Efficacy and safety of acupuncture for post-COVID-19 depression: a protocol for systematic review and meta-analysis.

INTRODUCTION: Post-COVID-19 depression (PCD) is a possible sequela of COVID-19. Some doctors have used acupuncture to treat PCD, but no systematic review or meta-analysis has yet evaluated its efficacy and safety for the treatment of PCD. The aim of this systematic review is to assess the efficacy and safety of acupuncture therapy for PCD. METHODS AND ANALYSIS: Two reviewers will independently search the Cochrane Central Register of Controlled Trials (CENTRAL), Medline (PubMed), Excerpt Medica Database (EMBASE), China National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature Database (CBM), Chinese Scientific Journal Database (VIP) and Wan-Fang Database from inception to 24 January 2023. Study selection, data extraction and assessment of study quality will be independently performed by two reviewers. If a meta-analysis is appropriate, Review Manager V.5.3 will be used for data synthesis; otherwise, a descriptive analysis will be conducted. Data will be synthesised using a fixed-effects or random-effects model, according to the results of a heterogeneity test. The results will be presented as risk ratios with 95% CIs for dichotomous data, and weighted mean differences or standardised mean differences with 95% CIs for continuous data. ETHICS AND DISSEMINATION: The entire process used for this systematic review does not use private information, so ethical approval is not required. The results of this meta-analysis will be disseminated through publication in a peer-reviewed journal and/or conference presentations. PROSPERO REGISTRATION NUMBER: CRD42022379312.

Effects of COVID-19 home quarantine on pregnancy outcomes of patients with gestational diabetes mellitus: a retrospective cohort study.

OBJECTIVE: This study aimed to evaluate the effects of the home quarantine on pregnancy outcomes of gestational diabetes mellitus (GDM) patients during the COVID-19 outbreak. METHODS: The complete electronic medical records of patients with GDM with home quarantine history were collected and classified into the home quarantine group from 24 February 2020 to 24 November 2020. The same period of patients with GDM without home quarantine history were included in the control group from 2018 to 2019. The pregnant outcomes of the home quarantine and control groups were systematically compared, such as neonatal weight, head circumference, body length, one-minute Apgar score, fetal macrosomia, and pre-term delivery. RESULTS: A total of 1358 patients with GDM were included in the analysis, including 484 in 2018, 468 in 2019, and 406 in 2020. Patients with GDM with home quarantine in 2020 had higher glycemic levels and adverse pregnancy outcomes than in 2018 and 2019, including higher cesarean section rates, lower Apgar scores, and higher incidence of macrosomia and umbilical cord around the neck. More importantly, the second trimester of home quarantine had brought a broader impact on pregnant women and fetuses. CONCLUSION: Home quarantine has aggravated the condition of GDM pregnant women and brought more adverse pregnancy outcomes during the COVID-19 outbreak. Therefore, we suggested governments and hospitals strengthen lifestyle guidance, glucose management, and antenatal care for patients with GDM with home quarantine during public health emergencies.

Evaluation of RT-LAMP Assay for Rapid Detection of SARS-CoV-2.

OBJECTIVE: To evaluate the accuracy of the reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay for rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in community or primary-care settings. METHOD: We systematically searched the Web of Science, Embase, PubMed, and Cochrane Library databases. We conducted quality evaluation using ReviewManager software (version 5.0). We then used MetaDisc software (version 1.4) and Stata software (version 12.0) to build forest plots, along with a Deeks funnel plot and a bivariate boxplot for analysis. RESULT: Overall, the sensitivity, specificity, and diagnostic odds ratio were 0.79, 0.97, and 328.18, respectively. The sensitivity for the subgroup with RNA extraction appeared to be higher, at 0.88 (0.86-0.90), compared to the subgroup without RNA extraction, at 0.50 (0.45-0.55), with no significant difference in specificity. CONCLUSION: RT-LAMP assay exhibited high specificity regarding current SARS-CoV-2 infection. However, its overall sensitivity was relatively moderate. Extracting RNA was found to be beneficial in improving sensitivity.

