Gaining Instead of Losing: The Image of Dunhuang as a Religious Heritage in a WeChat Mini-Programme

In 2020, a WeChat mini-programme called the Dunhuang E-Tour ((sic)) was launched during the COVID-19 pandemic to showcase one of China's most important religious heritage sites, the Dunhuang Mogao Grottoes (also known as the Dunhuang Caves), and it attracted a considerable number of online tourists. Unlike the colonial image of Dunhuang in Chinese public discourse, the mini-programme does not focus on Dunhuang's history;rather, it provides a dynamic and interactive representation of Dunhuang's religious murals, painted sculptures and cave architecture. To reflect the impact of the mini-programme's digital mechanisms on users' experience, this study adopts an analytical framework that combines the walkthrough method and religious tourist perspectives to explore the image of the digital Dunhuang and how it was shaped. The analysis finds that the functions of the Dunhuang E-Tour create a culturally rich image of Dunhuang, which subverts its decades-long Dunhuang image as a site of loss in Chinese public discourse. This difference in images mirrors the potential impact of China's recent cultural policy of 'cultural confidence' in relation to its cultural and creative industries.

Predicting the Impact of Change in Air Quality Patterns Due to COVID-19 Lockdown Policies in Multiple Urban Cities of Henan: A Deep Learning Approach

Several countries implemented prevention and control measures in response to the 2019 new coronavirus virus (COVID-19) pandemic. To study the impact of the lockdown due to COVID-19 on multiple cities, this study utilized data from 18 cities of Henan to understand the air quality pattern change during COVID-19 from 2019 to 2021. It examined the temporal and spatial distribution impact. This study firstly utilized a deep learning bi-directional long-term short-term (Bi-LSTM) model to predict air quality patterns during 3 periods, i.e., COVID-A (before COVID-19, i.e., 2019), COVID-B (during COVID-19, i.e., 2020), COVID-C (after COVID-19 cases, i.e., 2021) and obtained the R-2 value of more than 72% average in each year and decreased MAE value, which was better than other studies' deep learning methods. This study secondly focused on the change of pollutants and observed an increase in Air Quality Index by 10%, a decrease in PM2.5 by 14%, PM10 by 18%, NO2 by 14%, and SO2 by 16% during the COVID-B period. This study found an increase in O-3 by 31% during the COVID-C period and observed a significant decrease in pollutants during the COVID-C period (PM10 by 42%, PM2.5 by 97%, NO2 by 89%, SO2 by 36%, CO by 58%, O-3 by 31%). Lastly, the impact of lockdown policies was studied during the COVID-B period and the results showed that Henan achieved the Grade I standards of air quality standards after lockdown was implemented. Although there were many severe effects of the COVID-19 pandemic on human health and the global economy, lockdowns likely resulted in significant short-term health advantages owing to reduced air pollution and significantly improved ambient air quality. Following COVID-19, the government must take action to address the environmental problems that contributed to the deteriorating air quality.

Preliminary Recommendations on the Timing of Lung Surgery after Novel Coronavirus Infection in Patients with Pulmonary Nodules and Lung Cancer.

In recent years, the corona virus disease 2019 (COVID-19) pandemic has had a huge impact on the global medical, political and economic fields. Since the beginning of the COVID-19 epidemic, our understanding of the impact of COVID-19 has grown exponentially. Recently, the COVID-19 epidemic has changed rapidly in China, and there has been controversy over how to carry out surgical operations for patients with lung neoplastic lesions. Some studies have shown that lung cancer patients undergoing surgery are more likely to experience respiratory failure and perioperative death after contract-ing COVID-19 than the general population, however, delays in cancer treatment are also associated with increased mortality among these patients. In particular, the novel coronavirus Omikron variant has a higher transmissibility and may escape the immunity obtained through the previous novel coronavirus infection and vaccination. In order to minimize the risk of novel coronavirus infection in surgical patients, it is necessary to develop new treatment guidelines, expert consensus and preventive measures. However, the current rapid change of the epidemic situation has led to insufficient time and evidence to develop guidelines and consensus. Therefore, thoracic surgeons need to evaluate specific patient populations at higher risk of severe complications before surgery and weigh the benefit of surgical treatment against the risk of novel coronavirus infection. We try to give some recommendations on lung surgery during the current domestic epidemic situation based on the guidelines and consensus of oncology and thoracic surgery organizations in different regions on lung surgery.Copyright © 2023, Chinese Journal of Lung Cancer. All rights reserved.

Preliminary Recommendations on the Timing of Lung Surgery after Novel Coronavirus Infection in Patients with Pulmonary Nodules and Lung Cancer.

In recent years, the corona virus disease 2019 (COVID-19) pandemic has had a huge impact on the global medical, political and economic fields. Since the beginning of the COVID-19 epidemic, our understanding of the impact of COVID-19 has grown exponentially. Recently, the COVID-19 epidemic has changed rapidly in China, and there has been controversy over how to carry out surgical operations for patients with lung neoplastic lesions. Some studies have shown that lung cancer patients undergoing surgery are more likely to experience respiratory failure and perioperative death after contract-ing COVID-19 than the general population, however, delays in cancer treatment are also associated with increased mortality among these patients. In particular, the novel coronavirus Omikron variant has a higher transmissibility and may escape the immunity obtained through the previous novel coronavirus infection and vaccination. In order to minimize the risk of novel coronavirus infection in surgical patients, it is necessary to develop new treatment guidelines, expert consensus and preventive measures. However, the current rapid change of the epidemic situation has led to insufficient time and evidence to develop guidelines and consensus. Therefore, thoracic surgeons need to evaluate specific patient populations at higher risk of severe complications before surgery and weigh the benefit of surgical treatment against the risk of novel coronavirus infection. We try to give some recommendations on lung surgery during the current domestic epidemic situation based on the guidelines and consensus of oncology and thoracic surgery organizations in different regions on lung surgery.Copyright © 2023, Chinese Journal of Lung Cancer. All rights reserved.

