Experiences of healthcare workers following occupational exposure to COVID-19 at the early stages of the pandemic: A phenomenological qualitative study.

AIM: To explore the experiences of healthcare workers (HCWs) following occupational exposure to coronavirus disease 2019 (COVID-19) during the early stage of the pandemic. DESIGN: A Husserl descriptive phenomenological study design was employed. METHODS: Convenient and snowball sampling was used. In-depth semi-structured telephone interviews were conducted from February to March 2020 with the frontline HCWs who were exposed to COVID-19 during work. Data analysis was conducted following the 7-step analysis method developed by Colaizzi. RESULTS: Fifteen HCWs participated in the study. Four themes were identified, including (1) traumatic experiences since the occupational exposure; (2) getting through the hard time; (3) struggling to return to work; (4) reflections on occupational exposures. CONCLUSION: The HCWs had traumatic and painful experiences after the occupational exposure. But they returned to work with strong resilience, professional obligation and social support. Training and supervision, and adequate supply of personal protective equipment are suggested to prevent professional exposure. Social and organizational support should be provided for the exposed HCWs.

Federated Active Learning for Multicenter Collaborative Disease Diagnosis.

Current computer-aided diagnosis system with deep learning method plays an important role in the field of medical imaging. The collaborative diagnosis of diseases by multiple medical institutions has become a popular trend. However, large scale annotations put heavy burdens on medical experts. Furthermore, the centralized learning system has defects in privacy protection and model generalization. To meet these challenges, we propose two federated active learning methods for multicenter collaborative diagnosis of diseases: the Labeling Efficient Federated Active Learning (LEFAL) and the Training Efficient Federated Active Learning (TEFAL). The proposed LEFAL applies a task-agnostic hybrid sampling strategy considering data uncertainty and diversity simultaneously to improve data efficiency. The proposed TEFAL evaluates the client informativeness with a discriminator to improve client efficiency. On the Hyper-Kvasir dataset for gastrointestinal disease diagnosis, with only 65% of labeled data, the LEFAL achieves 95% performance on the segmentation task with whole labeled data. Moreover, on the CC-CCII dataset for COVID-19 diagnosis, with only 50 iterations, the accuracy and F1-score of TEFAL are 0.90 and 0.95, respectively on the classification task. Extensive experimental results demonstrate that the proposed federated active learning methods outperform state-of-the-art methods on segmentation and classification tasks for multicenter collaborative disease diagnosis.

Endothelial Biomarkers in Patients Recovered from COVID-19 One Year after Hospital Discharge: A Cross-Sectional Study.

Background: COVID-19 is characterized by endothelial dysfunction and is presumed to have long-term cardiovascular sequelae. In this cross-sectional study, we aimed to explore the serum levels of endothelial biomarkers in patients who recovered from COVID-19 one year after hospital discharge. Methods: In this clinical follow-up study, 345 COVID-19 survivors from Huanggang, Hubei, and 119 age and gender-matched medical staff as healthy controls were enrolled. A standardized symptom questionnaire was performed, while electrocardiogram and Doppler ultrasound of lower extremities, routine blood tests, biochemical and immunological tests, serum soluble vascular cell adhesion molecule-1(VCAM-1), intercellular cell adhesion molecule-1(ICAM-1), P-selectin, and fractalkine were measured by enzyme-linked immunosorbent assays (ELISA). Results: At one year after discharge, 39% of recovers possessed post-COVID syndromes, while a few had abnormal electrocardiogram manifestations, and no deep vein thrombosis was detected in all screened survivors. There were no significant differences in circulatory inflammatory markers (leukocytes, neutrophils, lymphocytes, C-reactive protein and interleukin-6), alanine aminotransferase, estimated glomerular filtration rate, glucose, triglycerides, total cholesterol and D-dimer observed among healthy controls with previously mild or severe infected. Furthermore, serum levels of VCAM-1, ICAM-1, P-selectin, and fractalkine do not significantly differ between survivors and healthy controls. Conclusions: SARS-CoV-2 infection may not impose a higher risk of developing long-term cardiovascular events, even for those recovering from severe illness.

