation of data analysis results and model predictions at an individual patient level into clinical decision-making processes while enabling seamless integration into a clinical information system or an electronic health record.

Abstract Background Individualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities. Results In the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level. Conclusions By integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.

Human cytomegalovirus (HCMV) is endowed with multiple highly sophisticated immune evasion strategies. This includes the evasion from antibody mediated immune control by counteracting host Fc-gamma receptor (FcγR) mediated immune control mechanisms such as antibody-dependent cellular cytotoxicity (ADCC). We have previously shown that HCMV avoids FcγR activation by concomitant expression of the viral Fc-gamma-binding glycoproteins (vFcγRs) gp34 and gp68. We now show that gp34 and gp68 bind IgG simultaneously at topologically different Fcγ sites and achieve efficient antagonization of host FcγR activation by distinct but synergizing mechanisms. While gp34 enhances immune complex internalization, gp68 acts as inhibitor of host FcγR binding to immune complexes. In doing so, gp68 induces Fcγ accessibility to gp34 and simultaneously limits host FcγR recognition. The synergy of gp34 and gp68 is compelled by the interfering influence of excessive non-immune IgG ligands and highlights conformational changes within the IgG globular chains critical for antibody effector function.

Viral infections are a global disease burden with only a limited number of antiviral agents available. Due to newly emerging viral pathogens and increasing occurrence of drug resistance, there is a continuous need for additional therapeutic options, preferably with extended target range. In the present study, we describe a novel antiviral peptide with broad activity against several double-stranded DNA viruses. The 22-mer peptide TAT-I24 potently neutralized viruses such as herpes simplex viruses, adenovirus type 5, cytomegalovirus, vaccinia virus, and simian virus 40 in cell culture models, while being less active against RNA viruses. The peptide TAT-I24 therefore represents a novel and promising drug candidate for use against double-stranded DNA viruses.

Lower respiratory tract infections, such as infections caused by influenza A viruses, are a constant threat for public health. Antivirals are indispensable to control disease caused by epidemic as well as pandemic influenza A. We developed a novel anti-influenza A virus approach based on an engineered single-domain antibody (VHH) construct that can selectively recruit innate immune cells to the sites of virus replication. This protective construct comprises two VHHs. One VHH binds with nanomolar affinity to the conserved influenza A matrix protein 2 (M2) ectodomain (M2e). Co-crystal structure analysis revealed that the complementarity determining regions 2 and 3 of this VHH embrace M2e. The second selected VHH specifically binds to the mouse Fcγ Receptor IV (FcγRIV) and was genetically fused to the M2e-specific VHH, which resulted in a bi-specific VHH-based construct that could be efficiently expressed in Pichia pastoris. In the presence of M2 expressing or influenza A virus-infected target cells, this single domain antibody construct selectively activated the mouse FcγRIV. Moreover, intranasal delivery of this bispecific FcγRIV-engaging VHH construct protected wild type but not FcγRIV−/− mice against challenge with an H3N2 influenza virus. These results provide proof of concept that VHHs directed against a surface exposed viral antigen can be readily armed with effector functions that trigger protective antiviral activity beyond direct virus neutralization.

Procrastination is a form of self-regulation failure characterized by the irrational delay of tasks despite potentially negative consequences. Previous research on procrastination was mainly conducted in academic settings, oftentimes combined with a focus on individual differences. As a consequence, scholarly knowledge about how situational factors affect procrastination in work settings is still scarce. Drawing on job stress literature, we assumed that work characteristics go along with cognitive appraisals of the work situation as a challenge and/or hindrance, that these cognitive appraisals affect employees' self-regulation effort to overcome inner resistances, and that self-regulation effort should in turn be related to workplace procrastination. In our study, we focused on three specific work characteristics that we expected to trigger both challenge and hindrance appraisal simultaneously: time pressure, problem solving, and planning and decision-making. We hypothesized serial indirect effects of these work characteristics on workplace procrastination via cognitive appraisal and self-regulation processes that unfold within individuals over short periods of time. Consequently, we conducted a diary study with three measurement occasions per workday over a period of 12 days. Overall, 762 day-level datasets from 110 employees were included in Bayesian multilevel structural equation modeling (MSEM; controlled for sleep quality and occupational self-efficacy). Our results revealed negative serial indirect effects of all three work characteristics on workplace procrastination via increased challenge appraisal and subsequently reduced self-regulation effort. Further, our results showed a positive serial indirect effect of time pressure (but not of problem solving or planning and decision making) on workplace procrastination via increased hindrance appraisal and subsequently increased self-regulation effort. Overall, our study showed that work characteristics are linked to workplace procrastination via within-person processes of cognitive appraisal and self-regulation. Because not all work characteristics triggered hindrance appraisal, we argue that it may make sense to further differentiate challenge stressors in the future. Moreover, cognitive appraisals affected self-regulation effort only on the within-person level. On the between-person level self-regulation effort was strongly negatively related with occupational self-efficacy. Thus, we conclude that depending the perspective on procrastination (e.g., differential psychology perspective vs. situational perspective) different variables will be considered relevant to explain the emergence of procrastination.