Factors Associated with Primary Care Provider's Job Satisfaction and Organizational Commitment in China: A Machine Learning-Based Random Forest Analysis.

The objective of the study is to explore the factors that influence the job satisfaction and organizational commitment of primary care providers in China, with a focus on the impact of the COVID-19 pandemic and the rescission of restriction policies. We utilized the 20-item Minnesota Satisfaction Questionnaire (MSQ) and the 25-item organizational commitment survey to assess job satisfaction and organizational commitment. In total, 435 valid responses were included in our analysis. The average scores for job satisfaction and organizational commitment were 80.6 and 90.8. After a two-step tuning process, we built random forest models by machine learning. The results show income change, working years, working years in the current institute, and age were the four most important features associated with job satisfaction, organizational commitment, and most of their dimensions. The number of professional fields engaged, gender, job status, and types of endowment insurance were least associated. During pandemic time, income-related factors remain a core concern for primary care providers, whereas job security may lose its importance. These findings suggest that financial bonuses may be an effective way to boost morale, and age-specific motivation plans may be necessary.

Cost-effectiveness of COVID-19 vaccination: A systematic review.

OBJECTIVE: The COVID-19 vaccination strategy has been widely used to protect population health worldwide. This study aims to summarize the cost-effectiveness evidence of economic evaluation of COVID-19 vaccination strategies to provide evidence supporting the usage of COVID-19 vaccination, especially where the supply of COVID-19 vaccine is limited. METHODS: A systematic literature review was performed by searching both English and Chinese databases, including PubMed, Embase, Science Direct, Web of Science, Medline, Scopus, and CNKI. Articles published from January 1, 2020 to August 1, 2022 (PROSPERO registration number: CRD42022355442). RESULTS: Of the 1035 papers identified, a total of 28 English studies that met the preset criteria were included. COVID-19 vaccination and booster vaccination were cost-effective or cost-saving regardless of the vaccine type; vaccine efficacy, vaccine price, vaccine supply or prioritization, and vaccination pace were the influential factors of cost-effectiveness among different population groups. When supply is adequate, mass vaccination should be encouraged, while when supply is inadequate, prioritizing the high risk and the elderly is more cost-effective. CONCLUSIONS: COVID-19 vaccination strategies are economically favorable in a wide range of countries and population groups, and further research on suitable strategies for booster COVID-19 vaccination is needed.

Long-term exposure to air pollution and COVID-19 severity: A cohort study in Greater Manchester, United Kingdom.

Exposure to outdoor air pollution may affect incidence and severity of coronavirus disease 2019 (COVID-19). In this retrospective cohort based on patient records from the Greater Manchester Care Records, all first COVID-19 cases diagnosed between March 1, 2020 and May 31, 2022 were followed until COVID-19 related hospitalization or death within 28 days. Long-term exposure was estimated using mean annual concentrations of particulate matter with diameter ≤2.5 µm (PM2.5), ≤10 µm (PM10), nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2) and benzene (C6H6) in 2019 using a validated air pollution model developed by the Department for Environment, Food and Rural Affairs (DEFRA). The association of long-term exposure to air pollution with COVID-19 hospitalization and mortality were estimated using multivariate logistic regression models after adjusting for potential individual, temporal and spatial confounders. Significant positive associations were observed between PM2.5, PM10, NO2, SO2, benzene and COVID-19 hospital admissions with odds ratios (95% Confidence Intervals [CI]) of 1.27 (1.25-1.30), 1.15 (1.13-1.17), 1.12 (1.10-1.14), 1.16 (1.14-1.18), and 1.39 (1.36-1.42), (per interquartile range [IQR]), respectively. Significant positive associations were also observed between PM2.5, PM10, SO2, or benzene and COVID-19 mortality with odds ratios (95% CI) of 1.39 (1.31-1.48), 1.23 (1.17-1.30), 1.18 (1.12-1.24), and 1.62 (1.52-1.72), per IQR, respectively. Individuals who were older, overweight or obese, current smokers, or had underlying comorbidities showed greater associations between all pollutants of interest and hospital admission, compared to the corresponding groups. Long-term exposure to air pollution is associated with developing severe COVID-19 after a positive SARS-CoV-2 infection, resulting in hospitalization or death.

Effectiveness of SARS-CoV-2-inactivated vaccine and the correlation to neutralizing antibodies: A test-negative case-control study.