Making better IS security investment decisions: discovering the cost of data breach announcements during the COVID-19 pandemic

PurposeStock price reactions have often been used to evaluate the cost of data breaches in the current information systems (IS) security literature. To further this line of research, this study examines the impact of data breaches on stock returns, information asymmetry and unsystematic firm risk in the context of COVID-19.Design/methodology/approachThis paper employs an event study methodology and examines data breach events released in public databases, spanning pre- and post-COVID settings. This study investigated 283 data breaches of the US publicly traded firms, and the economic cost was measured by cumulative abnormal returns (CARs), trading volume, bid-ask spread and unsystematic risk.FindingsThe authors observe that data breaches during the COVID pandemic make investors react more negatively to data breach announcements, as reflected in the significantly negative difference in CARs between breached firms before COVID and those after COVID. The findings also indicate that, after the disclosure of data breach incidents, information asymmetry is reduced to a lesser extent compared with that in the pre-COVID setting. The authors also find that data breach events lead to an increase in the unsystematic risk of breached companies in the pre-COVID era but no change in the post-COVID era.Originality/valueThis study is the first effort to examine the economic consequences of data breaches by investigating the effects in the form of trading activities and risk measurement in the COVID setting.

Lung image segmentation based on DRD U-Net and combined WGAN with Deep Neural Network.

PURPOSE: COVID-19 is a hot issue right now, and it's causing a huge number of infections in people, posing a grave threat to human life. Deep learning-based image diagnostic technology can effectively enhance the deficiencies of the current main detection method. This paper proposes a multi-classification model diagnosis based on segmentation and classification multi-task. METHOD: In the segmentation task, the end-to-end DRD U-Net model is used to segment the lung lesions to improve the ability of feature reuse and target segmentation. In the classification task, the model combined with WGAN and Deep Neural Network classifier is used to effectively solve the problem of multi-classification of COVID-19 images with small samples, to achieve the goal of effectively distinguishing COVID-19 patients, other pneumonia patients, and normal subjects. RESULTS: Experiments are carried out on common X-ray image and CT image data sets. The results display that in the segmentation task, the model is optimal in the key indicators of DSC and HD, and the error is increased by 0.33% and reduced by 3.57 mm compared with the original network U-Net. In the classification task, compared with SMOTE oversampling method, accuracy increased from 65.32% to 73.84%, F-measure increased from 67.65% to 74.65%, G-mean increased from 66.52% to 74.37%. At the same time, compared with other classical multi-task models, the results also have some advantages. CONCLUSION: This study provides new possibilities for COVID-19 image diagnosis methods, improves the accuracy of diagnosis, and hopes to provide substantial help for COVID-19 diagnosis.

Home Quarantine: A Double-Edged Sword During COVID-19 Pandemic for Hypertensive Disorders of Pregnancy and the Related Complications.

Object: COVID-19 pandemic and worldwide quarantine seriously affected the physical and mental health of the general public. Our study aimed to investigate the effects of the COVID-19 quarantine on pregnancy outcomes among pregnant women with hypertensive disorders of pregnancy (HDP). Methods: This single-center retrospective cohort study collected complete clinical data of HDP patients with a history of home quarantine in The First Affiliated Hospital of Chongqing Medical University (Chongqing, China) in 2020 as well as the patients without home quarantine in 2018 and 2019. Then, the maternal and neonatal outcomes of two subtypes of HDP, gestational hypertension (GH) and preeclampsia/eclampsia (PE/E), were analyzed over the three years. Results: The incidence of HDP increased from 0.84% in 2018 and 0.51% in 2019 to 2.30% in 2020. The data suggested that home quarantine was associated with higher gestational weight gain, obesity rates, blood pressure, and uric acid among the patients with HDP in 2020. Furthermore, HDP patients with a history of home quarantine may have worse neonatal outcomes, including lower newborn weight, shorter body length, lower Apgar score, and higher risk of fetal growth restriction. Conclusion: Our results suggested that COVID-19 quarantine may be a risk factor for poor pregnancy outcomes in HDP patients. Lifestyle guidance and antenatal care may be necessary for HDP patients with home quarantine in an epidemic outbreak.

