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Background: The standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology. However, its use as a standard for health research is still less prevalent. To use existing data sources more efficiently for health research, data interoperability becomes increasingly important. FHIR provides solutions by offering resource domains such as “Public Health & Research” and “Evidence-Based Medicine” while using already established web technologies. Therefore, FHIR could help standardize data across different data sources and improve interoperability in health research. Objective: The aim of our study was to provide a systematic review of existing literature and determine the current state of FHIR implementations in health research and possible future directions. Methods: We searched the PubMed/MEDLINE, Embase, Web of Science, IEEE Xplore, and Cochrane Library databases for studies published from 2011 to 2022. Studies investigating the use of FHIR in health research were included. Articles published before 2011, abstracts, reviews, editorials, and expert opinions were excluded. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and registered this study with PROSPERO (CRD42021235393). Data synthesis was done in tables and figures. Results: We identified a total of 998 studies, of which 49 studies were eligible for inclusion. Of the 49 studies, most (73%, n=36) covered the domain of clinical research, whereas the remaining studies focused on public health or epidemiology (6%, n=3) or did not specify their research domain (20%, n=10). Studies used FHIR for data capture (29%, n=14), standardization of data (41%, n=20), analysis (12%, n=6), recruitment (14%, n=7), and consent management (4%, n=2). Most (55%, 27/49) of the studies had a generic approach, and 55% (12/22) of the studies focusing on specific medical specialties (infectious disease, genomics, oncology, environmental health, imaging, and pulmonary hypertension) reported their solutions to be conferrable to other use cases. Most (63%, 31/49) of the studies reported using additional data models or terminologies: Systematized Nomenclature of Medicine Clinical Terms (29%, n=14), Logical Observation Identifiers Names and Codes (37%, n=18), International Classification of Diseases 10th Revision (18%, n=9), Observational Medical Outcomes Partnership common data model (12%, n=6), and others (43%, n=21). Only 4 (8%) studies used a FHIR resource from the domain “Public Health & Research.” Limitations using FHIR included the possible change in the content of FHIR resources, safety, legal matters, and the need for a FHIR server. Conclusions: Our review found that FHIR can be implemented in health research, and the areas of application are broad and generalizable in most use cases. The implementation of international terminologies was common, and other standards such as the Observational Medical Outcomes Partnership common data model could be used as a complement to FHIR. Limitations such as the change of FHIR content, lack of FHIR implementation, safety, and legal matters need to be addressed in future releases to expand the use of FHIR and, therefore, interoperability in health research.

The COVID-19 pandemic has spurred large-scale, interinstitutional research efforts. To enable these efforts, researchers must agree on data set definitions that not only cover all elements relevant to the respective medical specialty but also are syntactically and semantically interoperable. Therefore, the German Corona Consensus (GECCO) data set was developed as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As the GECCO data set is a compact core data set comprising data across all medical fields, the focused research within particular medical domains demands the definition of extension modules that include data elements that are the most relevant to the research performed in those individual medical specialties. We aimed to (1) specify a workflow for the development of interoperable data set definitions that involves close collaboration between medical experts and information scientists and (2) apply the workflow to develop data set definitions that include data elements that are the most relevant to COVID-19-related patient research regarding immunization, pediatrics, and cardiology. We developed a workflow to create data set definitions that were (1) content-wise as relevant as possible to a specific field of study and (2) universally usable across computer systems, institutions, and countries (ie, interoperable). We then gathered medical experts from 3 specialties-infectious diseases (with a focus on immunization), pediatrics, and cardiology-to select data elements that were the most relevant to COVID-19-related patient research in the respective specialty. We mapped the data elements to international standardized vocabularies and created data exchange specifications, using Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR). All steps were performed in close interdisciplinary collaboration with medical domain experts and medical information specialists. Profiles and vocabulary mappings were syntactically and semantically validated in a 2-stage process. We created GECCO extension modules for the immunization, pediatrics, and cardiology domains according to pandemic-related requests. The data elements included in each module were selected, according to the developed consensus-based workflow, by medical experts from these specialties to ensure that the contents aligned with their research needs. We defined data set specifications for 48 immunization, 150 pediatrics, and 52 cardiology data elements that complement the GECCO core data set. We created and published implementation guides, example implementations, and data set annotations for each extension module. The GECCO extension modules, which contain data elements that are the most relevant to COVID-19-related patient research on infectious diseases (with a focus on immunization), pediatrics, and cardiology, were defined in an interdisciplinary, iterative, consensus-based workflow that may serve as a blueprint for developing further data set definitions. The GECCO extension modules provide standardized and harmonized definitions of specialty-related data sets that can help enable interinstitutional and cross-country COVID-19 research in these specialties.

The COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, researchers must agree on dataset definitions that not only cover all elements relevant to the respective medical specialty but that are also syntactically and semantically interoperable. Following such an effort, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demands the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties. To (i) specify a workflow for the development of interoperable dataset definitions that involves a close collaboration between medical experts and information scientists and to (ii) apply the workflow to develop dataset definitions that include data elements most relevant to COVID-19-related patient research regarding immunization, pediatrics, and cardiology. We developed a workflow to create dataset definitions that are (i) content-wise as relevant as possible to a specific field of study and (ii) universally usable across computer systems, institutions, and countries, i.e., interoperable. We then gathered medical experts from three specialties (infectious diseases with a focus on immunization, pediatrics, and cardiology) to the select data elements most relevant to COVID-19-related patient research in the respective specialty. We mapped the data elements to international standardized vocabularies and created data exchange specifications using HL7 FHIR. All steps were performed in close interdisciplinary collaboration between medical domain experts and medical information specialists. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process. We created GECCO extension modules for the immunization, pediatrics, and cardiology domains with respect to the pandemic requests. The data elements included in each of these modules were selected according to the here developed consensus-based workflow by medical experts from the respective specialty to ensure that the contents are aligned with the respective research needs. We defined dataset specifications for a total number of 48 (immunization), 150 (pediatrics), and 52 (cardiology) data elements that complement the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module. These here presented GECCO extension modules, which contain data elements most relevant to COVID-19-related patient research in infectious diseases with a focus on immunization, pediatrics and cardiology, were defined in an interdisciplinary, iterative, consensus-based workflow that may serve as a blueprint for the development of further dataset definitions. The GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties.

Mayaro virus (MAYV) is an alphavirus endemic to South and Central America associated with sporadic outbreaks in humans. MAYV infection causes severe joint and muscle pain that can persist for weeks to months. Currently, there are no approved vaccines or therapeutics to prevent MAYV infection or treat the debilitating musculoskeletal inflammatory disease. In the current study, a prophylactic MAYV vaccine expressing the complete viral structural polyprotein was developed based on a non-replicating human adenovirus V (AdV) platform. Vaccination with AdV-MAYV elicited potent neutralizing antibodies that protected WT mice against MAYV challenge by preventing viremia, reducing viral dissemination to tissues and mitigating viral disease. The vaccine also prevented viral-mediated demise in IFN⍺R1 -/- mice. Passive transfer of immune serum from vaccinated animals similarly prevented infection and disease in WT mice as well as virus-induced demise of IFN⍺R1 -/- mice, indicating that antiviral antibodies are protective. Immunization with AdV-MAYV also generated cross-neutralizing antibodies against two related arthritogenic alphaviruses–chikungunya and Una viruses. These cross-neutralizing antibodies were protective against lethal infection in IFN⍺R1 -/- mice following challenge with these heterotypic alphaviruses. These results indicate AdV-MAYV elicits protective immune responses with substantial cross-reactivity and protective efficacy against other arthritogenic alphaviruses. Our findings also highlight the potential for development of a multi-virus targeting vaccine against alphaviruses with endemic and epidemic potential in the Americas.

