Cravenmangum6579
The extension of the metamodel of ISO/IEC 11179-3 is in agreement with the literature. However, further research is needed to find workable solutions for metadata management.The early detection of Heart Disease (HD) and the prediction of Heart Failure (HF) via telemonitoring and can contribute to the reduction of patients' mortality and morbidity as well as to the reduction of respective treatment costs. In this study we propose a novel classification model based on fuzzy logic applied in the context of HD detection and HF prediction. The proposed model considers that data can be represented by fuzzy phrases constructed from fuzzy words, which are fuzzy sets derived from data. Advantages of this approach include the robustness of data classification, as well as an intuitive way for feature selection. The accuracy of the proposed model is investigated on real home telemonitoring data and a publicly available dataset from UCI.The aim of this study is to build an evaluation framework for the user-centric testing of the Data Curation Tool. The tool was developed in the scope of the FAIR4Health project to make health data FAIR by transforming them from legacy formats into a Common Data Model based on HL7 FHIR. The end user evaluation framework was built by following a methodology inspired from the Delphi method. We applied a series of questionnaires to a group of experts not only in different roles and skills, but also from various parts of Europe. Overall, 26 questions were formulated for 16 participants. The results showed that the users are satisfied with the capabilities and performance of the tool. The feedbacks were considered as recommendations for technical improvement and fed back into the software development cycle of the Data Curation Tool.Reproducible information is important in science, medicine and other professional fields. Repeating the same experiment with measurement should yield the same information as the result. This original information should also be transported digitally in reproducible form, as a globally well-defined sequence of numbers. The article explains that "Domain Vectors" (DVs) with the structure "UL plus sequence of numbers" are well suited for this purpose. "UL" is an efficient link to the online definition of the sequence of numbers. DVs are globally comparable and searchable and have other important advantages. It is concluded that DVs can fill an important gap in the digital representation of information.Cancer stem cells (CSCs) represent an important field in translational medicine for generating novel cancer treatments. To identify important stemness markers in liver CSCs that potentially explain their resistance to treatment, we analyzed 10865 single-cell RNA-seq samples across 42684 coding and non-coding genes. Our results show that CSCs have significantly increased expression of two Yamanaka factors (Oct4, 2.14X and SOX2, 1.13X) and three stemness factors (CD44, 3.25X; KRT7, 2.2X; SOX9, 1.71X), relative to liver progenitor cells. Our study demonstrates the potential power of harnessing shared big data for driving translational medicine for novel hypothesis generation.Access to digital technologies depends on the availability of technical infrastructure, but this access is unequally distributed among social groups and newly summarized under the term digital divide. The aim is to analyze the perception of a tracing app to contain Covid-19 in Germany. The results showed that participants with the highest level of formal education rate the app as beneficial and were the most likely to use the app.We evaluated medication reconciliation processes of a qualitative case study at a 1000-bed public hospital. Lean tools were applied to identify factors contributing to prescribing errors and propose process improvement. Errors were attributed to the prescriber's skills, high workload, staff shortage, poor user attitude and rigid system function. Continuous evaluation of medication reconciliation efficiency is imperative to identify and mitigate errors and increase patient safety.The high demand of hospitalization in the intensive care units (ICUs) during the first wave of the COVID-19 outbreak brought out the critical issues of the limited capacity of the regional systems to deal with high patient inflows in a short period of time. In this view, a rapid and efficient reallocation of resources is one of the main challenges to be addressed by regional systems to prevent overload and saturation. Aim of this study is to assess the spatial accessibility of ICU beds in the 20 Italian regions to capture the equity distribution of critical care services across the country. This analysis may contribute to gain a deeper understanding of the allocation of health resources. It can provide input for policy makers in view of a possible reorganization of the national system in terms of both its preparedness for emergency period and routine capability.The relationship between social determinants of health (SDoH) and health outcomes is established and extends to a higher risk of contracting COVID-19. Given the factors included in SDoH, such as education level, race, rurality, and socioeconomic status are interconnected, it is unclear how individual SDoH factors may uniquely impact risk. Lower socioeconomic status often occurs in concert with lower educational attainment, for example. Because literacy provides access to information needed to avoid infection and content can be made more accessible, it is essential to determine to what extent health literacy contributes to successful containment of a pandemic. By incorporating this information into clinical data, we have isolated literacy and geographic location as SDoH factors uniquely related to the