Diet and Hygiene in Modulating Autoimmunity During the Pandemic Era.

The immune system is an efficiently toned machinery that discriminates between friends and foes for achieving both host defense and homeostasis. Deviation of immune recognition from foreign to self and/or long-lasting inflammatory responses results in the breakdown of tolerance. Meanwhile, educating the immune system and developing immunological memory are crucial for mounting defensive immune responses while protecting against autoimmunity. Still to elucidate is how diverse environmental factors could shape autoimmunity. The emergence of a world pandemic such as SARS-CoV-2 (COVID-19) not only threatens the more vulnerable individuals including those with autoimmune conditions but also promotes an unprecedented shift in people's dietary approaches while urging for extraordinary hygiene measures that likely contribute to the development or exacerbation of autoimmunity. Thus, there is an urgent need to understand how environmental factors modulate systemic autoimmunity to better mitigate the incidence and or severity of COVID-19 among the more vulnerable populations. Here, we discuss the effects of diet (macronutrients and micronutrients) and hygiene (the use of disinfectants) on autoimmunity with a focus on systemic lupus erythematosus.

Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning.

This paper investigates how information timeliness and richness affect public engagement using text data from China's largest social media platform during times of the COVID-19 pandemic. We utilize a similarity calculation method based on natural language processing (NLP) and text mining to evaluate three dimensions of information timeliness: retrospectiveness, immediateness, and prospectiveness. Public engagement is divided into breadth and depth. The empirical results show that information retrospectiveness is negatively associated with public engagement breadth but positively with depth. Both information immediateness and prospectiveness improved the breadth and depth of public engagement. Interestingly, information richness has a positive moderating effect on the relationships between information retrospectiveness, prospectiveness, and public engagement breadth but no significant effects on immediateness; meanwhile, it has a negative moderating effect on the relationship between retrospectiveness and depth but a positive effect on immediateness, prospectiveness. In the extension analysis, we constructed a supervised NLP model to identify and classify health emergency-related information (epidemic prevention and help-seeking) automatically. We find that public engagement differs in the two emergency-related information categories. The findings can promote a more responsive public health strategy that magnifies the transfer speed for critical information and mitigates the negative impacts of information uncertainty or false information.

Circulating ACE2-expressing extracellular vesicles block broad strains of SARS-CoV-2.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the pandemic of the coronavirus induced disease 2019 (COVID-19) with evolving variants of concern. It remains urgent to identify novel approaches against broad strains of SARS-CoV-2, which infect host cells via the entry receptor angiotensin-converting enzyme 2 (ACE2). Herein, we report an increase in circulating extracellular vesicles (EVs) that express ACE2 (evACE2) in plasma of COVID-19 patients, which levels are associated with severe pathogenesis. Importantly, evACE2 isolated from human plasma or cells neutralizes SARS-CoV-2 infection by competing with cellular ACE2. Compared to vesicle-free recombinant human ACE2 (rhACE2), evACE2 shows a 135-fold higher potency in blocking the binding of the viral spike protein RBD, and a 60- to 80-fold higher efficacy in preventing infections by both pseudotyped and authentic SARS-CoV-2. Consistently, evACE2 protects the hACE2 transgenic mice from SARS-CoV-2-induced lung injury and mortality. Furthermore, evACE2 inhibits the infection of SARS-CoV-2 variants (α, ß, and δ) with equal or higher potency than for the wildtype strain, supporting a broad-spectrum antiviral mechanism of evACE2 for therapeutic development to block the infection of existing and future coronaviruses that use the ACE2 receptor.

Using Mobile Phone Data for Emergency Management: a Systematic Literature Review.

Emergency management (EM) has always been a concern of people from all walks of life due to the devastating impacts emergencies can have. The global outbreak of COVID-19 in 2020 has pushed EM to the top topic. As mobile phones have become ubiquitous, many scholars have shown interest in using mobile phone data for EM. This paper presents a systematic literature review about the use of mobile phone data for EM that includes 65 related articles written between 2014 and 2019 from six electronic databases. Five themes in using mobile phone data for EM emerged from the reviewed articles, and a systematic framework is proposed to illustrate the current state of the research. This paper also discusses EM under COVID-19 pandemic and five future implications of the proposed framework to guide future work.