Severe acute respiratory syndrome coronavirus 2 breakthrough infection in highly vaccinated populations raises study on the effectiveness for inactivated vaccine, including effectiveness of the vaccine dose, the continuance of effectiveness, the effectiveness against severe/critical coronavirus disease 2019 and against secondary attacks. A population of 10 870 close contacts were investigated in a Delta variant's epidemic. The effectiveness of vaccination was estimated in a test-negative case-control study. In addition, serum was used to detect neutralizing antibodies, to explore their correlation to effectiveness. The vaccine effectiveness (VE) values were estimated for populations aged 12 years or older. The overall adjusted VE was 56.2% and a two-dose vaccine was more effective than a one-dose vaccine (56.7% vs. 43.8%). In addition, the population that got the second dose vaccine within 2 months showed higher VE than the population vaccinated for longer than 2 months (61.5% vs. 52.3%). Among the population who vaccinated 2 doses or within 2 months, a higher level of neutralizing antibodies was observed. For infected cases, vaccinated populations showed lower rates of transmission (2.63% vs. 4.36%). Further, those vaccinated cases, who were not found causing transmission, had a higher level of antibodies. The study provided a full view of the effectiveness of inactivated vaccines in a real-world setting. The time-related VE against infection and lower transmission of breakthrough vaccinated cases were observed, which may indicate that a necessity of a booster vaccine to maintain the effectiveness and high level of neutralizing antibody.

Evaluating the value of anti-SARS-CoV-2 antibody detection and neutralizing responses with euvirus: A population of 10776 close contacts in the epidemic of Fujian.

BACKGROUND: Nucleic acid detection represents limitations due to its false-negative rate and technical complexity in the COVID-19 pandemic. Anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody tests are widely spread all over the world presently. However, there is no report on the effectiveness of anti-SARS-CoV-2 antibody testing methods in China. METHODS: We gathered 10776 serum samples from close contacts of the SARS-CoV-2 infections in Fujian of China and used 2 chemiluminescence immunoassays (Wantai Bio., Yahuilong Bio.) and 2 lateral flow immunoassays (Lizhu Bio. and Dongfang Bio.) to perform the anti-SARS-CoV-2 antibody tests in China. RESULTS: The 4 antibody tests have great diagnostic value for infected or uninfected, especially in the neutralizing antibodies tests, the AUC can reach 0.939 (Wantai Bio.) and 0.916 (Yahuilong Bio.). Furthermore, we used pseudoviruses and euvirus neutralization assay to validate the effectiveness of these antibody test, the results of pseudoviruses neutralization assay or euvirus neutralization assay shows a considerable correlation with the 4 antibody detection respectively, particularly in euvirus neutralization assay, neutralizing antibodies detected by Wantai Bio. or Yahuilong Bio., the correlation can get the level of 0.93 or 0.82. CONCLUSIONS: The findings of this study demonstrate that the detections of antibodies have profound value in the diagnosis of COVID-19.

Health Services Utilization in China during the COVID-19 Pandemic: Results from a Large-Scale Online Survey.

Timely access to essential health services is a concern as COVID-19 continues. This study aimed to investigate health services utilization during the first wave of the pandemic in China. A cross-sectional online survey was conducted using a self-administrated questionnaire in March 2020. Descriptive statistics and logistic regression were used for data analysis. A total of 4744 respondents were included, with 52.00% reporting affected services utilization. Clinical testing (68.14%) and drug purchase (49.61%) were the most affected types. Higher education level, being married, chronic disease, frequently visiting a provincial medical institution, spending more time on pandemic-related information, perception of high-risk of infection, perception of large health impact of the pandemic, and anxiety/depression were significant predictors for reporting affected services utilization. For the 431 chronic disease respondents, 62.18% reported interruption, especially for drug purchase (58.58%). Affected health services utilization was reported during the first wave of the pandemic in China, especially for those with higher education level, chronic diseases, and COVID-19 related concerns. Enhancing primary healthcare, use of telehealth, extended prescription, and public communication were countermeasures undertaken by China during the rapid rise period. As COVID-19 progresses, the changing disease characteristics, adapted health system, along with enhanced public awareness/knowledge should be considered for the evolution of health services utilization, and further investigation is needed.

EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network

Depression is an undetectable mental disease. Most of the patients with depressive symptoms do not know that they are suffering from depression. Since the novel Coronavirus pandemic 2019, the number of patients with depression has increased rapidly. There are two kinds of traditional depression diagnosis. One is that professional psychiatrists make diagnosis results for patients, but it is not conducive to large-scale depression detection. Another is to use electroencephalography (EEG) to record neuronal activity. Then, the features of the EEG are extracted using manual or traditional machine learning methods to diagnose the state and type of depression. Although this method achieves good results, it does not fully utilize the multi-channel information of EEG. Aiming at this problem, an EEG diagnosis method for depression based on multi-channel data fusion cropping enhancement and convolutional neural network is proposed. First, the multi-channel EEG data are transformed into 2D images after multi-channel fusion (MCF) and multi-scale clipping (MSC) augmentation. Second, it is trained by a multi-channel convolutional neural network (MCNN). Finally, the trained model is loaded into the detection device to classify the input EEG signals. The experimental results show that the combination of MCF and MSC can make full use of the information contained in the single sensor records, and significantly improve the classification accuracy and clustering effect of depression diagnosis. The method has the advantages of low complexity and good robustness in signal processing and feature extraction, which is beneficial to the wide application of detection systems.

The mechanisms of immune response and evasion by the main SARS-CoV-2 variants.

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. SARS-CoV-2 carries a unique group of mutations, and the transmission of the virus has led to the emergence of other mutants such as Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Kappa (B.1.617.1), Delta (B.1.617.2) and Omicron (B.1.1.529). The advent of a vaccine has raised hopes of ending the pandemic. However, the mutation variants of SARS-CoV-2 have raised concerns about the effectiveness of vaccines because the data showed that the vaccine was less effective against mutation variants compared to the previous variants. Mutation variants could easily mutate the N-segment structure and receptor domain of its spike glycoprotein (S) protein to escape antibody recognition. Therefore, it is vital to understand the potential immune response and evasion mechanism of SARS-CoV-2 variants. In this review, immune response and evasion mechanisms of several SARS-CoV-2 variants are described, which could provide some helpful advice for future vaccines.

Stringency of COVID-19 Containment Response Policies and Air Quality Changes: A Global Analysis across 1851 Cities.

The COVID-19 containment response policies (CRPs) had a major impact on air quality (AQ). These CRPs have been time-varying and location-specific. So far, despite having numerous studies on the effect of COVID-19 lockdown on AQ, a knowledge gap remains on the association between stringency of CRPs and AQ changes across the world, regions, nations, and cities. Here, we show that globally across 1851 cities (each more than 300 000 people) in 149 countries, after controlling for the impacts of relevant covariates (e.g., meteorology), Sentinel-5P satellite-observed nitrogen dioxide (NO2) levels decreased by 4.9% (95% CI: 2.2, 7.6%) during lockdowns following stringent CRPs compared to pre-CRPs. The NO2 levels did not change significantly during moderate CRPs and even increased during mild CRPs by 2.3% (95% CI: 0.7, 4.0%), which was 6.8% (95% CI: 2.0, 12.0%) across Europe and Central Asia, possibly due to population avoidance of public transportation in favor of private transportation. Among 1768 cities implementing stringent CRPs, we observed the most NO2 reduction in more populated and polluted cities. Our results demonstrate that AQ improved when and where stringent COVID-19 CRPs were implemented, changed less under moderate CRPs, and even deteriorated under mild CRPs. These changes were location-, region-, and CRP-specific.

Data Mining and Spatial Analysis of Social Media Text Based on the BERT-CNN Model to Achieve Situational Awareness: a Case Study of COVID-19

In response to the COVID-19, social media big data has played an important role in epidemic warning, tracking the source of infection, and public opinion monitoring, providing strong technical support for China’s epidemic prevention and control work. The paper used Sina Weibo posts related to COVID-19 hashtags as the data source, and built a BERT-CNN deep learning model to perform fine-grained and high-precision topic classificationon massive social media posts. Taking Shenzhen as a region of interest, we mined the “epidemic data bulletin” and “daily life impact” posts during the epidemic for spatial analysis. The results show that the confirmed communities and designated hospitals in Shenzhen as a whole present the characteristics of “sparse east and dense west”, and there is a strong positive spatial correlation between the number of confirmed cases and social media response. Specifically, Nanshan District, Futian District and Luohu District have more confirmed cases due to large population movements and dense transportation networks, and social media has responded more violently, and people’s lives have been greatly affected. However, Yantian District, Pingshan District and Dapeng New District showed opposite characteristics. The case study results further show that using deep learning methods to mine text information in social media is scientifically feasible for improving situational awareness and decision support during the COVID-19.

