Application of remote fetal heart rate monitoring via internet in late pregnancy during the COVID-19 pandemic.

BACKGROUND: Internet-related technologies have rapidly developed and started to impact the traditional medical practices, which combined wireless communication technology as well as "cloud service" technology with electronic fetal heart monitoring have become a mainstream tendency. OBJECTIVE: To investigate the clinical application value of remote fetal heart rate monitoring mode (RFHRM) on late pregnancy during the coronavirus disease (COVID-19) pandemic. METHODS: From March 2021 to February 2022, we recruited 800 cases of pregnant women received prenatal examination at the Anhui Province Maternity and Child Healthcare Hospital. These pregnant women were randomly divided into two groups: the control group (n= 400), which was given traditional management, and the observation group (n= 400), which received remote monitoring technology on this basis. The two groups were compared with neonatal asphyxia, pregnancy outcomes, Edinburgh postnatal depression scale scores (EPDS), prenatal examination expenses and total time consumption. RESULTS: There were no statistically significant differences between the groups in pregnancy outcome and neonatal outcome (P> 0.05). However, total EPDS score of 12.5% pregnant women in the observation group were higher than 12. The TPE group had significantly higher mean EPDS scores compared with the RFHRM group (7.79 ± 3.58 vs 5.10 ± 3.07; P< 0.05). The results showed a significant difference in maternity expenses (2949.83 ± 456.07 vs 2455.37 ± 506.67; P< 0.05) and total time consumption (42.81 ± 7.60 vs 20.43 ± 4.16; P< 0.05) between the groups. CONCLUSION: Remote fetal heart rate monitoring via internet served as an innovative, acceptable, safe and effective reduced-frequency prenatal examination model without affecting the outcome of perinatology of pregnant women with different risk factors.

Knowledge Graph: Applications in Tracing the Source of Large-Scale Outbreak - Beijing Municipality, China, 2020-2021.

Introduction: Tracing transmission paths and identifying infection sources have been effective in curbing the spread of coronavirus disease 2019 (COVID-19). However, when facing a large-scale outbreak, this is extremely time-consuming and labor-intensive, and resources for infection source tracing become limited. In this study, we aimed to use knowledge graph (KG) technology to automatically infer transmission paths and infection sources. Methods: We constructed a KG model to automatically extract epidemiological information and contact relationships from case reports. We then used an inference engine to identify transmission paths and infection sources. To test the model's performance, we used data from two COVID-19 outbreaks in Beijing. Results: The KG model performed well for both outbreaks. In the first outbreak, 20 infection relationships were identified manually, while 42 relationships were determined using the KG model. In the second outbreak, 32 relationships were identified manually and 31 relationships were determined using the KG model. All discrepancies and omissions were reasonable. Discussion: The KG model is a promising tool for predicting and controlling future COVID-19 epidemic waves and other infectious disease pandemics. By automatically inferring the source of infection, limited resources can be used efficiently to detect potential risks, allowing for rapid outbreak control.

Analysis of Taxi Travels during an Epidemic Period Using System Dynamics Method

This paper explores the factors influencing taxi travel in the context of COVID-19 from both demand and supply sides and provides a quantitative comparison of taxi travel characteristics and taxi industry operations before and after the epidemic. A model was established using system dynamics to simulate a taxi travel system, which was used to analyze the changes in demand and supply of taxi travel under scenarios such as closedowns, travel restrictions, etc. The analysis is based on a typical middle-sized city in China, Ningbo in Zhejiang Province, revealing factors leading to the significant drop in the amount of taxi travel due to the epidemic. The study can provide insights into impacts of public (or similar anomalous or catastrophic) events on taxi travel systems and could be useful for urban transport planning and management.

Occurrence of COVID-19 Symptoms During SARS-CoV-2 Infection Defines Waning of Humoral Immunity.

