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Most of the included CDSS studies were associated with positive patient outcomes effects but with substantial differences regarding the clinical impact. A subset of 6 disease entities could be filtered in which CDSS should be given special consideration at sites where computer-assisted decision-making is deemed to be underutilized. Registration number on PROSPERO: CRD42016049946.

This paper aims to numerically assess the effects of electrolyte properties and electrode morphology on the capacitance of electric double layer capacitors (EDLCs) made of mesoporous electrodes consisting of ordered cylindrical pores in non-aqueous electro-lytes. Simulations solved a three-dimensional modified Poisson-Boltzmann model. They accounted for the finite size of ions and field-dependent electrolyte permittivity while the pores were perpendicular to the current collector. The effects of pore radius, porosity, effective ion diameter, and electrolyte field-dependent permittivity on the diffuse layer gravimetric capacitance were investigated systematically in order to determine key parameters affecting EDLCs ’ performance. The simulations showed that reducing the ion effective diameter and the pore radius resulted in the strongest increase in diffuse layer gravimetric capacitance up to a critical radius below which the capacitance reaches a plateau. Increasing the electrode porosity also increased the diffuse layer gravimetric capacitance. Accounting for more realistic field-dependent permittivity was found to significantly reduce the pre-dicted diffuse layer gravimetric capacitance. Finally, accounting for the contribution of the Stern layer to the total capacitance was essential in predicting experimental data for a wide range of porous activated carbon electrodes and non-aqueous electrolytes. VC 2011 The Electrochemical Society. [DOI: 10.1149/1.3622342] All rights reserved. Manuscript submitted March 4, 2011; revised manuscript received July 13, 2011. Published August 8, 2011. Supercapacitors or electric double layer capacitors (EDLCs) are energy storage devices that store electric charge in the electric dou

Introduction: Information systems are a key success factor for medical research and healthcare. Currently, most of these systems apply heterogeneous and proprietary data models, which impede data exchange and integrated data analysis for scientific purposes. Due to the complexity of medical terminology, the overall number of medical data models is very high. At present, the vast majority of these models are not available to the scientific community. The objective of the Portal of Medical Data Models (MDM, https://medical-data-models.org) is to foster sharing of medical data models.Methods: MDM is a registered European information infrastructure. It provides a multilingual platform for exchange and discussion of data models in medicine, both for medical research and healthcare. The system is developed in collaboration with the University Library of Münster to ensure sustainability. A web front-end enables users to search, view, download and discuss data models. Eleven different export formats are available (ODM, PDF, CDA, CSV, MACRO-XML, REDCap, SQL, SPSS, ADL, R, XLSX). MDM contents were analysed with descriptive statistics.Results: MDM contains 4387 current versions of data models (in total 10 963 versions). 2475 of these models belong to oncology trials. The most common keyword (n = 3826) is ‘Clinical Trial’; most frequent diseases are breast cancer, leukemia, lung and colorectal neoplasms. Most common languages of data elements are English (n = 328 557) and German (n = 68 738).Semantic annotations (UMLS codes) are available for 108 412 data items, 2453 item groups and 35 361 code list items. Overall 335 087 UMLS codes are assigned with 21 847 unique codes. Few UMLS codes are used several thousand times, but there is a long tail of rarely used codes in the frequency distribution.Discussion: Expected benefits of the MDM portal are improved and accelerated design of medical data models by sharing best practice, more standardised data models with semantic annotation and better information exchange between information systems, in particular Electronic Data Capture (EDC) and Electronic Health Records (EHR) systems. Contents of the MDM portal need to be further expanded to reach broad coverage of all relevant medical domains.Database URL: https://medical-data-models.org

<b><i>Background:</i></b> Artificial intelligence (AI) applications that utilize machine learning are on the rise in clinical research and provide highly promising applications in specific use cases. However, wide clinical adoption remains far off. This review reflects on common barriers and current solution approaches. <b><i>Summary:</i></b> Key challenges are abbreviated as the RISE criteria: Regulatory aspects, Interpretability, interoperability, and the need for Structured data and Evidence. As reoccurring barriers of AI adoption, these concepts are delineated and complemented by points to consider and possible solutions for effective and safe use of AI applications. <b><i>Key Messages:</i></b> There is a fraction of AI applications with proven clinical benefits and regulatory approval. Many new promising systems are the subject of current research but share common issues for wide clinical adoption. The RISE criteria can support preparation for challenges and pitfalls when designing or introducing AI applications into clinical practice.