Nomogram Model for Prediction of SARS-CoV-2 Breakthrough Infection in Fujian: A Case-Control Real-World Study.

SARS-CoV-2 breakthrough infections have been reported because of the reduced efficacy of vaccines against the emerging variants globally. However, an accurate model to predict SARS-CoV-2 breakthrough infection is still lacking. In this retrospective study, 6,189 vaccinated individuals, consisting of SARS-CoV-2 test-positive cases (n = 219) and test-negative controls (n = 5970) during the outbreak of the Delta variant in September 2021 in Xiamen and Putian cities, Fujian province of China, were included. The vaccinated individuals were randomly split into a training (70%) cohort and a validation (30%) cohort. In the training cohort, a visualized nomogram was built based on the stepwise multivariate logistic regression. The area under the curve (AUC) of the nomogram in the training and validation cohorts was 0.819 (95% CI, 0.780-0.858) and 0.838 (95% CI, 0.778-0.897). The calibration curves for the probability of SARS-CoV-2 breakthrough infection showed optimal agreement between prediction by nomogram and actual observation. Decision curves indicated that nomogram conferred high clinical net benefit. In conclusion, a nomogram model for predicting SARS-CoV-2 breakthrough infection based on the real-world setting was successfully constructed, which will be helpful in the management of SARS-CoV-2 breakthrough infection.

Practical development and operationalization of a 12-hour hospital census prediction algorithm.

Hospital census prediction has well-described implications for efficient hospital resource utilization, and recent issues with hospital crowding due to CoVID-19 have emphasized the importance of this task. Our team has been leading an institutional effort to develop machine-learning models that can predict hospital census 12 hours into the future. We describe our efforts at developing accurate empirical models for this task. Ultimately, with limited resources and time, we were able to develop simple yet useful models for 12-hour census prediction and design a dashboard application to display this output to our hospital's decision-makers. Specifically, we found that linear models with ElasticNet regularization performed well for this task with relative 95% error of +/- 3.4% and that this work could be completed in approximately 7 months.

Bioinformatic and mouse model reveal the potential high vulnerability of Leydig cells on SARS-CoV-2.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in China and spread rapidly since the end of 2019. Previous studies have confirmed that SARS-CoV-2 infects host cells via binding to angiotensin converting enzyme II (ACE2). METHODS: To explore the expression of ACE2 and the potential risk of infection in testis, we performed a bioinformatic analysis based on public databases, and conducted a pilot study using a mouse model. We also collected clinical follow-up date on male patients who had recovered from COVID-19 for 6 months. RESULTS: The results showed that the RNA expression of ACE2 was higher in testis compared with other organs. Single-cell analysis and immunocytochemistry further indicated that Leydig cells were at risk of SARS-CoV-2 infection. Green fluorescence was only detected in the Leydig cells after intratesticular injection of pseudovirus SARS-CoV-2 in the mouse model. In the clinical follow-up, serum total testosterone level was statistically lower in patients who had recovered from COVID-19 compared with healthy men (P=0.010). CONCLUSIONS: The findings of the present study indicate the potential vulnerability of Leydig cells. It is important to monitor the reproductive system and its complications in male COVID-19 patients. Further studies are still needed on SARS-CoV-2-associated reproductive complications.

Primer design for quantitative real-time PCR for the emerging Coronavirus SARS-CoV-2.

In December 2019, a new coronavirus disease (COVID-19) outbreak occurred in Wuhan, China. Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), which is the seventh coronavirus known to infect humans, is highly contagious and has rapidly expanded worldwide since its discovery. Quantitative nucleic acid testing has become the gold standard for diagnosis and guiding clinical decisions regarding the use of antiviral therapy. However, the RT-qPCR assays targeting SARS-CoV-2 have a number of challenges, especially in terms of primer design. Primers are the pivotal components of a RT-qPCR assay. Once virus mutation and recombination occur, it is difficult to effectively diagnose viral infection by existing RT-qPCR primers. Some primers and probes have also been made available on the WHO website for reference. However, no previous review has systematically compared the previously reported primers and probes and described how to design new primers in the event of a new coronavirus infection. This review focuses on how primers and probes can be designed methodically and rationally, and how the sensitivity and specificity of the detection process can be improved. This brief review will be useful for the accurate diagnosis and timely treatment of the new coronavirus pneumonia.