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This paper deals with foreign state-run media outlets that disseminate Persian language news targeted to the Iranian public. More specifically, it focuses on the mobile news app Telegram by undertaking a content analysis of a sample of the top 400 most viewed stories across four channels, i.e., BBC Persian, Voice of America's Persian language service VOA Farsi, Radio Farda, and Iran International television channel. BMS493 It also offers a topic modelling of all news stories posted. Results show that most of the news coverage centered on politics, particularly with an emphasis on internal Iranian issues, while a few other channels repeatedly urged their followers to submit not only their email addresses and other private information, but also photographs and/or videos of anti-government protests. Conceptually, I consider these channels as portable alternative media, as opposed to state-run news media, since the Iranian public seeks them out as sources of political information that assist them in better understanding world news and, most importantly, news about their own country. The Telegram instant messaging app is related to the meso dimension of alternative media, meaning that it is characterized by the unique production and dissemination means it utilizes. This paper concludes by highlighting the implications of foreign state-run news outlets using news loopholing to disseminate information, while simultaneously collecting private information about their users and/or potentially risking their safety.Following the immense impact of the COVID-19 pandemic on health and everyday lives world-wide, people's fear of COVID-19 has been studied in a number of settings using the Fear of COVID scale. In Sweden, virus-preventing strategies have differed from comparable countries, with low use of formal lock-down procedures. It is crucial to study correlates of non-compliance with COVID-19 recommendations, and unwillingness to become vaccinated. This study aims to study whether fear of COVID is associated with mental distress and attitudes towards the pandemic, and to study correlates of non-compliance with key anti-COVID recommendations and with reluctancy to vaccination. This anonymous online survey study in web panel participants (N = 1,501) aimed to study a range of behavioral changes during COVID-19. Recommendations and vaccinations reluctancy were analyzed in logistic regressions against socio-demographic data, COVID-19 status, and mental health history. Internal consistency of the Fear of COVID scale was calculated. The Fear of COVID scale had a satisfactory internal consistency (Cronbach-alpha 0.84), and was significantly associated with compliance with all COVID-19 recommendations and with mental health. Non-compliance with recommendations was associated with low fear of disease and younger age, among other variables. Being against vaccination was associated, among other variables, with low fear of disease and with low education. In conclusion, the Fear of COVID scale appears to be associated with key attitudes towards the COVID-19 disease. Anti-virus strategies may need to promote compliance with recommendations in subgroups who feel low fear of disease or who believe not to be in a risk group for severe disease.Since March 2020, it is known that Indonesia has experienced the impact of the Corona Virus Disease 2019 (Covid-19) Pandemic, and making health workers at the forefront of potential exposure to the Covid-19 virus because they have to deal with Covid-19 patients every day during the pandemic. The purpose of this study is to focus on developing an empirical model to increase job performance in the hospital to create the right quality of work and still make the organization grow well sustainably. This study will use assistance of quality of work life, organizational commitment, job satisfaction, and psychological empowerment in creating an increase in job performance needed by nurses at the Type B Hospital in Jakarta. This study uses a quantitative approach through a questionnaire survey method conducted on nurses at 36 Type B Hospital in Jakarta, totalling 400 respondents. The sample used in this study was obtained through a purposive sampling technique with the criteria of a service period of more than 2 years. Then the data were analysed using the Structural Equation Model (SEM). Though quality of work life is found to significantly affect organizational commitment, job satisfaction and psychological empowerment, its effect on job performance found to be insignificant. Further findings will be discussed further.Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease's initiation and progression as well as developing drug therapeutics.Blood tests play an essential role in everyday medicine and are used by doctors in several diagnostic procedures. Moreover, this data is multivariate - and often some diseases, such as COVID-19, could have different symptom manifestations and outcomes. This study proposes a method of extracting useful information from blood tests using UMAP technique - Uniform Manifold Approximation and Projection for Dimension Reduction combined with DBSCAN clustering and statistical approaches. The analysis performed here indicates several clusters of infection prevalence varying between 2%-37%, showing that our procedure is indeed capable of finding different patterns. A possible explanation is that COVID-19 is not just a respiratory infection but a systemic disease with critical hematological implications, primarily on white-cell fractions, as indicated by relevant statistical test p -values in the range of 0.03-0.1. The novel analysis procedure proposed could be adopted in other data-sets of different illnesses to help researchers to discover new patterns of data that could be used in various diseases and contexts.To draw real-world evidence about the comparative effectiveness of multiple time-varying treatment regimens on patient survival, we develop a joint marginal structural proportional hazards model and novel weighting schemes in continuous time to account for time-varying confounding and censoring. Our methods formulate complex longitudinal treatments with multiple ``start/stop switches as the recurrent events with discontinuous intervals of treatment eligibility. We derive the weights in continuous time to handle a complex longitudinal dataset on its own terms, without the need to discretize or artificially align the measurement times. We further propose using machine learning models designed for censored survival data with time-varying covariates and the kernel function estimator of the baseline intensity to efficiently estimate the continuous-time weights. Our simulations demonstrate that the proposed methods provide better bias reduction and nominal coverage probability when analyzing observational longitudinal survival data with irregularly spaced time intervals, compared to conventional methods that require aligned measurement time points. We apply the proposed methods to a large-scale COVID-19 dataset to estimate the causal effects of several COVID-19 treatment strategies on in-hospital mortality or ICU admission, and provide new insights relative to findings from randomized trials.In individual SARS-CoV-2 outbreaks, the count of confirmed cases and deaths follow a Gompertz growth function for locations of very different sizes. This lack of dependence on region size leads us to hypothesize that virus spread depends on universal properties of the network of social interactions. We test this hypothesis by simulating the propagation of a virus on networks of different topologies. Our main finding is that Gompertz growth observed for early outbreaks occurs only for a scale-free network, in which nodes with many more neighbors than average are common. These nodes that have very many neighbors are infected early in the outbreak and then spread the infection very rapidly. When these nodes are no longer infectious, the remaining nodes that have most neighbors take over and continue to spread the infection. In this way, the rate of spread is fastest at the very start and slows down immediately. Geometrically it is seen that the "surface" of the epidemic, the number of susceptible nodes in contact with the infected nodes, starts to rapidly decrease very early in the epidemic and as soon as the larger nodes have been infected. In our simulation, the speed and impact of an outbreak depend on three parameters the average number of contacts each node makes, the probability of being infected by a neighbor, and the probability of recovery. Intelligent interventions to reduce the impact of future outbreaks need to focus on these critical parameters in order to minimize economic and social collateral damage.Cerebral arteries play a crucial role in the regulation of blood flow to the brain to satisfy the demand of oxygen and glucose for proper function of the organ. Physiological cerebral blood flow (CBF) is maintained within a normal range in response to changes in blood pressure a mechanism named Cerebral Blood Flow Auto Regulation (CBFAR). Structure and function of cerebral arteries have an important impact on CBFAR. Several studies in human and animals have showed significant morphological and functional changes in cerebral vessels of aged brain associated with a reduced CBF which is also impaired in cerebrovascular pathology linked to brain diseases. Interestingly, one new emergent aspect is the lifelong Calorie Restriction (CR) as a potential intervention to prevent age-related cerebral artery changes and preserve the health of aging brain. This review summarizes the recent literature on the effects of aging on cerebral artery structure and function and the potential of CR as opportunities for prevention and treatment.