A finite element implementation of the transient nonlinear Nernst-Planck-Poisson (NPP) and Nernst-Planck-Poisson-modified Stern (NPPMS) models is presented. The NPPMS model uses multipoint constraints to account for finite ion size, resulting in realistic ion concentrations even at high surface potential. The Poisson-Boltzmann equation is used to provide a limited check of the transient models for low surface potential and dilute bulk solutions. The effects of the surface potential and bulk molarity on the electric potential and ion concentrations as functions of space and time are studied. The ability of the models to predict realistic energy storage capacity is investigated. The predicted energy is much more sensitive to surface potential than to bulk solution molarity.

With increasing numbers of patients recovering from COVID-19, there is increasing evidence for persistent symptoms and the need for follow-up studies. This retrospective study included patients without comorbidities, who recovered from COVID-19 and attended an outpatient clinic at a university hospital for follow-up care and potential convalescent plasma donation. Network analysis was applied to visualize symptom combinations and persistent symptoms. Comprehensive lab-testing was ascertained at each follow-up to analyze differences regarding patients with vs without persistent symptoms. 116 patients were included, age range was 18–69 years (median: 41) with follow-ups ranging from 22 to 102 days. The three most frequent persistent symptoms were Fatigue (54%), Dyspnea (29%) and Anosmia (25%). Lymphopenia was present in 13 of 112 (12%) cases. Five of 35 cases (14%) had Lymphopenia in the later follow-up range of 80–102 days. Serum IgA concentration was the only lab parameter with significant difference between patients with vs without persistent symptoms with reduced serum IgA concentrations in the patient cohort of persistent symptoms (p = 0.0219). Moreover, subgroup analyses showed that patients with lymphopenia experienced more frequently persistent symptoms. In conclusion, lymphopenia persisted in a noticeable percentage of recovered patients. Patients with persistent symptoms had significantly lower serum IgA levels. Furthermore, our data provides evidence that lymphopenia is associated with persistence of COVID-19 symptoms.

BackgroundThe volume and complexity of patient data – especially in personalised medicine – is steadily increasing, both regarding clinical data and genomic profiles: Typically more than 1,000 items (e.g., laboratory values, vital signs, diagnostic tests etc.) are collected per patient in clinical trials. In oncology hundreds of mutations can potentially be detected for each patient by genomic profiling. Therefore data integration from multiple sources constitutes a key challenge for medical research and healthcare.MethodsSemantic annotation of data elements can facilitate to identify matching data elements in different sources and thereby supports data integration. Millions of different annotations are required due to the semantic richness of patient data. These annotations should be uniform, i.e., two matching data elements shall contain the same annotations. However, large terminologies like SNOMED CT or UMLS don’t provide uniform coding. It is proposed to develop semantic annotations of medical data elements based on a large-scale public metadata repository. To achieve uniform codes, semantic annotations shall be re-used if a matching data element is available in the metadata repository.ResultsA web-based tool called ODMedit (https://odmeditor.uni-muenster.de/) was developed to create data models with uniform semantic annotations. It contains ~800,000 terms with semantic annotations which were derived from ~5,800 models from the portal of medical data models (MDM). The tool was successfully applied to manually annotate 22 forms with 292 data items from CDISC and to update 1,495 data models of the MDM portal.ConclusionUniform manual semantic annotation of data models is feasible in principle, but requires a large-scale collaborative effort due to the semantic richness of patient data. A web-based tool for these annotations is available, which is linked to a public metadata repository.