Industrial construction safety policies and practices with cost impacts in a COVID-19 pandemic environment: A Louisiana DOW case study.

There are always significant challenges in improving the safety culture by changing and adding additional safety protocols. The unknown impacts of COVID-19 and how it quickly spreads led the industry to institute essential safety protocols. This paper addresses two problem statements. The first problem statement is: what are the additional safety protocols for process safety, construction & maintenance, and personal protective equipment requirements? The second problem statement is: what are the cost and schedule impacts of industrial construction projects resulting from implementing safety protocols and process safety during construction with the added PPE? While complying with added safety protocols, the industrial construction industry cannot forget that it has a distinct reputation for high incident rates and less than desirable safety performance. In 2017, the construction industry suffered 971 fatalities. This alarming number is compared to 1123 total fatalities in 2017 for the Gulf Coast States. The objective is to share the rationale and practices of social distancing, required additional PPE, and personal hygiene practices to reduce spreading and outbreaks during a pandemic within an industrial construction environment. Before any construction work, the process safety teams must clear, isolate, and tag out process lines, equipment, and instruments to be repaired or replaced. The information presented demonstrates the significant cost and schedule impacts that industrial construction companies will encounter during a pandemic like COVID-19. This paper aims to improve safety processes, cost & schedule impacts, and prescribe additional personal protective equipment in industrial construction during a pandemic such as COVID-19. The COVID-19 pandemic spread globally in a very short period. The reactions in mitigating the spread were suggestive, with little to no data on safety protective equipment and practices. The contribution this paper addresses are how to employ efficient safety practices and policies during a pandemic in an industrial construction environment.

Extreme events, energy security and equality through micro- and macro-levels: Concepts, challenges and methods

Low-income households face long-standing challenges of energy insecurity and inequality (EII). During extreme events (e.g., disasters and pandemics) these challenges are especially severe for vulnerable populations reliant on energy for health, education, and well-being. However, many EII studies rarely incorporate the micro- and macro-perspectives of resilience and reliability of energy and internet infrastructure and social-psychological factors. To remedy this gap, we first address the impacts of extreme events on EII among vulnerable populations. Second, we evaluate the driving factors of EII and how they change during disasters. Third, we situate these inequalities within broader energy systems and pinpoint the importance of equitable infrastructure systems by examining infrastructure reliability and resilience and the role of renewable technologies. Then, we consider the factors influencing energy consumption, such as energy practices, socio-psychological factors, and internet access. Finally, we propose interdisciplinary research methods to study these issues during extreme events and provide recommendations.

Serum surfactant protein D in COVID-19 is elevated and correlated with disease severity.

