DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19.

The Coronavirus Disease 2019 (COVID-19) pandemic is still wreaking havoc worldwide. Therefore, the urgent need for efficient treatments pushes researchers and clinicians into screening effective drugs. Drug repurposing may be a promising and time-saving strategy to identify potential drugs against this disease. Here, we developed a novel computational approach, named Drug Target Set Enrichment Analysis (DTSEA), to identify potent drugs against COVID-19. DTSEA first mapped the disease-related genes into a gene functional interaction network, and then it used a network propagation algorithm to rank all genes in the network by calculating the network proximity of genes to disease-related genes. Finally, an enrichment analysis was performed on drug target sets to prioritize disease-candidate drugs. It was shown that the top three drugs predicted by DTSEA, including Ataluren, Carfilzomib, and Aripiprazole, were significantly enriched in the immune response pathways indicating the potential for use as promising COVID-19 inhibitors. In addition to these drugs, DTSEA also identified several drugs (such as Remdesivir and Olumiant), which have obtained emergency use authorization (EUA) for COVID-19. These results indicated that DTSEA could effectively identify the candidate drugs for COVID-19, which will help to accelerate the development of drugs for COVID-19. We then performed several validations to ensure the reliability and validity of DTSEA, including topological analysis, robustness analysis, and prediction consistency. Collectively, DTSEA successfully predicted candidate drugs against COVID-19 with high accuracy and reliability, thus making it a formidable tool to identify potential drugs for a specific disease and facilitate further investigation.

Cost Accounting in Multiple-Source and Multiple-Sink Logistics Management From Network Flow Perspectives

Transportation and logistics costs are becoming a large portion of the operating expenses for many businesses. Recently, supply chain disruptions caused by the COVID-19 pandemic and inflation crisis have brought challenges, especially, to many small- and medium-sized companies. Not only are companies struggling with logistics costs, but logistics bottlenecks are often preventing businesses from growing, expanding, and obtaining additional market shares. According to both academics and practitioners, there must be more literature and studies to address these logistics management challenges from cost accounting perspectives. This study focuses on multiple-source and multiple-sink scenarios, in which products are delivered from various production units to various stores. Optimized solutions to these cases may suggest optimal logistics strategies in terms of the minimized costs, as well as provide insights for later profitability analysis through common cost allocations and segment income statement reports. This paper can contribute to the practical examples in logistics management for businesses and is an addition to the current literature on cost accounting issues.

Multidimensional evaluation of offline and online education in dermatology teaching during the COVID-19 pandemic in a chinese teaching hospital: a cross-sectional study.

BACKGROUND: The global spread of Coronavirus disease (COVID-19) has led to the use of online teaching methods in universities, but the effect of online education on dermatology teaching remains unclear. METHODS: We designed a multi-dimensional teaching evaluation form for data collection, student teaching feedback evaluation, and assessed the scores of final theoretical and clinical skill tests, to compare the effective difference between online and offline teaching of dermatology. RESULTS: A total of 311 valid questionnaires of medical undergraduates were collected, 116 of which were enrolled for offline learning, and 195 for online learning. The average score of final theoretical test in the online teaching group had no significant difference compared with that in the offline teaching group (75.33 ± 7.37 vs.75.63 ± 7.51, P = 0.734). However, both scores of skin lesion recognition test and medical history collection test in the online teaching group were significantly lower than that in the offline teaching group (6.53 ± 0.86 vs. 7.10 ± 1.11, P < 0.001; 6.70 ± 1.16 vs. 7.62 ± 0.85, P < 0.001). Additionally, the scores of understanding skin lesions in the online teaching group were significantly lower than that in the offline group (P < 0.001), and the scores of overall understanding of skin diseases and evaluating their learning mode in the online teaching group also decreased (P < 0.05). Among the 195 students enrolled in the online learning group, 156 students (80.0%) recognized that the time of offline teaching should be increased. CONCLUSIONS: Both online and offline education can be used in dermatology theory teaching, but online education is less efficient in skin lesion and practical skills learning. More online teaching software with skin diseases characteristic should be developed to improve the online teaching effect.