Characteristics of online medical care consultation for pregnant women during the COVID-19 outbreak: cross-sectional study.

OBJECTIVES: This study described the needs of pregnant women and the contents of online obstetric consultation in representative areas with various severity of the epidemic in China. DESIGN: This was a cross- sectional study. SETTING: Yue Yi Tong (YYT), a free online communication platform that allows pregnant women to consult professional obstetricians. PARTICIPANTS: All the pregnant women who used the YYT platform. INTERVENTION: From 10 to 23 February, we collected data on online obstetric consultations and participants' satisfaction through the YYT platform in the mild, moderate and severe epidemic areas which were defined according to the local confirmed cases. The primary outcomes were the reasons for online consultations by the severity of the epidemic. All the comparisons were performed using χ2 test. Statistical analysis was performed using SPSS V.24. RESULTS: A total of 2599 pregnant women participated in this study, of whom 448 (17.24%), 1332 (51.25%) and 819 (31.51%) were from the mild, moderate and severe epidemic areas, respectively. The distribution of the amount of online consultations was significantly different not only in different areas (p<0.001) but also in different trimesters (p<0.001). A total of 957 participants completed the satisfaction part of the survey. In this study, 77.95% of the participants used e-health for the first time, and 94.63% of the participants were completely or mostly satisfied with the online consultations. CONCLUSIONS: The distribution of the amount of online consultations was significantly different not only in different areas but also in different trimesters. In any trimester, the amount of consultations on the second category (obstetric care-seeking behaviour) was the highest in the severe epidemic areas. The needs for online consultations were substantial. In order to prevent irreversible obstetric adverse events, an appropriate antenatal care contingency plan with e-health services is highly recommended during the Public Health Emergency of International Concern.

Changes in physiology and immune system during pregnancy and coronavirus infection: A review.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the 3rd epidemic coronavirus after severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). Since December 2019, the outbreak of the Coronavirus Disease 2019 (COVID-19) caused by SARS-CoV-2 has aroused great attention around the world. Pregnant women and their fetuses have been concerned as a high-risk population. We explained why pregnant women are susceptible to coronavirus in terms of their adaptive changes in physiology and immune system during pregnancy, and described the associations between maternal clinical symptoms, perinatal outcomes and coronavirus infections.

How do agribusinesses thrive through complexity? The pivotal role of e-commerce capability and business agility.

The recent COVID-19 pandemic has clearly shown how agricultural foods and e-commerce initiatives are critical for many organizations, regions, and countries worldwide. Despite this vital importance, prior IS research on the business value of IT has not paid enough attention to the potential specificities of the agribusinesses. This study examines the impact of e-commerce capability on business agility in agribusinesses. Using a sample of Chinese agriculture firms, we find that: 1) The e-commerce capability of agribusinesses enables two types of business agility: market capitalizing agility and operational adjustment agility, and 2) while environmental complexity positively moderates the effects of e-commerce capability on the market capitalizing agility and operational adjustment agility, environmental dynamism does not. This study contributes to the IS research on the business value of IT by providing an eloquent theoretical explanation and empirical evidence on how e-commerce capability help agricultural firms to thrive through complexity by enabling market capitalizing agility (strategic focus) and operational adjustment agility (operational focus).

Management of a delivery suite during the COVID-19 epidemic.

BACKGROUND: Since the first report of the new coronavirus (COVID-19) infection in December of 2019, it has become rapidly prevalent and been declared as a Public Health Emergency of International Concern by the World Health Organization. There are quite a few cases reported involving delivery with COVID-19 infection, but little valuable suggestion was provided about what healthcare providers of obstetrics and neonatology should do in their clinic practice for unknown status or presumed negative women. Here, we summarized the current practice of delivery management in China that successfully prevented rapid increase in adverse pregnancy outcomes and nosocomial infection in departments of obstetrics and neonatology during the pandemic of COVID-19.