Notes

The particular politics of Covid-19 vaccine self confidence.
Comparison regarding Prescribed analgesic Effectiveness of Ultrasound-Guided Transversus Abdominus Airplane Stop as well as Caudal Obstruct pertaining to Inguinal Hernia Repair in Pediatric Population: A new Single-Blinded, Randomized Governed Review.
Moreover, expression of MAFB and Sox9 was highly correlated in osteosarcoma and associated with disease progression. Combined detection of both MAFB and Sox9 represented a promising prognostic biomarker that stratified a subset of osteosarcoma patients with shortest overall survival. Taken together, these findings reveal a MAFB-Sox9 reciprocal regulatory axis driving cancer stemness and malignancy in osteosarcoma and identify novel molecular targets that might be therapeutically applicable in clinical settings. Copyright ©2020, American Association for Cancer Research.BACKGROUND The recent global outbreak of coronavirus disease (COVID-19) is affecting many countries worldwide. Iran is one of the top 10 most affected countries. Search engines provide useful data from populations, and these data might be useful to analyze epidemics. Utilizing data mining methods on electronic resources' data might provide a better insight into the COVID-19 outbreak to manage the health crisis in each country and worldwide. OBJECTIVE This study aimed to predict the incidence of COVID-19 in Iran. METHODS Data were obtained from the Google Trends website. Linear regression and long short-term memory (LSTM) models were used to estimate the number of positive COVID-19 cases. All models were evaluated using 10-fold cross-validation, and root mean square error (RMSE) was used as the performance metric. RESULTS The linear regression model predicted the incidence with an RMSE of 7.562 (SD 6.492). The most effective factors besides previous day incidence included the search frequency of handwashing, hand sanitizer, and antiseptic topics. The RMSE of the LSTM model was 27.187 (SD 20.705). CONCLUSIONS Data mining algorithms can be employed to predict trends of outbreaks. This prediction might support policymakers and health care managers to plan and allocate health care resources accordingly. ©Seyed Mohammad Ayyoubzadeh, Seyed Mehdi Ayyoubzadeh, Hoda Zahedi, Mahnaz Ahmadi, Sharareh R Niakan Kalhori. Originally published in JMIR Public Health and Surveillance (http//publichealth.jmir.org), 14.04.2020.BACKGROUND Physician burnout is on the rise, yet little is known about its relationship to anxiety. Mindfulness-based stress reduction has demonstrated decreases in anxiety, yet physicians have reported reluctance to engage in it due to significant time commitments. OBJECTIVE The aims of this study are to assess whether app-based mindfulness training can reduce anxiety in physicians and to explore if anxiety and burnout are correlated, thus leading to a reduction in both anxiety and burnout. METHODS This was a nonrandomized pilot study comprised of 34 physicians who worked in a large US health care network and reported having anxiety. The intervention was an app-based mindfulness program. The main outcome measure was anxiety, measured by the Generalized Anxiety Disorder-7 (GAD-7). The secondary outcome measures assessed burnout cynicism and emotional exhaustion items from the Maslach Burnout Inventory. RESULTS GAD-7 scores decreased significantly at posttreatment (1 month after treatment initiation, 48% reducealth.jmir.org), 01.04.2020.BACKGROUND Sleep disorders are a major public health issue. Nearly 1 in 2 people experience sleep disturbances during their lifetime, with a potential harmful impact on well-being and physical and mental health. selleck inhibitor OBJECTIVE The aim of this study was to better understand the clinical applications of wearable-based sleep monitoring; therefore, we conducted a review of the literature, including feasibility studies and clinical trials on this topic. METHODS We searched PubMed, PsycINFO, ScienceDirect, the Cochrane Library, Scopus, and the Web of Science through June 2019. We created the list of keywords based on 2 domains wearables and sleep. The primary selection criterion was the reporting of clinical trials using wearable devices for sleep recording in adults. RESULTS The initial search identified 645 articles; 19 articles meeting the inclusion criteria were included in the final analysis. In all, 4 categories of the selected articles appeared. Of the 19 studies in this review, 58 % (11/19) were comparison studies with the gold standard, 21% (4/19) were feasibility studies, 15% (3/19) were population comparison studies, and 5% (1/19) assessed the impact of sleep disorders in the clinic. The samples were heterogeneous in size, ranging from 1 to 15,839 patients. Our review shows that mobile-health (mHealth) wearable-based sleep monitoring is feasible. However, we identified some major limitations to the reliability of wearable-based monitoring methods compared with polysomnography. CONCLUSIONS This review showed that wearables provide acceptable sleep monitoring but with poor reliability. However, wearable mHealth devices appear to be promising tools for ecological monitoring. ©Elise Guillodo, Christophe Lemey, Mathieu Simonnet, Michel Walter, Enrique Baca-García, Vincent Masetti, Sorin Moga, Mark Larsen, HUGOPSY Network, Juliette Ropars, Sofian Berrouiguet. selleck inhibitor Originally published in JMIR mHealth and uHealth (http//mhealth.jmir.org), 01.04.2020.BACKGROUND The increasing volume of health-related social media activity, where users connect, collaborate, and engage, has increased the significance of analyzing how people use health-related social media. OBJECTIVE The aim of this study was to classify the content (eg, posts that share experiences and seek support) of users who write health-related social media posts and study the effect of user demographics on post content. METHODS We analyzed two different types of health-related social media (1) health-related online forums-WebMD and DailyStrength-and (2) general online social networks-Twitter and Google+. We identified several categories of post content and built classifiers to automatically detect these categories. These classifiers were used to study the distribution of categories for various demographic groups. RESULTS We achieved an accuracy of at least 84% and a balanced accuracy of at least 0.81 for half of the post content categories in our experiments. In addition, 70.04% (4741/6769) of posts by male WebMD users asked for advice, and male users' WebMD posts were more likely to ask for medical advice than female users' posts.
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