Meta-analysis and systematic review: The administration of glucocorticoids, as a line of treatment for COVID-19 patients, could result in lowering mortality

The aim of this meta-analysis was to investigate the association between the use of glucocorticoids and the mortality of covid-19 patients. Trials were identified through a comprehensive systematic search of four medical databases including PubMed, Cochrane Library, Web of Science, and Embase. We also searched for relevant papers using the Google search engine and major Preprint platforms including Medrix, bioRxiv, and SSRN. A meta-analysis was performed on the pooled results of these studies. Fourteen studies were enrolled. Five studies were Chinese, and nine were foreign. In the foreign studies, we found use of steroids was associated with a decrease in mortality (RR, 0.81;95% CI 0.68-0.97;p = 0.02) whilst in the Chinese studies the use of steroids was associated with an increase in mortality (RR, 1.70;95% CI 1.10-2.63;p = 0.02). The foreign studies included high-dose and medium-dose groups. The medium-dose glucocorticoid group (0.5 mg/kg/d <= Prednisone <= 1.0 mg/kg/d) showed an association with decreased mortality of covid-19 patients (RR 0.86;95% CI 0.75-1.00;p = 0.05). We also found an association with decreasing mortality of covid-19 patients (RR 0.65;95% CI 0.43-0.98;heterogeneity p = 0.04) in patients treated for <= five days. In summary, this meta-analysis demonstrated that the use of a medium dose of glucocorticoids for a short time is likely to decrease mortality, although this needs clinical confirmation.Copyright © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Intelligent Monitoring Model for Aggregated Infection Risk 计Against the Background of COVID-19 Epidemic

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.

Real-Time Face Mask Detector Using Deep Learning

Coronavirus Disease 2019, also known as Covid-19, is an infectious respiratory disease that was first identified in December 2019 in Wuhan, China. Covid-19 then caused a global pandemic in 2020, which is still ongoing (as of Feb 2022). The Covid-19 virus is known to be transmitted through small droplets exhaled by an infected person. Wearing a face mask is one of the most effective ways to reduce the spread of the Covid-19 virus. Face masks can prevent direct contact of the nose and mouth with the external environment, preventing inhalation of small droplets and thus lowering the risk of infection. Because face masks are so important in everyday life, there has been a lot of research done on them. However, most research has focused on detecting the presence of a face mask, rather than how the face mask is worn or the type of face mask. The primary goal of this project would be to create a face mask detector using deep learning with additional features and to investigate the possibilities of additional features and system enhancements. © 2022 IEEE.

REAL WORLD DATA SUPPORTS POST-PANDEMIC CONTINUATION OF TELEMEDICINE SURVEILLANCE VISITS FOLLOWING RESECTION OF COLORECTAL LIVER METASTASES

INTRODUCTION The purpose of surveillance after resection of colorectal liver metastases (CLM) is to detect and treat recurrence using axial imaging, biomarker measurement, and a history/physical examination. In response to COVID-19 pandemic, telemedicine was used as a risk mitigation strategy to replace in-person visits, including for cancer surveillance. The objective of the study was to measure the uptake of telemedicine for cancer surveillance and outcomes following telemedicine surveillance after resection of CLM. METHODS Data from a prospective database was combined with real world data obtained from electronic health records using a cloud-based, data integration tool (Palantir Foundry) to identify patients in active surveillance following first surgical resection for CLM between April 2017 and April 2021. Telemedicine surveillance visit was defined as a follow-up visit >90 days following surgery using video or telephone. Recurrence was defined as detection of a new lesion. Bivariate statistical testing was performed using Student's t-test or chi-squared test. Retrospective chart review was used to validate identification of recurrence using the Foundry platform (100% interobserver agreement). RESULTS A total of 1,057 surveillance visits (306 patients) met our inclusion criteria. Prior to April 2020, 0% (0/686) visits utilized telemedicine. After April 2020, an average of 47.3% of visits per month utilized telemedicine (range 33.0 – 69.0%). The overall rate of identifying a recurrence during surveillance visit was 18.1% (191/1,057). There was no difference when comparing detection of recurrence using in-person (17.6%, 154/872) versus telemedicine visits (20.0%, 37/185, P=.371). The management of recurrence did not differ whether it was identified with an in-person or telemedicine visit;surgery, 36 (23%) vs. 10 (27%);ablation, 26 (17%) vs. 8 (22%);systemic therapy, 83 (54%) vs. 16 (43%);other, 9 (6%) vs. 3 (8%), respectively (P=.699). CONCLUSION Telemedicine was used in almost half of surveillance visits for CLM during the COVID- 19 pandemic. Detection and treatment of recurrence was similar for both telemedicine and in-person visits. Telemedicine-based follow-up is a safe and effective approach for surveillance after resection of CLM, supporting continued utilization beyond the pandemic.

