An unidentified pneumonia outbreak was first observed in Wuhan, the capital of Hubei Province, China, in December 2019. WHO officially named the disease, Coronavirus Disease 2019 (COVID-19), and declared it as pandemic on Mar 11, 2020. Globally, there are more than 3 million confirmed cases with nearly 200,000 deaths. Hence, we aimed to perform a systematic review and pooled analysis of the current published literature on COVID-19 to provide an insight on the epidemiological and clinical characteristics of COVID-19 patients. A systematic search of published peer-reviewed articles that reported cases with demographical and clinical features of real-time reverse transcriptase polymerase chain reaction (rRT-PCR)-confirmed SARS-CoV-2 infection using MOOSE guidelines was conducted from December 1, 2019, to April 27, 2020, and 50 eligible articles were included for the final analysis. Review articles, opinion articles, and letters not presenting original data as well as studies with incomplete information were excluded. We included a total of 6635 patients from 50 articles, with 54.5 % being male. The predominant symptoms were fever (80.3%), cough (64.2%), and fatigue/myalgia (36.5%) and other symptoms including dyspnea, chest pain, and sore throat. We also found patients with GI symptoms like diarrhea (9.2%) and nausea/vomiting (5.2%). Comorbidities were found in 3,435 (51.7%) patients with the most common being hypertension (22.67%) followed by diabetes mellitus (12.78%). COVID-19 pandemic is not only leading a huge burden on health care facilities but significant disruption in the world society. Patients with coexisting comorbidities are at higher risk and need more utilization of health care resources. As this virus is spreading globally, all countries have to join hands and prepare at all levels of human resources, infrastructure, and facilities to combat the COVID-19 disease.Keywords COVID-19 . coronavirus disease . SARS-CoV-2 . 2019-nCoV . pandemic . outbreak . epidemiology This article is part of the Topical Collection on Covid-19

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Several indexes are used to classify physician burnout, with the Maslach Burnout Inventory currently being the most widely accepted. This index measures physician burnout based on emotional exhaustion, detachment from work, and lack of personal achievement. The overall percentage of physicians with burnout is estimated to be around 40%, but the proportion varies between specialties. Neurology currently has the second-highest rate of burnout and is projected to eventually take the top position. The purpose of this review is to provide a comprehensive overview focusing on the causes and ramifications of burnout and possible strategies for addressing the crisis. Several factors contribute to burnout among neurologist, including psychological trauma associated with patient care and a lack of respect compared to other specialties. Various interventions have been proposed for reducing burnout, and this article explores the feasibility of some of them. Burnout not only impacts the physician but also has adverse effects on the overall quality of patient care and places a strain on the health-care system. Burnout has only recently been recognized and accepted as a health crisis globally, and hence most of the proposed action plans have not been validated. More studies are needed to evaluate the long-term effects of such interventions.

AbstractThe day-to-day use of digital devices with Internet access, such as tablets and smartphones, has increased exponentially in recent years and this has had a consequent effect on the usage of the Internet and social media networks. When using social networks, people share personal data that is broadcast between users, which provides useful information for organizations. This means that characterizing users through their social media activity is an emerging research area in the field of Natural Language Processing (NLP) and this paper will present a review of how personality can be detected using online content.Approach A systematic literature review identified 30 papers published between 2007 and 2019, while particular inclusion and exclusion criteria were used to select the most relevant articles.Outcomes This review describes a variety of challenges and trends, as well as providing ideas for the direction of future research. In addition, personality trait identification and techniques were classified into different types, including deep learning, machine learning (ML) and semi-supervised/hybrid.Implications This paper’s outcomes will not only facilitate insight into the various personality types and models but will also provide knowledge about the relevant detection techniques.Novelty While prior studies have conducted literature reviews in the personality trait detection field, the systematic literature review in this paper provides specific answers to the proposed research questions. This is novel to this field as this particular type of study has not been conducted before.

Bipolar fuzzy sets (BFSs) are effective tool for dealing with bipolarity and fuzziness. The sine trigonometric functions having two significant features, namely, periodicity and symmetry about the origin, are helping in decision analysis and information analysis. Taking the advantage of sine trigonometric functions and significance of BFSs, innovative sine trigonometric operational laws (STOLs) are proposed. New aggregation operators (AOs) are developed based on proposed operational laws to aggregate bipolar fuzzy information. Certain characteristics of these operators are also discussed, such as boundedness, monotonicity, and idempotency. Moreover, a modified superiority and inferiority ranking (SIR) method is proposed to cope with multicriteria group decision-making (MCGDM) with bipolar fuzzy (BF) information. To exhibit the relevance and feasibility of this methodology, a robust application of best medical tourism supply chain is presented. Finally, a comprehensive comparative and sensitivity analysis is evaluated to validate the efficiency of suggested methodology.

The fabrication, characterization, and CO2 gas detection performance of single component-based and hetero-nanostructure-based optical gas sensors are reported in the present work. Single component-based structures include (i) TiO2 thin films with varied film thickness (37.45 nm, 51.92 nm, and 99.55 nm) fabricated via the RF sputtering system for different deposition times and (ii) silver nanoparticles (AgNPs) deposited on the glass substrate by the wet chemical method. Hetero-nanostructures were achieved by decorating the AgNPs on the predeposited TiO2 thin films. The structural, morphological, and optical characteristics of prepared samples were studied by X-ray diffraction (XRD), scanning electron microscopy (SEM), and ellipsometry, respectively. XRD analysis of AgNPs confirmed the crystalline nature of prepared particles with average crystallite size of 21 nm, however, in the case of TiO2 films XRD results suggested amorphous structure of all as-deposited films. size 21 nm. The SEM micrographs confirmed the deposition of AgNPs on the TiO2 thin films. With increasing sputtering time, TiO2 films were found to be denser and more compact, indicating a reduced porosity and higher film thickness. CO2 gas-sensing properties were investigated by measuring the optical transmission spectra in alone air and in CO2 gaseous atmosphere at room temperature. It was observed that neither TiO2 thin films even with higher thickness nor alone AgNPs could demonstrate any substantial gas-sensing activity. Nevertheless, TiO2/AgNP hetero-nanostructured substrates exhibited excellent CO2 gas-sensing performance as indicated by a huge change in the transmission spectra. The enhanced sensing efficiency of TiO2/AgNP nanostructures owing to synergistic effects suggests a promising role of our manufactured sensors in practical applications.