RESUMEN
The Interdisciplinary Cardiac Arrest Research Review (ICARE) group was formed in 2018 to conduct an annual search of peer-reviewed literature relevant to cardiac arrest. Now in its fourth year, the goals of this review are to highlight annual updates on clinically relevant and impactful clinical and population-level studies in the interdisciplinary world of cardiac arrest research from 2021. To achieve these goals, a search of PubMed using keywords related to clinical research in cardiac arrest was conducted. Titles and s were screened for relevance and sorted into seven categories: Epidemiology & Public Health;Prehospital Resuscitation;In-Hospital Resuscitation & Post-Arrest Care;Prognostication & Outcomes;Pediatrics;Interdisciplinary Guidelines;and Coronavirus disease 2019. Screened manuscripts underwent standardized scoring of methodological quality and impact by reviewer teams lead by a subject matter expert editor. Articles scoring higher than 99t h per-centile by category were selected for full critique. Systematic differences between editors' and reviewers' scores were assessed using Wilcoxon signed-rank test. A total of 4,730 articles were identified on initial search;of these, 1,677 were scored after screening for relevance and deduplication. Compared to the 2020 ICARE review, this represents a relative increase of 32% and 63%, respectively. Ultimately, 44 articles underwent full critique. The leading category was In-Hospital Resuscitation, representing 41% of fully reviewed articles, followed by Pre-hospital Resuscitation (20%) and Interdisciplinary Guidelines (16%). In conclusion, several clinically relevant studies in 2021 have added to the evidence base for the management of cardiac arrest patients including implementation and incorporation of resuscitation systems, technology, and quality improvement programs to improve resuscitation. © 2023 Tehran University of Medical Sciences.
RESUMEN
To stop the COVID-19 epidemic from spreading among their populations, several countries have implemented lockdowns. Students are being forced to stay at home during these lockdowns, which is causing them to use mobile phones, social media, and other digital technologies more frequently than ever. Their poor utilization of these digital tools may be detrimental to their emotional and mental health. In this study, we implement an Artificial Intelligence (AI) approach named Hierarchy-based K-Means Clustering (HKMC) algorithm to group individuals with comparable Twitter consumption habits to detect addictive Twitter activity during the epidemic. The effectiveness of the suggested HKMC is evaluated in terms of accuracy, precision, recall, and f1-score in respect to the association between students' mental health and mobile phone dependency. Additionally, this study offers a comparative examination of both the suggested and existing procedures. © 2022 The authors.
RESUMEN
The Corona Virus Disease 2019（COVID-19）epidemic is a serious threat to people’s lives.Supervision of the density of clustered people and wearing of masks is key to controlling the virus.Public places are characterized by a dense flow of people and high mobility.Manual monitoring can easily increase the risk of infection，and existing mask detection algorithms based on deep learning suffer from the limitation of having a single function and can be applied to only a single type of scenes；as such，they cannot achieve multi-category detection across multiple scenes. Furthermore，their accuracy needs to be improved. The Cascade-Attention R-CNN target detection algorithm is proposed for realizing the automatic detection of aggregations in areas，pedestrians，and face masks. Aiming to solve the problem that the target scale changes too significantly during the task，a high-precision two-stage Cascade R-CNN target detection algorithm is selected as the basic detection framework. By designing multiple cascaded candidate classification regression networks and adding a spatial attention mechanism，we highlight the important features of the candidate region features and suppress noise features to improve the detection accuracy. Based on this，an intelligent monitoring model for aggregated infection risk is constructed，and the infection risk level is determined by combining the outputs of the proposed algorithm. The experimental results show that the model has high accuracy and robustness for multi-category target images with different scenes and perspectives. The average accuracy of the Cascade Attention R-CNN algorithm reaches 89.4%， which is 2.6 percentage points higher than that of the original Cascade R-CNN algorithm，and 10.1 and 8.4 percentage points higher than those of the classic two-stage target detection algorithm，Faster R-CNN and the single-stage target detection framework，RetinaNet，respectively. © 2022, Editorial Office of Computer Engineering. All rights reserved.