Background Gene expression analysis is a crucial tool for uncovering the biological mechanisms that underlie differences between patient subgroups, offering insights that can inform clinical decisions. However, despite its potential, gene expression analysis remains challenging for clinicians due to the specialised skills required to access, integrate, and analyse large datasets. Existing tools primarily focus on RNA-Seq data analysis, providing user-friendly interfaces but often falling short in several critical areas: they typically do not integrate clinical data, lack support for patient-specific analyses, and offer limited flexibility in exploring relationships between gene expression and clinical outcomes in disease cohorts. Users, including clinicians with a general knowledge of transcriptomics, however, who may have limited programming experience, are increasingly seeking tools that go beyond traditional analysis. To overcome these issues, computational tools must incorporate advanced techniques, such as machine learning, to better understand how gene expression correlates with patient symptoms of interest. Results Our RNAcare platform, addresses these limitations by offering an interactive and reproducible solution specifically designed for analysing transcriptomic data from patient samples in a clinical context. This enables researchers to directly integrate gene expression data with clinical features, perform exploratory data analysis, and identify patterns among patients with similar diseases. By enabling users to integrate transcriptomic and clinical data, and customise the target label, the platform facilitates the analysis of the relationships between gene expression and clinical symptoms like pain and fatigue. This allows users to generate hypotheses and illustrative visualisations/reports to support their research. As proof of concept, we use RNAcare to link inflammation-related genes to pain and fatigue in rheumatoid arthritis (RA) and detect signatures in the drug response group, confirming previous findings. Conclusion We present a novel computational platform allowing the interpretation of clinical and transcriptomics data in real-time. The platform can be used for data generated by the user, such as the patient data presented here or using published datasets. The platform is available at https://rna-care.mvls.gla.ac.uk/ , and its source code is https://github.com/sii-scRNA-Seq/RNAcare/ .

Chikungunya virus (CHIKV) is a reemerging alphavirus that causes acute febrile illness and severe joint pain in humans. Although acute symptoms often resolve within a few days, chronic joint and muscle pain can be long lasting. In the last decade, CHIKV has caused widespread outbreaks of unprecedented scale in the Americas, Asia, and the Indian Ocean island regions. Despite these outbreaks and the continued expansion of CHIKV into new areas, mechanisms of chikungunya pathogenesis and disease are not well understood. Experimental animal models are indispensable to the field of CHIKV research. The most commonly used experimental animal models of CHIKV infection are mice and nonhuman primates; each model has its advantages for studying different aspects of CHIKV disease. This review will provide an overview of animal models used to study CHIKV infection and disease and major advances in our understanding of chikungunya obtained from studies performed in these models.

Purposes Postoperative infections are the common cause of morbidity and mortality in colorectal surgery. Calprotectin, an S100 protein, may be a potential marker. The aim was to define the postoperative course (PC) of calprotectin in serum (CIS), compared to white blood cell (WBC), C-reactive protein (CRP), lactate and procalcitonin (PCT). Methods This prospective, single-center study measured all biomarkers preoperatively, on the first, third and fifth postoperative days (POD). The endpoint was the PC of CIS compared to the WBC, CRP, PCT, and lactate. CIS between benign and malignant disease and between patients with and without complications was compared. Also a correlation was carried out. Results 56 patients (14 rectum, 42 colon) were included. The postoperative CIS increased to preoperative values ( p  < 0.05). The preoperative CIS in malignant disease was higher ( p  < 0.05). CIS on the first and fifth POD was higher in the group with complications ( p  < 0.05). CIS and CRP, WBC and CRP on the third POD ( p  < 0.05) correlate in complications . Conclusion We present the normal course of CIS and its potential function as a marker for systemic inflammation. We showed that postoperative CIS were significantly higher in complications. Although our study has several limitations, - namely a small sample size and heterogeneous surgical procedures -, serum calprotectin may be an interesting biomarker for future larger studies, particularly due to its increased specificity for intestinal inflammation. Trial registration This study is prospectively registered in DRKS-ID (Deutsches Register Klinischer Studien, German WHO Register): DRKS00027142, data of registration 29.11.2021. This is a WHO-recognized primary registry that meets the requirements of the International Committee of Medical Journal Editors (ICMJE). We registered this study in the DKG (Deutsche Krebsgesellschaft) Sudybox ST-D512, data of registration 11.03.2022.