COVID-19 Vaccine Hesitancy and Racial Discrimination among US Adults.

Vaccination is a critical preventive measure to reduce COVID-19 health risks. We utilize full information maximum likelihood (FIML) logistic regression to analyze COVID-19 vaccine hesitancy in a national sample of United States (US) adults (N=2,022). Online survey data was collected between September 7th and October 3rd, 2021. Before weighting, the racial composition of the sample was as follows: Asian American (15.0%), Black/African American (20.0%), Hispanic/Latino (20.0%), American Indian or Alaska Native (12.6%), Native Hawaiian or Pacific Islander (12.5%), and White (20.0%). Informed by the Increasing Vaccination Model (IVM), we assessed the relationship between COVID-19 vaccine hesitancy and experiences of racial discrimination (Krieger's 9-item measure). Odds of COVID-19 vaccine hesitancy were greater for most younger age groups, women (OR=1.96; 95% CI[1.54, 2.49]), Black/African American respondents (OR=1.68; 95% CI[1.18, 2.39]), those with a high school education or less (OR=1.46; 95% CI[1.08, 1.98]), Independent (OR=1.77; 95% CI[1.34, 2.35]) or Republican political affiliation (OR=2.69; 95% CI[1.92, 3.79]), and prior COVID-19 infection (OR=1.78; 95% CI[1.29, 2.45]). Odds of COVID-19 vaccine hesitancy were 1.04 greater for every one unit increase in lifetime experiences of racial discrimination (95% CI[1.02, 1.05]). Odds of COVID-19 vaccine hesitancy were lower for Asian American respondents (OR=.682; 95% CI[.480, .969]), and those who had a primary care doctor had reduced odds of COVID-19 vaccine hesitancy (OR=.735; 95% CI[.542, .998). Our primary finding provides support for a link between experiences of racial discrimination and hesitancy towards a COVID-19 vaccine among US adults. We discuss implications for public health officials and future research.

3- to 24-month Follow-up on COVID-19 with Pulmonary Tuberculosis Survivors after Discharge: Results from a Prospective, Multicenter Study.

Objective: Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are major public health and social issues worldwide. The long-term follow-up of COVID-19 with pulmonary TB (PTB) survivors after discharge is unclear. This study aimed to comprehensively describe clinical outcomes, including sequela and recurrence at 3, 12, and 24 months after discharge, among COVID-19 with PTB survivors. Methods: From January 22, 2020 to May 6, 2022, with a follow-up by August 26, 2022, a prospective, multicenter follow-up study was conducted on COVID-19 with PTB survivors after discharge in 13 hospitals from four provinces in China. Clinical outcomes, including sequela, recurrence of COVID-19, and PTB survivors, were collected via telephone and face-to-face interviews at 3, 12, and 24 months after discharge. Results: Thirty-two COVID-19 with PTB survivors were included. The median age was 52 (45, 59) years, and 23 (71.9%) were men. Among them, nearly two-thirds (62.5%) of the survivors were moderate, three (9.4%) were severe, and more than half (59.4%) had at least one comorbidity (PTB excluded). The proportion of COVID-19 survivors with at least one sequela symptom decreased from 40.6% at 3 months to 15.8% at 24 months, with anxiety having a higher proportion over a follow-up. Cough and amnesia recovered at the 12-month follow-up, while anxiety, fatigue, and trouble sleeping remained after 24 months. Additionally, one (3.1%) case presented two recurrences of PTB and no re-positive COVID-19 during the follow-up period. Conclusion: The proportion of long symptoms in COVID-19 with PTB survivors decreased over time, while nearly one in six still experience persistent symptoms with a higher proportion of anxiety. The recurrence of PTB and the psychological support of COVID-19 with PTB after discharge require more attention.