risk of COVID-19 infection. For patients with comorbidities linked to higher illness severity, residents of rural areas associated with lower health literacy at the zip code level had a greater likelihood of positive COVID-19 results unrelated to their economic status.The ongoing COVID-19 pandemic has become the most impactful pandemic of the past century. The SARS-CoV-2 virus has spread rapidly across the globe affecting and straining global health systems. More than 2 million people have died from COVID-19 (as of 30 January 2021). To lessen the pandemic's impact, advanced methods such as Artificial Intelligence models are proposed to predict mortality, morbidity, disease severity, and other outcomes and sequelae. We performed a rapid scoping literature review to identify the deep learning techniques that have been applied to predict hospital mortality in COVID-19 patients. Our review findings provide insights on the important deep learning models, data types, and features that have been reported in the literature. These summary findings will help scientists build reliable and accurate models for better intervention strategies for predicting mortality in current and future pandemic situations.COVID-19 poses a major challenge to individuals and societies around the world. Yet, it is difficult to obtain a good overview of studies across different medical fields of research such as clinical trials, epidemiology, and public health. Here, we describe a consensus metadata model to facilitate structured searches of COVID-19 studies and resources along with its implementation in three linked complementary web-based platforms. A relational database serves as central study metadata hub that secures compatibilities with common trials registries (e.g. ICTRP and standards like HL7 FHIR, CDISC ODM, and DataCite). The Central Search Hub was developed as a single-page application, the other two components with additional frontends are based on the SEEK platform and MICA, respectively. These platforms have different features concerning cohort browsing, item browsing, and access to documents and other study resources to meet divergent user needs. By this we want to promote transparent and harmonized COVID-19 research.Health-dedicated groups on social media provide different contents and social support to their peers. Our objective is to analyze users' engagement with health education and physical activity promotion posts according to the expressed social support and social media. All health education and physical activity promotion posts on Facebook, Twitter, and Instagram during 2017-2019 by a diabetes association were extracted. We identified the type of social support within these posts; and analysed the users' engagement with these posts according to the type of social support and social media channel. A total of 260 posts dealing with health education (n=200) and physical activity promotion (n=60) were published. Posts promoting physical activity received 54% more likes than posts on health education (p less then 0.05), but they were 69% less likely to receive comments and be shared (both p less then 0.01). Posts expressing tangible assistance received 6 times more likes (p less then 0.001), and the ones indicating network support almost 11 times more shares (p less then 0.05). Posts expressing two or more types of social support were the most engaging (3 times more likes, 2 times more comments, and over 6 times more shares, all p less then 0.001). Health-dedicated social media groups can be effective channels for providing health education and for promoting physical activity among individuals with diabetes. Our findings suggest that engagement with health education and physical activity promotion posts can be increased by providing tangible assistance, network support, or expressing two or more types of social support; and by posting on Facebook and Instagram.
Hepatotoxicity is very frequent and is a dangerously adverse effect of anti-TB medications. ABR-238901 inhibitor This effect can reduce the effectiveness of the treatment by compromising treatment regimens. Among these first-line quadruple therapy drugs (INH, RMP, PZA, and EMB), INH, RMP, and PZA are metabolized mostly by the liver, and due to this, are likely hepatotoxic. However, the survival times of hepatotoxicity among patients with TB in Thailand are currently not available. The aims of the present study were to assess the prevalence and survival time of drug-induced hepatotoxicity in patients with TB.
A cross-sectional retrospective study was performed to explore the survival time of the development of drug-induced hepatotoxicity among 327 patients with TB who received standard drug treatment at the TB clinic in Phichit Hospital. Data was collected from the HOSxP program and medical records from 2016 to 2018. Kaplan-Meier and Cox's regressions were used for data analysis.
The results showed that prevalence of drug-induced hepatotoxicity was 6.42% and confirmed that patients with TB who were <50 years of age will be a median survival time on drug-induced hepatotoxicity is 17 days and 30 days for those who age group ≥50 years.
The median survival time of drug-induced hepatotoxicity among patients with TB who were <50 years of age is 17 days. So, patients with TB whose ages are less than 50 years should receive liver function tests such as AST and ALT and investigate risk behavior before receiving the anti- TB treatment.
The median survival time of drug-induced hepatotoxicity among patients with TB who were less then 50 years of age is 17 days. So, patients with TB whose ages are less than 50 years should receive liver function tests such as AST and ALT and investigate risk behavior before receiving the anti- TB treatment.