BACKGROUND: The serum surfactant protein D (SP-D) level is suggested to be a useful biomarker for acute lung injuries and acute respiratory distress syndrome. Whether the serum SP-D level could identify the severity of coronavirus disease 2019 (COVID-19) in the early stage has not been elucidated. METHODS: We performed an observational study on 39 laboratory-confirmed COVID-19 patients from The Fourth People's Hospital of Yiyang, Hunan, China. Receiver operating characteristic (ROC) curve analysis, correlation analysis, and multivariate logistic regression model analysis were performed. RESULTS: In the acute phase, the serum levels of SP-D were elevated significantly in severe COVID-19 patients than in mild cases (mean value ± standard deviation (SD), 449.7 ± 125.8 vs 245.9 ± 90.0 ng/mL, P<0.001), while the serum levels of SP-D in the recovery period were decreased dramatically than that in the acute phase (mean value ± SD, 129.5 ± 51.7 vs 292.9 ± 130.7 ng/ml, P<0.001), and so were for the stratified patients. The chest CT imaging scores were considerably higher in the severe group compared with those in the mild group (median value, 10.0 vs 9.0, P = 0.011), while markedly lower in the recovery period than those in the acute phase (median value, 2.0 vs 9.0, P<0.001), and so were for the stratified patients. ROC curve analysis revealed that areas under the curve of lymphocyte counts (LYM), C-reaction protein (CRP), erythrocyte sedimentation rate (ESR), interleukin-6 (IL-6), and SP-D for severe COVID-19 were 0.719, 0.833, 0.817, 0.837, and 0.922, respectively. Correlation analysis showed that the SP-D levels were negatively correlated with LYM (r = - 0.320, P = 0.047), while positively correlated with CRP (r = 0.658, P<0.001), IL-6 (r = 0.471, P = 0.002), the duration of nucleic acid of throat swab turning negative (r = 0.668, P<0.001), chest CT imaging score on admission (r = 0.695, P<0.001) and length of stay (r = 0.420, P = 0.008). Multivariate logistic regression model analysis showed that age (P = 0.041, OR = 1.093) and SP-D (P = 0.008, OR = 1.018) were risk factors for severe COVID-19. CONCLUSIONS: Elevated serum SP-D level was a potential biomarker for the severity of COVID-19; this may be useful in identifying patients whose condition worsens at an early stage.

Anti-SARS-CoV-2 IgG levels in relation to disease severity of COVID-19.

The durability of infection-induced severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunity has crucial implications for reinfection and vaccine effectiveness. However, the relationship between coronavirus disease 2019 (COVID-19) severity and long-term anti-SARS-CoV-2 immunoglobulin G (IgG) antibody level is poorly understood. Here, we measured the longevity of SARS-CoV-2-specific IgG antibodies in survivors who had recovered from COVID-19 1 year previously. In a cohort of 473 survivors with varying disease severity (asymptomatic, mild, moderate, or severe), we observed a positive correlation between virus-specific IgG antibody titers and COVID-19 severity. In particular, the highest virus-specific IgG antibody titers were observed in patients with severe COVID-19. By contrast, 74.4% of recovered asymptomatic carriers had negative anti-SARS-CoV-2 IgG test results, while many others had very low virus-specific IgG antibody titers. Our results demonstrate that SARS-CoV-2-specific IgG persistence and titer depend on COVID-19 severity.

Severity-associated markers and assessment model for predicting the severity of COVID-19: a retrospective study in Hangzhou, China.

BACKGROUND: The severity of COVID-19 associates with the clinical decision making and the prognosis of COVID-19 patients, therefore, early identification of patients who are likely to develop severe or critical COVID-19 is critical in clinical practice. The aim of this study was to screen severity-associated markers and construct an assessment model for predicting the severity of COVID-19. METHODS: 172 confirmed COVID-19 patients were enrolled from two designated hospitals in Hangzhou, China. Ordinal logistic regression was used to screen severity-associated markers. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed for further feature selection. Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. Internal validation was performed by using bootstrap with 500 re-sampling in the training set, and external validation was performed in the validation set for the four models, respectively. RESULTS: Age, comorbidity, fever, and 18 laboratory markers were associated with the severity of COVID-19 (all P values < 0.05). By LASSO regression, eight markers were included for the assessment model construction. The ridge regression model had the best performance with AUROCs of 0.930 (95% CI, 0.914-0.943) and 0.827 (95% CI, 0.716-0.921) in the internal and external validations, respectively. A risk score, established based on the ridge regression model, had good discrimination in all patients with an AUROC of 0.897 (95% CI 0.845-0.940), and a well-fitted calibration curve. Using the optimal cutoff value of 71, the sensitivity and specificity were 87.1% and 78.1%, respectively. A web-based assessment system was developed based on the risk score. CONCLUSIONS: Eight clinical markers of lactate dehydrogenase, C-reactive protein, albumin, comorbidity, electrolyte disturbance, coagulation function, eosinophil and lymphocyte counts were associated with the severity of COVID-19. An assessment model constructed with these eight markers would help the clinician to evaluate the likelihood of developing severity of COVID-19 at admission and early take measures on clinical treatment.