Metabolite profile of COVID-19 revealed by UPLC-MS/MS-based widely targeted metabolomics.

The metabolic characteristics of COVID-19 disease are still largely unknown. Here, 44 patients with COVID-19 (31 mild COVID-19 patients and 13 severe COVID-19 patients), 42 healthy controls (HC), and 42 patients with community-acquired pneumonia (CAP), were involved in the study to assess their serum metabolomic profiles. We used widely targeted metabolomics based on an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The differentially expressed metabolites in the plasma of mild and severe COVID-19 patients, CAP patients, and HC subjects were screened, and the main metabolic pathways involved were analyzed. Multiple mature machine learning algorithms confirmed that the metabolites performed excellently in discriminating COVID-19 groups from CAP and HC subjects, with an area under the curve (AUC) of 1. The specific dysregulation of AMP, dGMP, sn-glycero-3-phosphocholine, and carnitine was observed in the severe COVID-19 group. Moreover, random forest analysis suggested that these metabolites could discriminate between severe COVID-19 patients and mild COVID-19 patients, with an AUC of 0.921. This study may broaden our understanding of pathophysiological mechanisms of COVID-19 and may offer an experimental basis for developing novel treatment strategies against it.

SARS-CoV-2 vaccination in androgen sensitive phenotypes - A study on associated factors for SARS-CoV-2 vaccination and its adverse effects among androgenetic alopecia and benign prostate hyperplasia patients.

Background: Androgen sensitivity, which was established as the leading etiology of androgenetic alopecia (AGA) and benign prostatic hyperplasia (BPH), plays an important role in SARS-CoV-2 infection. Vaccination is essential for AGA and BPH patients in view of the high risk from SARS-CoV-2 infection. Purpose: We aimed to investigate the associated factors for SARS-CoV-2 vaccination and its side effects in populations with AGA and BPH. Method: We collected the data on SARS-CoV-2 vaccination and adverse reactions of male AGA and BPH patients visited the outpatient of Xiangya hospital by telephone and web-based questionnaires. Vaccination rate and adverse reactions were compared by different vaccine types and use of anti-androgen therapy. Result: A total of 457 AGA patients and 397 BPH patients were recruited in this study. Among which, 92.8% AGA patients and 61.0% BPH patients had at least the first dose of SARS-CoV-2 vaccination (p < 0.001). Having comorbidities and use of anti-androgen therapy increased the risk of un-vaccination among AGA by 2.875 and 3.729 times, respectively (p < 0.001). Around 31.1% AGA patients and 9.5% BPH patients presented adverse reactions, which were mostly mild. Anti-androgen therapy increased the inclination of injection site pain after vaccination (18.7% vs 11.9%; OR: 1.708, 95% CI: 1.088-2.683, p = 0.019). Conclusion: Co-existence of other systemic diseases and anti-androgen therapy were the limiting factors for SARS-CoV-2 unvaccination, especially in AGA patients. The importance of SARS-CoV-2 vaccines should be strengthened and popularized in androgen sensitive phenotypes.

SARS-CoV-2 vaccination in androgen sensitive phenotypes – A study on associated factors for SARS-CoV-2 vaccination and its adverse effects among androgenetic alopecia and benign prostate hyperplasia patients