Chikungunya virus (CHIKV) infections can cause severe and debilitating joint and muscular pain that can be long lasting. Current CHIKV vaccines under development rely on the generation of neutralizing antibodies for protection; however, the role of T cells in controlling CHIKV infection and disease is still unclear. Using an overlapping peptide library, we identified the CHIKV-specific T cell receptor epitopes recognized in C57BL/6 infected mice at 7 and 14 days post-infection. A fusion protein containing peptides 451, 416, a small region of nsP4, peptide 47, and an HA tag (CHKVf5) was expressed using adenovirus and cytomegalovirus-vectored vaccines. Mice vaccinated with CHKVf5 elicited robust T cell responses to higher levels than normally observed following CHIKV infection, but the vaccine vectors did not elicit neutralizing antibodies. CHKVf5-vaccinated mice had significantly reduced infectious viral load when challenged by intramuscular CHIKV injection. Depletion of both CD4 and CD8 T cells in vaccinated mice rendered them fully susceptible to intramuscular CHIKV challenge. Depletion of CD8 T cells alone reduced vaccine efficacy, albeit to a lesser extent, but depletion of only CD4 T cells did not reverse the protective phenotype. These data demonstrated a protective role for CD8 T cells in CHIKV infection. However, CHKVf5-vaccinated mice that were challenged by footpad inoculation demonstrated equal viral loads and increased footpad swelling at 3 dpi, which we attributed to the presence of CD4 T cell receptor epitopes present in the vaccine. Indeed, vaccination of mice with vectors expressing only CHIKV-specific CD8 T cell epitopes followed by CHIKV challenge in the footpad prevented footpad swelling and reduced proinflammatory cytokine and chemokines associated with disease, indicating that CHIKV-specific CD8 T cells prevent CHIKV disease. These results also indicate that a T cell-biased prophylactic vaccination approach is effective against CHIKV challenge and reduces CHIKV-induced disease in mice.

Deformable objects such as ropes, wires, and clothing are omnipresent in society and industry but are little researched in robotics research. This is due to the infinite amount of possible state configurations caused by the deformations of the deformable object. Engineered approaches try to cope with this by implementing highly complex operations in order to estimate the state of the deformable object. This complexity can be circumvented by utilizing learning-based approaches, such as reinforcement learning, which can deal with the intrinsic high-dimensional state space of deformable objects. However, the reward function in reinforcement learning needs to measure the state configuration of the highly deformable object. Vision-based reward functions are difficult to implement, given the high dimensionality of the state and complex dynamic behavior. In this work, we propose the consideration of concepts beyond vision and incorporate other modalities which can be extracted from deformable objects. By integrating tactile sensor cells into a textile piece, proprioceptive capabilities are gained that are valuable as they provide a reward function to a reinforcement learning agent. We demonstrate on a low-cost dual robotic arm setup that a physical agent can learn on a single CPU core to fold a rectangular patch of textile in the real world based on a learned reward function from tactile information.

Water isotopologues, as natural tracers of the hydrological cycle on Earth, provide a unique way to assess the skill of climate models in representing realistic atmospheric‐terrestrial water pathways. This study presents the newly developed WRF‐Hydro‐iso, which is a version of the coupled atmospheric‐hydrological WRF‐Hydro model enhanced with a joint soil‐vegetation‐atmospheric description of water isotopologue motions. It allows the consideration of isotopic fractionation processes during water phase changes in the atmosphere, the land surface, and the subsurface. For validation, WRF‐Hydro‐iso is applied to two different climate zones, namely Europe and Southern Africa under the present climate conditions. Each case is modeled with a domain employing a 5 km grid‐spacing coupled with a terrestrial subgrid employing a 500 m grid‐spacing in order to represent lateral terrestrial water flow. A 10‐year slice is simulated for 2003–2012, using ERA5 reanalyses as driving data. The boundary condition of isotopic variables is prescribed with mean values from a 10‐year simulation with the Community Earth System Model Version 1. WRF‐Hydro‐iso realistically reproduces the climatological variations of the isotopic concentrations δPO18 and δPH2 from the Global Network of Isotopes in Precipitation. In a sensitivity analysis, it is found that land surface evaporation fractionation increases the isotopic concentrations in the rootzone soil moisture and slightly decreases the isotopic concentrations in precipitation. Lateral terrestrial water flow minorly affects these isotopic concentrations through changes in evaporation‐transpiration partitioning. Plain Language Summary Global climate models are limited by their coarse resolution, which may reduce their meaningfulness. This problem can be circumvented for a specific region with regional climate models, which provide, for example, a detailed description of clouds and land‐atmosphere interactions. But it remains a question: How realistic is the model representation of water transport through the different compartments of the hydrological cycle, the atmosphere, the land, and the sea? A unique way to assess modeled water transport is the comparison to natural tracers, such as water isotopologues, which requires to include the fate of these water isotopologues in the model. This is what we pursue here with the newly developed WRF‐Hydro‐iso model. A model description and a proof of concept are provided for two climate zones, using the Global Network of Isotopes in Precipitation data set as reference. Key Points A new coupled atmospheric‐hydrological regional modeling system of water isotopologues is presented Land surface evaporation fractionation increases the isotopic concentrations in the rootzone Lateral terrestrial water flow has a minor effect on isotopic concentrations in the rootzone