Prevalence and Associated Factors of Depression Among Frontline Nurses in Wuhan 6 Months After the Outbreak of COVID-19: A Cross-Sectional Study

Background Due to the COVID-19 pandemic, frontline nurses have experienced psychological problems. However, the depression of frontline nurses in Wuhan 6 months after the outbreak of COVID-19 has not been studied adequately. The purpose of this study was to investigate the depression of frontline nurses in Wuhan 6 months after the COVID-19 outbreak and to analyze possible risk and protective factors. Material/Methods Data were collected through Wenjuanxing from 612 frontline nurses in Wuhan national COVID-19-designated hospitals between July 27, 2020, and August 12, 2020. The levels of depression, family functioning, and psychological resilience were assessed among frontline nurses in Wuhan with a depression scale, family function scale, and 10-item psychological resilience scale, respectively. The factors associated with depressive symptoms were identified using the chi-square test and binary logistic regression analysis. Results A total of 126 respondents were included in the study. The overall prevalence of depression was 25.2%. The need for mental health services was a potential risk factor for depressive symptoms, while family functioning and psychological resilience were potential protective factors. The COVID-19 pandemic poses a major challenge to the depressive symptoms of frontline nursing in Wuhan, highlighting the need for all frontline nurses in Wuhan to be screened for depression regularly for timely intervention. Conclusions To mitigate the impact of the pandemic on depression, psychological interventions for frontline nurses need to be implemented to preserve their mental health.

Study on the correlations of different clinical types with imaging findings at initial diagnosis and clinical laboratory indexes in COVID-19 patients.

Objectives: To investigate the correlations of initial lab and imaging findings in COVID-19 patients of different clinical types. Methods: We retrospective analyzed patients confirmed with COVID-19 in the Fifth Medical Center of the People's Liberation Army (PLA) General Hospital between February to April 2020, selected a total of 58 (N) patients with lab and imaging examinations that met the study criteria, using Artificial intelligence (AI) software to calculate the percentage of COVID-19 lesions in the volume of the whole lung, then the correlations of general information, initial chest CT examination after admission and laboratory examinations were analyzed. Results: The 58 (N) COVID-19 patients were divided into mild group [41(n) cases]: and severe group [17(n) cases]: according to patient's condition. CT findings of the severe group and mild group mainly included single or multiple ground glass opacity (GGO), with lesions mainly distributed in the periphery of lungs or GGO mixed with consolidation, with lesions involved in peripheral and central areas of both lungs, accompanied other signs. A significant difference in CRP, IL-6, D-D, GGT was observed between the two groups (p < 0.05). The ratios regarding lymphocyte abnormality and neutrophil abnormality in the severe group were higher than those in the mild group (p < 0.05). Conclusion: The CT features at initial diagnosis of COVID-19 were mainly characterized by multiple GGO with or without partial consolidation in both lungs, with the lesions mainly distributed at the subpleural regions. Some lab test indexes were correlated with the clinical types of COVID-19.

Potent Anti-SARS-CoV-2 Efficacy of COVID-19 Hyperimmune Globulin from Vaccine-Immunized Plasma.

Coronavirus disease 2019 (COVID-19) remains a global public health threat. Hence, more effective and specific antivirals are urgently needed. Here, COVID-19 hyperimmune globulin (COVID-HIG), a passive immunotherapy, is prepared from the plasma of healthy donors vaccinated with BBIBP-CorV (Sinopharm COVID-19 vaccine). COVID-HIG shows high-affinity binding to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein, the receptor-binding domain (RBD), the N-terminal domain of the S protein, and the nucleocapsid protein; and blocks RBD binding to human angiotensin-converting enzyme 2 (hACE2). Pseudotyped and authentic virus-based assays show that COVID-HIG displays broad-spectrum neutralization effects on a wide variety of SARS-CoV-2 variants, including D614G, 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) in vitro. However, a significant reduction in the neutralization titer is detected against Beta, Delta, and Omicron variants. Additionally, assessments of the prophylactic and treatment efficacy of COVID-HIG in an Adv5-hACE2-transduced IFNAR-/- mouse model of SARS-CoV-2 infection show significantly reduced weight loss, lung viral loads, and lung pathological injury. Moreover, COVID-HIG exhibits neutralization potency similar to that of anti-SARS-CoV-2 hyperimmune globulin from pooled convalescent plasma. Overall, the results demonstrate the potential of COVID-HIG against SARS-CoV-2 infection and provide reference for subsequent clinical trials.