Background Androgen sensitivity, which was established as the leading etiology of androgenetic alopecia (AGA) and benign prostatic hyperplasia (BPH), plays an important role in SARS-CoV-2 infection. Vaccination is essential for AGA and BPH patients in view of the high risk from SARS-CoV-2 infection. Purpose We aimed to investigate the associated factors for SARS-CoV-2 vaccination and its side effects in populations with AGA and BPH. Method We collected the data on SARS-CoV-2 vaccination and adverse reactions of male AGA and BPH patients visited the outpatient of Xiangya hospital by telephone and web-based questionnaires. Vaccination rate and adverse reactions were compared by different vaccine types and use of anti-androgen therapy. Result A total of 457 AGA patients and 397 BPH patients were recruited in this study. Among which, 92.8% AGA patients and 61.0% BPH patients had at least the first dose of SARS-CoV-2 vaccination (p < 0.001). Having comorbidities and use of anti-androgen therapy increased the risk of un-vaccination among AGA by 2.875 and 3.729 times, respectively (p < 0.001). Around 31.1% AGA patients and 9.5% BPH patients presented adverse reactions, which were mostly mild. Anti-androgen therapy increased the inclination of injection site pain after vaccination (18.7% vs 11.9%;OR: 1.708, 95% CI: 1.088-2.683, p = 0.019). Conclusion Co-existence of other systemic diseases and anti-androgen therapy were the limiting factors for SARS-CoV-2 unvaccination, especially in AGA patients. The importance of SARS-CoV-2 vaccines should be strengthened and popularized in androgen sensitive phenotypes.

Targeting on Nrf2/Sesn2 Signaling to Rescue Cardiac Dysfunction during High-Fat Diet-Induced Obesity.

Obesity is of concern to the population because it is known to cause inflammation and oxidative stress throughout the body, leading to patient predisposition for health conditions such as diabetes, hypertension, and some cancers. However, some proteins that are activated in times of oxidative stress may provide cytoprotective properties. In this study, we aim to gain further understanding of the interconnection between Nrf2 and Sesn2 during obesity-related stress and how this relationship can play a role in cardio-protection. Cardiomyocyte-specific Sesn2 knockout (cSesn2-/-) and Sesn2 overexpressed (tTa-tet-Sesn2) mice and their wildtype littermates (Sesn2flox/flox and tet-Sesn2, respectively) were assigned to either a normal chow (NC) or a high-fat (HF) diet to induce obesity. After 16 weeks of dietary intervention, heart function was evaluated via echocardiography and cardiac tissue was collected for analysis. Immunoblotting, histology, and ROS staining were completed. Human heart samples were obtained via the LifeLink Foundation and were also subjected to analysis. Overall, these results indicated that the overexpression of Sesn2 appears to have cardio-protective effects on the obese heart through the reduction of ROS and fibrosis present in the tissues and in cardiac function. These results were consistent for both mouse and human heart samples. In human samples, there was an increase in Sesn2 and Nrf2 expression in the obese patients' LV tissue. However, there was no observable pattern of Sesn2/Nrf2 expression in mouse LV tissue samples. Further investigation into the link between the Sesn2/Nrf2 pathway and obesity-related oxidative stress is needed.

The mechanism and effects of remdesivir-induced developmental toxicity in zebrafish: Blood flow dysfunction and behavioral alterations.

The antiviral drug remdesivir has been used to treat the growing number of coronavirus disease 2019 (COVID-19) patients. However, the drug is mainly excreted through urine and feces and introduced into the environment to affect non-target organisms, including fish, which has raised concerns about potential ecotoxicological effects on aquatic organisms. Moreover, studies on the ecological impacts of remdesivir on aquatic environments have not been reported. Here, we aimed to explore the toxicological impacts of microinjection of remdesivir on zebrafish early embryonic development and larvae and the associated mechanism. We found that 100 µM remdesivir delayed epiboly and impaired convergent movement of embryos during gastrulation, and dose-dependent increases in mortality and malformation were observed in remdesivir-treated embryos. Moreover, 10-100 µM remdesivir decreased blood flow and swimming velocity and altered the behavior of larvae. In terms of molecular mechanisms, 80 differentially expressed genes (DEGs) were identified by transcriptome analysis in the remdesivir-treated group. Some of these DEGs, such as manf, kif3a, hnf1ba, rgn, prkcz, egr1, fosab, nr4a1, and ptgs2b, were mainly involved in early embryonic development, neuronal developmental disorders, vascular disease and the blood flow pathway. These data reveal that remdesivir can impair early embryonic development, blood flow and behavior of zebrafish embryos/larvae, probably due to alterations at the transcriptome level. This study suggests that it is important to avoid the discharge of remdesivir to aquatic ecosystems and provides a theoretical foundation to hinder remdesivir-induced ecotoxicity to aquatic environments.