Approximately half of the SARS-CoV-2 infections occur without apparent symptoms, raising questions regarding long-term humoral immunity in asymptomatic individuals. Plasma levels of immunoglobulin G (IgG) and M (IgM) against the viral spike or nucleoprotein were determined for 25,091 individuals enrolled in a surveillance program in Wuhan, China. We compared 405 asymptomatic individuals who mounted a detectable antibody response with 459 symptomatic COVID-19 patients. The well-defined duration of the SARS-CoV-2 endemic in Wuhan allowed a side-by-side comparison of antibody responses following symptomatic and asymptomatic infections without subsequent antigen re-exposure. IgM responses rapidly declined in both groups. However, both the prevalence and durability of IgG responses and neutralizing capacities correlated positively with symptoms. Regardless of sex, age, and body weight, asymptomatic individuals lost their SARS-CoV-2-specific IgG antibodies more often and rapidly than symptomatic patients did. These findings have important implications for immunity and favour immunization programs including individuals after asymptomatic infections.

Study on Factors of People's Wearing Masks Based on Two Online Surveys: Cross-Sectional Evidence from China.

(1) Background: To analyze factors of people's wearing masks based on two online surveys, and to explore whether living area factor or quarantine status could have an impact on mask-wearing. (2) Methods: Chi-square test and logistic regression analysis were used to explore the influence of different factors on people's behavior of mask-wearing in the present study. R3.6.2 was used for data cleaning, SPSS 26.0 was used to conduct corresponding statistical analysis, and a two-sided p-value less than 0.05 was considered to be statistically significant. (3) Results: According to our study, the proportion of participants who wore face masks was higher than 90.0% in both surveys. Single factor analysis showed that the proportion of people wearing face masks raised with the increase of people's education, age, and monthly income (Yuan) in both surveys. People who lived in rural areas were less likely to wear masks. Mask-wearing rate was lower in the isolated individuals than in the non-isolated ones. (4) Conclusions: Masks-wearing is one of the effective measures for COVID-19 pandemic prevention and control. After the Chinese government made wearing face masks mandatory in public places, most residents in China had developed the habit of wearing masks, contributing to the high rate of masks-wearing in China. However, people in rural areas need to raise their awareness of wearing masks. Meanwhile, the mask-wearing of the isolated individuals needs to be enhanced.

Epidemiological characteristics of clustering outbreaks of 51 cases of COVID-19 in

Objective: To analyze the occurrence characteristics and epidemic regularity of a new epidemic situation of COVID-19, and we provide scientific basis for formulating strategies and measures for epidemic prevention and control.

Rapid Detection of Anti-SARS-CoV-2 IgM and IgG Using a Selenium Nanoparticle-based Lateral Flow Immunoassay (preprint)

Background: Coronavirus disease 2019 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 is highly transmissible. Early and rapid testing is necessary to effectively prevent and control the outbreak. Detection of SARS-CoV-2 antibodies with lateral flow immunoassay can achieve this goal. Antibody detection is especially effective for the detection of asymptomatic infection.Methods: In this study, SARS-CoV-2 nucleoprotein was expressed by E. coli and purified by affinity chromatography. We used the highly stable and sensitive selenium nanoparticle as the labeling probe coupled with the SARS-CoV-2 nucleoprotein to prepare a new SARS-CoV-2 antibody (IgM and IgG) detection kit. The sensitivity and specificity of the kit were verified by plasma of COVID-19 patients and health persons. Separate detection of IgM and IgG, such as in this assay, was performed in order to reduce mutual interference and improve the accuracy of the test results.Results: The SARS-CoV-2 nucleoprotein was purified on a nickel column, and the final purity was greater than 90%. The sensitivity of the kit was 94.74% and the specificity was 95.12% by 41 negative plasma samples and 19 positive plasma samples detection.Conclusions: The assay kit does not require any special device for reading the results and the readout is a simple color change that can be evaluated with the naked eye. This kit is suitable for rapid and real-time detection of the SARS-CoV-2 antibody.