446 research outputs found

    Additional file 1 of The burden of persistent symptoms after COVID-19 (long COVID): a meta-analysis of controlled studies in children and adults

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    Additional file 1. Table S1: Supplementary preferred reporting items for systematic reviews and meta-analyses (PRISMA) checklist; Table S2: Full search strategy; Table S3: Checklist items for quality assessment of the included studies; Table S4: List of excluded studies; Table S5: Quality assessment of the included studies; Table S6: Pooled odds ratios for clinical signs and symptoms across all included studies stratified by patients’ age category regardless the hospitalization state; Fig. S1: Funnel plot of dyspnea in non-hospitalized COVID-19 patients relative to negative control; Fig. S2: Funnel plot of fatigue in non-hospitalized COVID-19 patients relative to negative control; Fig. S3: Funnel plot of brain and memory deficits in non-hospitalized COVID-19 patients relative to negative control

    On modeling the log-returns of Bitcoin and Ethereum prices against the USA Dollar

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    The study and investigation of the behavior of monetary phenomena is an interesting subject for actuaries and practitioners. In the recent age and development in the monetary and financial phenomena, cryptocurrency has gained much attention from actuaries. Over the past decade, several research studies have emerged on modeling and forecasting cryptocurrency exchange rates. This paper also contributes to the modeling of cryptocurrency exchange rates using a new version of the Logistic distribution, namely, a new cotangent-Logistic distribution. The mathematical properties and estimators of the new cotangent-logistic distribution's parameters are obtained. We illustrate the new cotangent-Logistic distribution using two financial data sets representing the log-returns of the Bitcoin and Ethereum prices. We compare the new cotangent-Logistic distribution with the baseline Logistic distribution and its modified version. Using the p-value and three other statistical tests, we show that the new cotangent-Logistic distribution repeatedly provides the optimal fit to cryptocurrency exchange rates

    Antecedents of adoption and usage of ChatGPT among Jordanian university students: Empirical study

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    This research uses Technology Acceptance Model to explore the elements influencing students' attitudes toward using Chat Generative Pre-Trained Transformer (ChatGPT), a recently developed artificial intelligence (AI) tool, for learning and educational purposes. Using Amos version 23 structural equation modelling and 880 student survey responses, the suggested model was empirically tested. According to the report, students think well of ChatGPT utilization in the classroom. Credibility, Usefulness and ease of use, all influence how positively people feel about using this technology in a classroom setting. The study's findings, however, did not support the notion that students' adoption and use of ChatGPT was insignificantly influenced by perceived enjoyment. Moreover, the results conclude that attitude mediates the relationship between usefulness and intention to use ChatGPT. The research will help businesses, educational institutions, and the global community by providing insight into how students view the ChatGPT service within a learning environment. Additionally, the application boosts learners' confidence and interest, which improves general awareness and literacy. Finally, the research will facilitate developers of AI in the betterment of their product and service delivery and regulators in regulating the use of AI-based bots. Owing to its recentness, there is not much study currently available on ChatGPT use in education. This research adds significantly to the extant knowledge on the adoption of advanced education technologies by examining the adoption characteristics of ChatGPT, a novel AI-based tool involving students. Additionally, there is a dearth of research in the literature on students' adoption of ChatGPT for educational purposes. Such a gap was filled as this study identified the factors affecting students' adoption of ChatGPT in the classroom

    Additional file 4 of Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000‚Äď2018

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    Additional file 4: Supplemental results.1. README. 2. Prevalence range across districts. 3. Prevalence range between sexes. 4. Prevalence range between ages. 5. Age-specific district ranges

    Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID-19 pandemic

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    Abstract Background A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to predict food insecurity across ten Arab countries in the Gulf and Mediterranean regions. A total of 13,443 participants were extracted from the international Corona Cooking Survey conducted by 38 different countries during the COVID -19 pandemic. Results The findings indicate that Jordanian, Palestinian, Lebanese, and Saudi Arabian respondents reported the highest rates of food insecurity in the region (15.4%, 13.7%, 13.7% and 11.3% respectively). On the other hand, Oman and Bahrain reported the lowest rates (5.4% and 5.5% respectively). Our model obtained accuracy levels of 70%-82% in all algorithms. Gradient Boosting and Random Forest techniques had the highest performance levels in predicting food insecurity (82% and 80% respectively). Place of residence, age, financial instability, difficulties in accessing food, and depression were found to be the most relevant features associated with food insecurity. Conclusions The ML algorithms seem to be an effective method in early detection and prediction of food insecurity and can profoundly aid policymaking. The integration of ML approaches in public health strategies could potentially improve the development of targeted and effective interventions to combat food insecurity in these regions and globally

    Decision-based routing for unmanned aerial vehicles and internet of things networks

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    New technologies and communication standards have changed traditional network processes. Internet of Things (IoT) is one of the emerging technologies where devices are connected to facilitate the users. When the networks are more congested due to a large number of users then the existing routing protocol and communication channels suffer from congestion, disconnection, overhead, and packet drop issues. Unmanned Aerial Vehicles (UAVs) are adopted to support the ground networks for more feasible data communication. These networks provide coverage facilities to IoT networks and provide smooth data dissemination services. Through the use of relay and cooperative communication technologies, UAVs could enlarge the communication space for IoT networks. Traditional network routing protocols have been adopted for data communication in these networks. However, the adopted protocols are not able to handle mobility and uncertain network conditions. This paper proposes a Decision-based Routing for Unmanned Aerial Vehicles and Internet of Things (DR-UAVIoT) network. The proposed protocol is useful for UAV-to-IoT and UAV-to?UAV data communication. The performance of the proposed solution is evaluated with the existing protocols in terms of data delivery, delay, and network overhead. The experimental results indicate the better performance of the proposed protocol in terms of less delay, less overhead, and better data delivery ratio as compared with existing routing protocols.</p

    Functional ability in knee osteoarthritis: role of neuropathic pain and central sensitization

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    Abstract Background Pain in osteoarthritis (OA) has been attributed traditionally to local tissue injury causing ‚Äėnociceptive pain‚Äô. However, recent studies suggest that neuropathic and central sensitization mechanisms may contribute to the pain experience. However, the relationship between these pain mechanisms and physical function has not been thoroughly addressed. This study aimed to assess the association of central sensitization and neuropathic pain with physical function in knee OA. Results Participants with a positive central sensitization inventory score (CSI) (‚Č•‚ÄČ40) had a decreased total Knee injury and Osteoarthritis Outcome Score (KOOS) and its subscales (p‚ÄČ<‚ÄČ0.001), a longer timed up and go test time (p‚ÄČ=‚ÄČ0.002) and a higher PainDETECT questionnaire (PD-Q) and visual analogue scale (p‚ÄČ<‚ÄČ0.001, p‚ÄČ=‚ÄČ0.026 respectively). The severity of Kellgren-Lawrence grading (KL) (p‚ÄČ<‚ÄČ0.001), depressive and anxiety symptoms (p‚ÄČ<‚ÄČ0.001) increased with neuropathic pain severity. In addition, participants with a high PD-Q score (‚Č•‚ÄČ19) had a longer timed up and go test time (p‚ÄČ<‚ÄČ0.001) and a decreased total KOOS score (p‚ÄČ<‚ÄČ0.001). Moreover, we found that CSI score, KOOS score, and KL grading were significantly predicted the PD-Q score (p‚ÄČ=‚ÄČ0.046, p‚ÄČ<‚ÄČ0.001, p‚ÄČ=‚ÄČ0.007, respectively). Regarding the physical function predictors, multivariate linear regression analysis revealed that pressure pain threshold at right elbow and right knee (p‚ÄČ=‚ÄČ0.005, p‚ÄČ<‚ÄČ0.001) in addition to PD-Q (P‚ÄČ<‚ÄČ0.001) were significantly associated with KOOS score, while CSI and Hospital Anxiety Depression Scale were not. Conclusion Knee OA patients with significant central sensitization and neuropathic pain reported increased pain, more functional impairment, more anxiety and depressive symptoms than OA patients without central sensitization and neuropathic pain. Additionally, neuropathic pain and presence of central sensitization were significant predictors for functional ability