Metabolite profile of COVID-19 revealed by UPLC-MS/MS-based widely targeted metabolomics

The metabolic characteristics of COVID-19 disease are still largely unknown. Here, 44 patients with COVID-19 (31 mild COVID-19 patients and 13 severe COVID-19 patients), 42 healthy controls (HC), and 42 patients with community-acquired pneumonia (CAP), were involved in the study to assess their serum metabolomic profiles. We used widely targeted metabolomics based on an ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). The differentially expressed metabolites in the plasma of mild and severe COVID-19 patients, CAP patients, and HC subjects were screened, and the main metabolic pathways involved were analyzed. Multiple mature machine learning algorithms confirmed that the metabolites performed excellently in discriminating COVID-19 groups from CAP and HC subjects, with an area under the curve (AUC) of 1. The specific dysregulation of AMP, dGMP, sn-glycero-3-phosphocholine, and carnitine was observed in the severe COVID-19 group. Moreover, random forest analysis suggested that these metabolites could discriminate between severe COVID-19 patients and mild COVID-19 patients, with an AUC of 0.921. This study may broaden our understanding of pathophysiological mechanisms of COVID-19 and may offer an experimental basis for developing novel treatment strategies against it.

Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images.

BACKGROUND: Corona Virus Disease 2019 (COVID-19) first appeared in December 2019, and spread rapidly around the world. COVID-19 is a pneumonia caused by novel coronavirus infection in 2019. COVID-19 is highly infectious and transmissible. By 7 May 2021, the total number of cumulative number of deaths is 3,259,033. In order to diagnose the infected person in time to prevent the spread of the virus, the diagnosis method for COVID-19 is extremely important. To solve the above problems, this paper introduces a Multi-Level Enhanced Sensation module (MLES), and proposes a new convolutional neural network model, MLES-Net, based on this module. METHODS: Attention has the ability to automatically focus on the key points in various information, and Attention can realize parallelism, which can replace some recurrent neural networks to a certain extent and improve the efficiency of the model. We used the correlation between global and local features to generate the attention mask. First, the feature map was divided into multiple groups, and the initial attention mask was obtained by the dot product of each feature group and the feature after the global pooling. Then the attention masks were normalized. At the same time, there were two scaling and translating parameters in each group so that the normalize operation could be restored. Then, the final attention mask was obtained through the sigmoid function, and the feature of each location in the original feature group was scaled. Meanwhile, we use different classifiers on the network models with different network layers. RESULTS: The network uses three classifiers, FC module (fully connected layer), GAP module (global average pooling layer) and GAPFC module (global average pooling layer and fully connected layer), to improve recognition efficiency. GAPFC as a classifier can obtain the best comprehensive effect by comparing the number of parameters, the amount of calculation and the detection accuracy. The experimental results show that the MLES-Net56-GAPFC achieves the best overall accuracy rate (95.27%) and the best recognition rate for COVID-19 category (100%). CONCLUSIONS: MLES-Net56-GAPFC has good classification ability for the characteristics of high similarity between categories of COVID-19 X-Ray images and low intra-category variability. Considering the factors such as accuracy rate, number of network model parameters and calculation amount, we believe that the MLES-Net56-GAPFC network model has better practicability.

Crowdsourced Data for Physical Activity-Built Environment Research: Applying Strava Data in Chengdu, China.