RESUMEN
Background: Over the last decade, social media analysis tools have been used to monitor public sentiment and communication methods for public health emergencies such as the Ebola and Zika epidemics. Research articles have indicated that many outbreaks and pandemics could have been promptly controlled if experts considered social media data. With the World Health Organization (WHO) pandemic statement and various governments government action on the disease, various sentiments regarding coronavirus disease 2019 (COVID-19) have spread across the world. Therefore, sentiment analyses in studying pandemics, such as COVID-19, are important based on recent events. Methods: The Term Frequency-Inverse Document Frequency (TF-IDF) method was used to extract keywords from the 850,083 content of Weibo from January 24, 2020, to March 31, 2020. Then the Latent Dirichlet Allocation (LDA) was used to perform topic analysis on the keywords. Finally, the fuzzy-c-means method was used to divide the content of Weibo into seven categories of emotions: fear, happiness, disgust, surprise, sadness, anger, and good. And the changes in emotion were tracked over time. Results: The results indicated that people showed “surprise” overall (55.89%);however, with time, the “surprise” decreased. As the knowledge regarding the COVID-19 increased, the “surprise” of the citizens decreased (from 59.95% to 46.58%). Citizens’ feelings of “fear” and “good” increased as the number of deaths associated with COVID-19 increased (“fear”: from 15.42% to 20.95% “good”: 10.31% to 18.89%). As the number of infections was suppressed, the feelings of “fear” and “good” diminished (“fear”: from 20.95% to 15.79% “good”: from 18.89% to 8.46%). Conclusions: The findings of this study indicate that people’s feelings were analyzed regarding the COVID-19 pandemic in three stages over time. In the beginning, people’s emotions were primarily “surprised”;however after the outbreak, people’s “surprise” decreased with increasing knowledge. At the end of the phase, I of the COVID-19 pandemic, people’s “fear” and “good” feelings were diminished as the epidemic was suppressed. People’s interest shifted from China to other countries and their concern about the situation in other countries. © Journal of Medical Artificial Intelligence. All rights reserved.
RESUMEN
The pandemic has brought teachers, young children and their parents lots of uncertainties and challenges in adapting to new teaching and learning environment. Compared with other professionals, teachers might face more challenges in transiting from using a traditional face-to-face teaching method to an online or blended teaching and learning model. This study aimed to explore Chinese primary school English language and literacy teachers' teaching preparation, in particular, their knowledge with regard to incorporating online technologies in learning and teaching. Participants in this project were English language and literacy teachers in central China. The study used a survey with a Likert scale to examine primary school English language and literacy teachers' perceptions of using digital technology in teaching preparation, students' competence development, assessment and professional development (PD) for online teaching. The results confirmed differing perspectives between young teachers and their elder colleagues on the value of using digital technology for online teaching and their PD programmes. A call for PD programmes is also proposed and discussed.
RESUMEN
Computed tomography (CT) of COVID-19 manifests a relatively global effect through the whole lungs, like peripheral ground glass, consolidation, reticular pattern, nodules etc. This characteristic effect renders the difficulties in differentiating COVID-19 from the normal body or other lung diseases by CT. This work presents a novel method to relieve the difficulties by reducing the global effect through the 3D whole lung volume into 2D-like domain. The hypothesis is that the lung tissue shares the similar anatomic structure within a small lung sub-volume for normal subjects. Therefore, the anatomic land-markers along the z-axis, denoted as Lung Marks are used to eliminate axial variable. Our experiments indicated that 30 Lung Marks are sufficient to eliminate the axial variable. The method computes texture measures from each 2D-like volumetric data and maps the measures on to the corresponding Lung Mark, resulting in a profile along the z-axis. The difference of the profiles between two different abnormalities is the proposed sensitive merit to differentiate COVID-19 cases from others in CT images. 48 COVID-19 cases and 48 normal screening cases were used to test the effectiveness of the proposed sensitive merit. Intensity and gradient based texture descriptors were computed from each axial cross image at the corresponding Lung Mark along the z-axis. Euclidean, Jaccard and Dice distances are calculated to generate the profiles of the proposed sensitive merit. Consistent results are observed across texture descriptor types and distance types in the texture measure between the normal and COVID-19 subjects. Uneven Profiles demonstrate the variation along the z-axis. With Lung Mark, the variation of texture descriptor has been reduced prominently. The Gradient based descriptor is more sensitive. Individual Haralick features analysis shows the 2nd and 10th dimensions are most distinguishable. © 2020 IEEE
RESUMEN
The announcement of National Health Commission on January 20, 2020 (No.1 of 2020) has included novel coronavirus pneumonia into the B class infectious diseases according to the law of the People's Republic of China on the prevention and control of infectious diseases, and has been managed as A class infectious diseases. People's governments at all levels and health administration departments have been paying high attention to it. With the alleviation of COVID-19 nationwide, dental clinics gradually resume to work. The main transmission routes of COVID-19 are respiratory droplets and contact transmission, hence oral radiological examination is kind of a high-risk operation. Standardized radiologic process is of great significance to reduce the risk of COVID-19 transmission. In accordance with the national and Shanghai epidemic prevention requirements, and in combination with the actual situation of various medical institutions, Oral and Maxillofacial Radiology Committee of Shanghai Stomatological Association formulated the expert consensus on standardized prevention and control of COVID-19 for clinical reference. This recommendation will be updated according to the situation of epidemic prevention and control in China and the new relevant diagnosis and treatment plans.