SARS-CoV-2 Host Receptor ACE2 Protein Expression Atlas in Human Gastrointestinal Tract.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects host cells through interactions with its receptor, Angiotensin-converting enzyme 2 (ACE2), causing severe acute respiratory syndrome and death in a considerable proportion of people. Patients infected with SARS-CoV-2 experience digestive symptoms. However, the precise protein expression atlas of ACE2 in the gastrointestinal tract remains unclear. In this study, we aimed to explore the ACE2 protein expression pattern and the underlying function of ACE2 in the gastrointestinal tract, including the colon, stomach, liver, and pancreas. METHODS: We measured the protein expression of ACE2 in the gastrointestinal tract using immunohistochemical (IHC) staining with an ACE2-specific antibody of paraffin-embedded colon, stomach, liver, and pancreatic tissues. The correlation between the protein expression of ACE2 and the prognosis of patients with gastrointestinal cancers was analyzed by the log-rank (Mantel-Cox) test. The influence of ACE2 on colon, stomach, liver, and pancreatic tumor cell line proliferation was tested using a Cell Counting Kit 8 (CCK-8) assay. RESULTS: ACE2 presented heterogeneous expression patterns in the gastrointestinal tract, and it showed a punctate distribution in hepatic cells. Compared to that in parallel adjacent non-tumor tissues, the protein expression of ACE2 was significantly increased in colon cancer, stomach cancer, and pancreatic cancer tissues but dramatically decreased in liver cancer tissues. However, the expression level of the ACE2 protein was not correlated with the survival of patients with gastrointestinal cancers. Consistently, ACE2 did not affect the proliferation of gastrointestinal cancer cells in vitro. CONCLUSION: The ACE2 protein is widely expressed in the gastrointestinal tract, and its expression is significantly altered in gastrointestinal tumor tissues. ACE2 is not an independent prognostic marker of gastrointestinal cancers.

Get Close to the Robot: The Effect of Risk Perception of COVID-19 Pandemic on Customer-Robot Engagement.

The purpose of this study was to examine the effect of the COVID-19 pandemic on customer-robot engagement in the Chinese hospitality industry. Analysis of a sample of 589 customers using service robots demonstrated that the perceived risk of COVID-19 has a positive influence on customer-robot engagement. The positive effect is mediated by social distancing and moderated by attitudes towards risk. Specifically, the mediating effect of social distancing between the perceived risk of COVID-19 and customer-robot engagement is stronger for risk-avoiding (vs. risk-seeking) customers. Our results provide insights for hotels when they employ service robots to cope with the shock of COVID-19 pandemic.

Changes in pediatric healthcare utilization in Hunan Province, China, during the COVID-19 pandemic: a multi-center cross-sectional study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic brought remarkable disruption to the ways in which healthcare was delivered. This study aimed to examine changes in pediatric healthcare utilization in Hunan Province, China, during the COVID-19 pandemic. METHODS: An electronic survey was conducted among 142 hospitals in Hunan Province, China. Using data from January 1 to April 30, 2019 as a reference, the changes in the number of visits for different types of pediatric healthcare between January 1 and April 30, 2020 were calculated. Changes in the number of admissions for infections and injuries were also evaluated. RESULTS: The total number of pediatric healthcare presentations decreased by 53.3% in the first four months of 2020. The most remarkable reductions were observed in the utilization of emergency room (ranging from -45.7% to -94.9% among three hospital levels) and observation room (-55.8% to -77.7%); neonatal inpatient care experienced the smallest decreases (-21.2% to -25.5%). Approximately 85% of the total reduction in the number of pediatric inpatient admissions was attributable to the reduction in admissions for infections. A 13.3% increase in the number of admissions for injuries was observed among third-level hospitals. CONCLUSIONS: The utilization of all types of pediatric healthcare services in Hunan Province declined markedly after the outbreak of COVID-19. The reasons, consequences, and responses to these changes should be addressed in future studies and actions.