The lack of physical activity has become a rigorous challenge for many countries, and the relationship between physical activity and the built environment has become a hot research topic in recent decades. This study uses the Strava Heatmap (novel crowdsourced data) to extract the distribution of cycling and running tracks in central Chengdu in December 2021 (during the COVID-19 pandemic) and develops spatial regression models for numerous 500 × 500 m grids (N = 2,788) to assess the impacts of the built environment on the cycling and running intensity indices. The findings are summarized as follows. First, land-use mix has insignificant effects on the physical activity of residents, which largely contrasts with the evidence gathered from previous studies. Second, road density, water area, green space area, number of stadiums, and number of enterprises significantly facilitate cycling and running. Third, river line length and the light index have positive associations with running but not with cycling. Fourth, housing price is positively correlated with cycling and running. Fifth, schools seem to discourage these two types of physical activities during the COVID-19 pandemic. This study provides practical implications (e.g., green space planning and public space management) for urban planners, practitioners, and policymakers.

Occurrence and risk assessment of typical PPCPs and biodegradation pathway of ribavirin in wastewater treatment plants.

A large number of pharmaceuticals and personal care products (PPCPs) persist in wastewater, and the consumption of PPCPs for COVID-19 control and prevention has sharply increased during the pandemic. This study investigated the occurrence, removal efficiency, and risk assessment of six typical PPCPs commonly used in China in two wastewater treatment plants (WWTPs). Ribavirin (RBV) is an effective pharmaceutical for severely ill patients with COVID-19, and the possible biodegradation pathway of RBV by activated sludge was discovered. The experimental results showed that PPCPs were detected in two WWTPs with a detection rate of 100% and concentrations ranging between 612 and 2323 ng L-1. The detection frequency and concentrations of RBV were substantially higher, with a maximum concentration of 314 ng L-1. Relatively high pollution loads were found for the following PPCPs from influent: ibuprofen > ranitidine hydrochloride > RBV > ampicillin sodium > clozapine > sulfamethoxazole. The removal efficiency of PPCPs was closely related to adsorption and biodegradation in activated sludge, and the moving bed biofilm reactor (MBBR) had a higher removal capacity than the anoxic-anaerobic-anoxic-oxic (AAAO) process. The removal efficiencies of sulfamethoxazole, ampicillin sodium, ibuprofen, and clozapine ranged from 92.21% to 97.86% in MBBR process and were relatively low, from 61.82% to 97.62% in AAAO process, and the removal of RBV and ranitidine hydrochloride were lower than 42.96% in both MBBR and AAAO processes. The discrepancy in removal efficiency is caused by temperature, hydrophilicity, and hydrophobicity of the compound, and acidity and alkalinity. The transformation products of RBV in activated sludge were detected and identified, and the biodegradation process of RBV could be speculated as follows: first breaks into TCONH2 and an oxygen-containing five-membered heterocyclic ring under the nucleosidase reaction, and then TCONH2 is finally formed into TCOOH through amide hydrolysis. Aquatic ecological risks based on risk quotient (RQ) assessment showed that PPCPs had high and medium risks in the influent, and the RQ values were all reduced after MBBR and AAAO treatment. Ranitidine hydrochloride and clozapine still showed high and medium risks in the effluent, respectively, and thus presented potential risks to the aquatic ecosystem.

Evolutionary Game Analysis of E-Commerce Intellectual Property Social Cogovernance with Collective Organizations

China’s E-commerce market is very active. Despite the impact of COVID-19, the market has ushered in major development opportunities. Alongside, the level of intellectual property protection in China is constantly improving. However, there are relatively few studies on intellectual property protection in the field of E-commerce. This study introduces the theory of social cogovernance and explores the construction of China’s E-commerce intellectual property protection system with the participation of collective organizations. Evolutionary game method is applied to model construction. Through numerical simulation, the study analyzes the evolution of E-commerce platform and collective organization strategic choice. The results depict that improving the open space provided by E-commerce platforms for collective organizations and strengthening their public service capabilities can promote the construction of social cogovernance systems. The establishment of reasonable reward and punishment mechanisms can increase cooperation enthusiasm. We also discuss the theoretical and practical implications for governments, E-commerce platforms, and collective organizations, to improve the level of intellectual property protection in E-commerce. This study can provide suggestions for China’s E-commerce intellectual property protection, improve the overall level of intellectual property protection in China, and provide a reference for global E-commerce intellectual property protection.