Management of safe distancing on construction sites during COVID-19: A smart real-time monitoring system.

The outbreak of Coronavirus Disease 2019 (COVID-19) poses a great threat to the world. One mandatory and efficient measure to prevent the spread of COVID-19 on construction sites is to ensure safe distancing during workers' daily activities. However, manual monitoring of safe distancing during construction activities can be toilsome and inconsistent. This study proposes a computer vision-based smart monitoring system to automatically detect worker breaching safe distancing rules. Our proposed system consists of three main modules: (1) worker detection module using CenterNet; (2) proximity determination module using Homography; and (3) warning alert and data collection module. To evaluate the system, it was implemented in a construction site as a case study. This study has two key contributions: (1) it is demonstrated that monitoring of safe distancing can be automated using our approach; and (2) CenterNet, an anchorless detection model, outperforms current state-of-the-art approaches in the real-time detection of workers.

Automatic detection of multiple types of pneumonia: Open dataset and a multi-scale attention network.

The quick and precise identification of COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, mycoplasma pneumonia, and normal lung on chest CT images play a crucial role in timely quarantine and medical treatment. However, manual identification is subject to potential misinterpretations and time-consumption issues owing the visual similarities of pneumonia lesions. In this study, we propose a novel multi-scale attention network (MSANet) based on a bag of advanced deep learning techniques for the automatic classification of COVID-19 and multiple types of pneumonia. The proposed method can automatically pay attention to discriminative information and multi-scale features of pneumonia lesions for better classification. The experimental results show that the proposed MSANet can achieve an overall precision of 97.31%, recall of 96.18%, F1-score of 96.71%, accuracy of 97.46%, and macro-average area under the receiver operating characteristic curve (AUC) of 0.9981 to distinguish between multiple classes of pneumonia. These promising results indicate that the proposed method can significantly assist physicians and radiologists in medical diagnosis. The dataset is publicly available at https://doi.org/10.17632/rf8x3wp6ss.1.

Treatment strategies for gastric cancer during the COVID-19 pandemic.

The coronavirus disease 2019 pandemic has become a major global public health problem. Governments are taking the necessary steps to reduce the movement of people to contain the spread of the virus. However, these measures have caused considerable distress to patients with gastric cancer who are newly diagnosed or are undergoing treatment. In addition to the cancer, they must deal with longer waiting times for surgery and poor communication with doctors. Furthermore, gastric cancer patients generally have low immunity and a poor nutritional status, so they are a high-risk group for infection with the novel coronavirus. Therefore, it is necessary to formulate reasonable outpatient management strategies to reduce the adverse effects of the pandemic on their treatment. We summarize the management strategies for patients with gastric cancer during the pandemic.

Fine-Scale Temporal Dynamics of SARS-CoV-2 RNA Abundance in Wastewater during A COVID-19 Lockdown.

Wastewater is a pooled sampling instrument that may provide rapid and even early disease signals in the surveillance of COVID-19 disease at the community level, yet the fine-scale temporal dynamics of SARS-CoV-2 RNA in wastewater remains poorly understood. This study tracked the daily dynamics of SARS-CoV-2 RNA in the wastewater from two wastewater treatment plants (WWTPs) in Honolulu during a rapidly expanding COVID-19 outbreak and a responding four-week lockdown that resulted in a rapid decrease of daily clinical COVID-19 new cases. The wastewater SARS-CoV-2 RNA concentration from both WWTPs, as measured by three quantification assays (N1, N2, and E), exhibited both significant inter-day fluctuations (101.2-105.1 gene copies or GC/L in wastewater liquid fractions, or 101.4-106.2 GC/g in solid fractions) and an overall downward trend over the lockdown period. Strong and significant correlation was observed in measured SARS-CoV-2 RNA concentrations between the solid and liquid wastewater fractions, with the solid fraction containing majority (82.5%-92.5%) of the SARS-CoV-2 RNA mass and the solid-liquid SARS-CoV-2 RNA concentration ratios ranging from 103.6 to 104.3 mL/g. The measured wastewater SARS-CoV-2 RNA concentration was normalized by three endogenous fecal RNA viruses (F+ RNA coliphages Group II and III, and pepper mild mottle virus) to account for variations that may occur during the multi-step wastewater processing and molecular quantification, and the normalized abundance also exhibited similar daily fluctuations and overall downward trend over the sampling period.