The analysis of isolation measures for epidemic control of COVID-19.

This paper proposes a susceptible exposed infectious recovered model (SEIR) with isolation measures to evaluate the COVID-19 epidemic based on the prevention and control policy implemented by the Chinese government on February 23, 2020. According to the Chinese government's immediate isolation and centralized diagnosis of confirmed cases, and the adoption of epidemic tracking measures on patients to prevent further spread of the epidemic, we divide the population into susceptible, exposed, infectious, quarantine, confirmed and recovered. This paper proposes an SEIR model with isolation measures that simultaneously investigates the infectivity of the incubation period, reflects prevention and control measures and calculates the basic reproduction number of the model. According to the data released by the National Health Commission of the People's Republic of China, we estimated the parameters of the model and compared the simulation results of the model with actual data. We have considered the trend of the epidemic under different incubation periods of infectious capacity. When the incubation period is not contagious, the peak number of confirmed in the model is 33,870; and when the infectious capacity is 0.1 times the infectious capacity in the infectious period, the peak number of confirmed in the model is 57,950; when the infectious capacity is doubled, the peak number of confirmed will reach 109,300. Moreover, by changing the contact rate in the model, we found that as the intensity of prevention and control measures increase, the peak of the epidemic will come earlier, and the peak number of confirmed will also be significantly reduced. Under extremely strict prevention and control measures, the peak number of confirmed cases has dropped by nearly 50%. In addition, we use the EEMD method to decompose the time series data of the epidemic, and then combine the LSTM model to predict the trend of the epidemic. Compared with the method of directly using LSTM for prediction, more detailed information can be obtained.

Elevated Serum Pentraxin-3 Levels is Positively Correlated to Disease Severity and Coagulopathy in COVID-19 Patients.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is highly contagious and deadly and is associated with coagulopathy. Pentraxin-3(PTX3) participates in innate resistance to infections and plays a role in thrombogenesis. PURPOSE: The present study aimed to investigate the role of PTX3 in coagulopathy in patients with COVID-19. METHODS: A retrospective study, including thirty-nine COVID-19 patients, enrolled in Hunan, China, were performed. The patients were classified into the D-dimer_L (D-dimer <1mg/L) and D-dimer_H (D-dimer≥1mg/L) groups basing on the plasma D-dimer levels on admission. Serum PTX3 levels were detected by enzyme-linked immunosorbent assays and compared between those two groups, then receiver operating characteristic (ROC) curve analysis, correlation analysis, and linear regression models were performed to analyze the association between PTX3 and D-dimer. RESULTS: Our results showed that serum PTX3 levels (median values, 10.21 vs. 3.36, P<0.001), computerized chest tomography (C.T.) scores (median values, 10.0 vs. 9.0, P<0.05), and length of stay (LOS) (mean values, 16.0 vs. 10.7, P=0.001) in the D-dimer_H group were significantly higher than that in D-dimer_L group. ROC curve analysis revealed that the AUC of white blood corpuscle counts, C-reaction protein, erythrocyte sedimentation rate, and PTX3 for COVID-19 were 0.685, 0.863, 0.846, and 0.985, respectively. Correlation analysis showed that there was a positive relationship between PTX3 and D-dimer (r=0.721, P<0.001), chest CT imaging score (r=0.418, P=0.008), and LOS (r=0.486, P=0.002). Multiple linear regression analysis showed that the coefficient of determination was 0.657 (P < 0.001). CONCLUSION: Serum level of PTX3 was positively correlated with disease severity and coagulopathy. Detection of serum PTX3 level could help identify severer patients on admission and may be a potential therapeutic target for coagulopathy in patients with COVID-19.