The Comparison of the Effects between Continuous and Intermittent Energy Restriction in Short-Term Bodyweight Loss for Sedentary Population: A Randomized, Double-Blind, Controlled Trial.

OBJECTIVE: To compare the effects of continuous energy restriction (CER) and intermittent energy restriction (IER) in bodyweight loss plan in sedentary individuals with normal bodyweight and explore the influence factors of effect and individual retention. METHODS: 26 participants were recruited in this randomized controlled and double-blinded trial and allocated to CER and IER groups. Bodyweight (BW), body mass index (BMI), and resting metabolic rate (RMR) would be collected before and after a 4-week (28 days) plan which included energy restriction (CER or IER) and moderate-intensity exercise. Daily intake of three major nutrients (protein, carbohydrate, fat) and calories were recorded. RESULTS: A significant decrease in BW and BMI were reported within each group. No statistically significant difference in the change of RMR in CERG. No statistically significant difference was reported in the effect between groups, neither as well the intake of total calories, three major nutrients, and individual plan retention. The influence factors of IER and CER are different. CONCLUSION: Both CER and IER are effective and safe energy restriction strategies in the short term. Daily energy intake and physical exercise are important to both IER and CER.

How Confirmation Bias Influences Risk and Contingency Management: Lessons From Global Leaders' Responses to the 2020 Pandemic

Abundant research has documented the negative effects of confirmatory bias in a broad range of managerial decision-making contexts. However, insufficient research has been conducted on the effect of confirmatory bias on risk and contingency management. This article addresses the research gap by examining how confirmatory bias may affect global leaders’ responses to the COVID-19 pandemic. Based on a comprehensive literature review of prior research and empirical evidence, the authors illustrate the relationship between confirmatory bias and organizational performance in disaster management and identify several important mediators and moderators, which influence the relationship between confirmatory bias and the performance of human collectives in disaster management.

Lung Ultrasound in COVID-19 Patients: Characteristics and Factors Affecting its Diagnostic Accuracy (preprint)

Background: Although chest computed tomography (CT) is the gold standard for diagnosing the majority of lung conditions, its use in screening patients for coronavirus disease 2019(COVID-19) pneumonia is not recommended. Lung ultrasound (LUS) is an alternative modality. To investigate the characteristics and diagnostic accuracy (DA) of bedside ultrasound for lung lesions in patients with COVID-19 and to determine the factors influencing the DA of lung ultrasound (LUS). Methods: A total of 330 patients with COVID-19 admitted to the hospital between February and March 2020 were retrospectively recruited. The imaging characteristics of LUS and computed tomography (CT) scans were analysed and summarized. DA was calculated using a chest CT scan as the reference standard. Furthermore, a binary logistic regression analysis was conducted to investigate the factors influencing the DA of LUS for interstitial syndrome. Results: The ultrasound findings of COVID-19 patients presented mainly as B lines (195/330, 59.1%), unsmooth or interrupted pleural lines (118/330, 35.8%), consolidation lesions (74/330, 22.4%), and pleural effusion (11/330, 3.33%). Compared with the chest CT scan, the DA of LUS for interstitial syndrome, consolidation, pleural effusion, and pleural thickening were 0.821, 0.927, 0.988, and 0.863, respectively. The diagnostic coincidence rate of LUS and chest CT in the mild, common, severe, and critical groups were 93%, 68.6%, 100%, and 100%, respectively. According to the results of the binary logistic regression, sex, disease duration, experience of the doctor, and involved lobes were independent predictors of the DA for interstitial syndrome. Conclusions: LUS had good diagnostic performance for diagnosing COVID-19 pneumonia, and showed a relatively low DA for interstitial syndrome. Female sex, doctors with less experience, long disease duration, and lesions limited to the upper or lower lobes may decrease the DA.