    Job embeddedness and missed nursing care at the operating theatres: the mediating role of polychronicity

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    Abstract Background Perioperative missed nursing care is a serious issue that can compromise patient safety and quality of care. However, little is known about the factors that influence perioperative missed nursing care. Aim This study aimed to examine the effects of job embeddedness and polychronicity on perioperative missed nursing care as well as to test the mediating role of polychronicity on the relationship between job embeddeness and perioperative missed nursing care. Method This was a cross-sectional correlational study that used a convenience sample of 210 operating room nurses from nine hospitals in Egypt. Data were collected using self-administered questionnaires that measured job embeddedness, polychronicity, and perioperative missed nursing care. Structural equation modeling was used to test the hypothesized relationships among the variables. Results The findings demonstrated a significant negative and moderate association between missed perioperative care and both nurses’ job embeddedness and polychronicity. Moreover, there was a moderately positive and significant correlation between polychronicity and job embeddedness. Path analysis revealed a significant positive causal effect between job embeddedness and polychronicity. The results of mediation revealed that the indirect effect of job embeddedness on missed care through polychronicity was statistically significant; suggesting that polychronicity partially mediated this relationship. Conclusion This study sheds light on the intricate relationship between nurses’ job embeddedness, missed care, and polychronicity in the operating theater context. By enhancing job embeddedness and fostering polychronicity among nurses, healthcare organizations can reduce perioperative missed care and ultimately improve patient care outcomes in this critical healthcare setting

    Global, regional, and national incidence of six major immune-mediated inflammatory diseases: findings from the global burden of disease study 2019Research in context

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    Summary: Background: The causes for immune-mediated inflammatory diseases (IMIDs) are diverse and the incidence trends of IMIDs from specific causes are rarely studied. The study aims to investigate the pattern and trend of IMIDs from 1990 to¬†2019. Methods: We collected detailed information on six major causes of IMIDs, including asthma, inflammatory bowel disease, multiple sclerosis, rheumatoid arthritis, psoriasis, and atopic dermatitis, between 1990 and 2019, derived from the Global Burden of Disease study in 2019. The average annual percent change (AAPC) in number of incidents and age standardized incidence rate (ASR) on IMIDs, by sex, age, region, and causes, were calculated to quantify the temporal trends. Findings: In 2019, rheumatoid arthritis, atopic dermatitis, asthma, multiple sclerosis, psoriasis, inflammatory bowel disease accounted 1.59%, 36.17%, 54.71%, 0.09%, 6.84%, 0.60% of overall new IMIDs cases, respectively. The ASR of IMIDs showed substantial regional and global variation with the highest in High SDI region, High-income North America, and United States of America. Throughout human lifespan, the age distribution of incident cases from six IMIDs was quite different. Globally, incident cases of IMIDs increased with an AAPC of 0.68 and the ASR decreased with an AAPC of¬†‚ąí0.34 from 1990 to 2019. The incident cases increased across six IMIDs, the ASR of rheumatoid arthritis increased (0.21, 95% CI 0.18, 0.25), while the ASR of asthma (AAPC¬†=¬†‚ąí0.41), inflammatory bowel disease (AAPC¬†=¬†‚ąí0.72), multiple sclerosis (AAPC¬†=¬†‚ąí0.26), psoriasis (AAPC¬†=¬†‚ąí0.77), and atopic dermatitis (AAPC¬†=¬†‚ąí0.15) decreased. The ASR of overall and six individual IMID increased with SDI at regional and global level. Countries with higher ASR in 1990 experienced a more rapid decrease in ASR. Interpretation: The incidence patterns of IMIDs varied considerably across the world. Innovative prevention and integrative management strategy are urgently needed to mitigate the increasing ASR of rheumatoid arthritis and upsurging new cases of other five IMIDs, respectively. Funding: The Global Burden of Disease Study is funded by the Bill and Melinda Gates Foundation. The project funded by Scientific Research Fund of Sichuan Academy of Medical Sciences &amp; Sichuan Provincial People's Hospital (2022QN38)
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