Does Temporary Mortgage Assistance for Unemployed Homeowners Reduce Longer-Term Mortgage Default? An Analysis of the Hardest Hit Fund Program

Economic crises like the Great Recession and the COVID pandemic prompt government intervention to stabilize homeowners and housing markets. During the Great Recession, the primary intervention was permanent loan modifications, with mixed evidence of success. The COVID pandemic spurred a more targeted but temporary intervention-mortgage payment relief for unemployed homeowners. Little is known about the long-term effectiveness of temporary mortgage assistance for homeowner outcomes. This paper leverages data on the U.S. Department of the Treasury's Hardest Hit Fund (HHF) program to analyze the longer-term effects of temporary mortgage payment subsidies on mortgage default. Our first research design exploits the fact that some states were not eligible to offer an HHF program and that certain Metropolitan Statistical Areas (MSAs) encompass jurisdictions in both HHF and non-HHF states. In a second research design, we model selection into the HHF program directly, exploiting lender variation in program participation as an instrument. Our results indicate that receipt of HHF assistance leads to a 40 percent reduction in the probability of mortgage default and foreclosure through four years post assistance. We estimate heterogeneous effects for different at-risk populations and discuss implications for policy.

Cos-CBDC: Design and Implementation of CBDC on Cosmos Blockchain

With the advent of e-commerce and electronic payment systems, the use of paper currency is decreasing. Therefore, it is sufficiently predictable that most paper currencies will disappear and digital currencies will become the mainstream. This phenomenon is further accelerated by advances in blockchain technology and COVID-19. This is why the Central Bank Digital Currency (CBDC) has recently begun to attract attention. Currently, there are studies on CBDC with a blockchain-based distributed ledger. In this paper, we propose Cosmos blockchain based CBDC (Cos-CBDC) that enables communication between blockchains using Inter-Blockchain Communication (IBC) protocol to ensure interoperability. We not only analyze the requirements of Cos-CBDC but also design and implement it using Cosmos-SDK. Furthermore, we propose a Group Key Management system in Cos-CBDC. It can give different user privileges, and privacy-preserving is possible in the key generation process. © 2021 IEICE.

Simulation Clinical Practice Education That Combines Virtual Reality Effects on Clinical Thinking Disposition and Clinical Practice Performance

In the face of a difficult situation in which effective clinical practice is difficult due to the recent COVID-19, we intend to grasp the effect of clinical practice education using virtual reality in which are fused in practical education. 69 The subjects of this study were a total of 59 randomly expressed nursing students from S university who used the vSim of nursing program and university B nursing students who did not use the vSim of nursing program as an alternative practice for clinical practice. The data analysis method used SPSS WIN 23.0, and the difference in scores of dependent variables between groups was analyzed with an independent t-test. As a result of this study, the experimental group was 50.8% (n=30), the control group was 49.2% (n=29) female students, and the major satisfaction was 69.5% (n=41), showing that most of the majors were satisfied. As for interpersonal relationships, 49.2% (n=29) of the relationship with clinical nurses was the most difficult, and 40.7% (n=24) of the median ranks and 59.3% (n=35) of the top ranks were found. It was found that both the experimental group and the control group were homogeneous in the homogeneity test. Critical thinking disposition was 3.63±.48, clinical practice ability was 3.97±.62, As a result of hypothesis verification, students who conducted simulation clinical practice education using virtual reality showed no significant difference between groups in critical thinking tendency and clinical practice performance ability than students who did not conduct education (p=.425) in both hypotheses 1 and 2 Was dismissed. If this study is complemented by vSim’s case-based program development for target audiences in the Korean clinical field, improvement of the learning environment (language et al), It is thought to give a positive effect. © 2021