Decreased NHE3 activity and trafficking in TGEV-infected IPEC-J2 cells via the SGLT1-mediated P38 MAPK/AKt2 pathway.

Transmissible gastroenteritis virus (TGEV) primarily replicates in intestinal epithelial cells and causes severe damage to host cells, resulting in diarrhea. Surface NHE3 serves as the key regulatory site controlling electroneutral Na+ absorption. In this study, our results showed that the surface NHE3 content was significantly reduced following TGEV infection, whereas the total level of protein expression was not significantly changed, and NHE3 activity gradually decreased with prolonged infection time. We then inhibited SGLT1 expression by lentiviral interference and drug inhibition, respectively. Inhibition studies showed that the level of phosphorylation of the downstream key proteins, MAPKAPK-2 and EZRIN, in the SGLT1-mediated p38MAPK/AKt2 signaling pathway was significantly increased. The surface NHE3 expression was also significantly increased, and NHE3 activity was also significantly enhanced. These results demonstrate that a TGEV infection can inhibit NHE3 translocation and attenuates sodium-hydrogen exchange activity via the SGLT1-mediated p38MAPK/AKt2 signaling pathway, affecting cellular electrolyte absorption leading to diarrhea.

Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans.

The COVID-19 pneumonia is a global threat since it emerged in early December 2019. Driven by the desire to develop a computer-aided system for the rapid diagnosis of COVID-19 to assist radiologists and clinicians to combat with this pandemic, we retrospectively collected 206 patients with positive reverse-transcription polymerase chain reaction (RT-PCR) for COVID-19 and their 416 chest computed tomography (CT) scans with abnormal findings from two hospitals, 412 non-COVID-19 pneumonia and their 412 chest CT scans with clear sign of pneumonia are also retrospectively selected from participating hospitals. Based on these CT scans, we design an artificial intelligence (AI) system that uses a multi-scale convolutional neural network (MSCNN) and evaluate its performance at both slice level and scan level. Experimental results show that the proposed AI has promising diagnostic performance in the detection of COVID-19 and differentiating it from other common pneumonia under limited number of training data, which has great potential to assist radiologists and physicians in performing a quick diagnosis and mitigate the heavy workload of them especially when the health system is overloaded. The data is publicly available for further research at https://data.mendeley.com/datasets/3y55vgckg6/1https://data.mendeley.com/datasets/3y55vgckg6/1.

Epidemic Dynamics and Clinical Features of COVID-19 and Government Measures to Control Disease Transmission in Southern Hainan Island, China (preprint)

Objective: The objective of this study is to determine the epidemic dynamics and clinical features of COVID-19 in southern Hainan Island, China, and provide experience for other tropical areas of the world. Methods: This retrospective study included confirmed cases of COVID-19 in southern Hainan. All enrolled patients were treated in Sanya, and data on epidemiological and clinical features of the disease and infection prevention and control measures adopted by the local government during the epidemic were collected. Results: Of the 74 cases, 71 (95.95%) were imported from Wuhan, Hubei Province (47, 63.51%), other cities in Hubei Province (11, 14.86%), or provinces other than Hubei and Hainan (13, 17.57%). Three (4.06%) patients were infected in southern Hainan, including one autochthonous case in Sanya. Fifty-four cases (72.97%) were detected in Sanya, and 27 cases (27.03%) were diagnosed in other cities. The rate of severe or critical cases was 28.38% (21/74), and mortality was 2.7% (2/74). The serum lactate levels and base excess of severe-critical patients were higher than those of patients with mild-moderate disease. Multivariate logistic regression analysis showed that chronic conditions were risk factors for severe and critical COVID-19. Seventy-four patients were diagnosed with COVID-19 over a 22-day period in Sanya, and the epidemic period in the city was 48 days. The outbreak was controlled rapidly because the local government adopted strict infection prevention and control measures. Conclusions: The clinical characteristics of COVID-19 in Hainan Island were similar to those reported in other regions. In Sanya, the rate of severe and very severe cases was higher than in other regions; however, most cases were imported, and there was only one autochthonous case. The rapid control of the outbreak in Sanya may be related to the tropical climate, adoption of strict infection prevention and control measures, daily reporting of new cases, increased public awareness about the epidemic, and other emergency actions implemented by the local government.