Investor sentiment based on scaled PCA method: A powerful predictor of realized volatility in the Chinese stock market

In this study, we construct an investor sentiment indicator (SsPCA) to predict stock volatility in the Chinese stock market by applying the scaled principal component analysis (sPCA). As a new dimension reduction technique for supervised learning, sPCA is employed to extract useful information from six individual sentiment proxies and obtain the common variations to characterize the investor sentiment (SsPCA). The empirical results indicate that SsPCA is a significant and powerful volatility predictor both in and out of sample. We also employ the partial least squares (PLS)-based investor sentiment index, three extra sentiment measures in past studies, and six individual sentiment proxies for comparison, and find SsPCA outperforms them on predicting stock volatility in the Chinese stock market. More importantly, the predictability of SsPCA remains significant before and after the famous financial crises (the sub-prime mortgage crisis and Chinese stock market turbulence) and the spread of the pandemic (COVID-19). Additionally, our findings imply that SsPCA still plays an essential role in predicting sock volatility after considering the leverage effect. The robustness of SsPCA in volatility forecasting is further verified in various industry indices of the Chinese stock market. Finally, we state that the strong predictability of SsPCA is highly related to its dimensionality reduction. Our results indicate that SsPCA is a robust volatility predictor from various aspects and performs better compared with existing sentiment indicators. © 2022 Elsevier Inc.

An RFID-Based Battery-Less Vibration Monitoring System for Electrical Appliances

This paper presents a vibration monitoring system for electrical appliances. This system is based on RFID sensors and edge processing technologies. For long-term monitoring, two different operation modes referred to as standby and active modes are introduced. The difference between the two modes is radio wave radiation times. The standby mode is useful to reduce energy consumption and temperature increase of an RFID reader, and amount of data uploaded to a network. This mode also detects a beginning of a vibration event caused by the motor of an electrical appliance. The standby mode subsequently triggers the active mode. The active mode accurately monitors the vibration event and keeps the measured data only for the active mode. Experiments for monitoring a refrigerator demonstrate that the proposed modes enable efficient vibration detections. This system can prevent unintended COVID-19 vaccine disposals caused by the problematic operation and management of refrigerators. © 2022 IEEE.

Optimizing the supply of essential goods under closed-off management: A case study of COVID-19

The COVID-19 pandemic is threatening people's health and economic development around the world. Many areas and cities are opting for actions such as lockdown or closed-off management to prevent rapid spread of the virus. Under these actions, it is necessary and important to guarantee the supply of essential goods. In this paper, we develop a multi-objective optimization model to optimize the supply of essential goods under closed-off management. A mixed binary integer programming model is built to help identify the optimal safety stock level as well as the supply network so that the demands for essential goods could be guaranteed. The effectiveness of our method is demonstrated by an example. We also perform sensitivity analysis for key parameters. Our work provides a reasonable solution for the supply of essential goods under closed-off management. Copyright (C) 2022 The Authors.

Research on dynamic matching model of electric vehicles and charging facilities in China: A case study of taxis in Beijing

Charging infrastructure supports the rapid development of China's new energy vehicle industry. It not only plays a decisive role in providing accessible and convenient services for electric vehicle (EV) users but also, in one of the seven new infrastructure areas, plays an important role in stabilizing growth and unleashing economic potential during the new coronavirus (COVID-19) pandemic, impacting China's economy. In this study, the system dynamics model was used to predict the development of the EV industry and the demand for charging infrastructure, while considering the influence of policy, increase in EV mileage, and consumer purchase intention index. Furthermore, using the matching of EVs and charging infrastructure in Beijing and policy-oriented sensitivity analysis, a simulation of the construction of battery swap taxis and power stations under three policy scenarios was conducted. This research shows that with policies implemented to support charging infrastructure and swapping compatible taxis, Beijing can achieve its goal of replacing all EVs with fast-swap batteries and fast-charging functions within three years.

Camera-Based Security Check for Face Mask Detection using Deep Learning

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, Deep Learning algorithms are proposed to develop a comprehensive mask detection through a camera. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms that are used to classify the features in each scenario. This project uses multiple open datasets, Google Image Search, and simulated photos. We aim to build a comprehensive camera system that is able to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. © 2021 IEEE.

Bidirectional GRU networks-based next POI category prediction for healthcare

The Corona Virus Disease 2019 has a great impact on public health and public psychology. People stay at home for a long time and rarely go out. With the improvement of the epidemic situation, people began to go to different places to check in. To maintain public mental health, it is necessary to propose a point-of-interest (POI) prediction model which can mine users' interests. However, the current techniques suffer from lower precision during prediction and the practical value is poor, which is due to the sparse data of users' check-in. Faced with this challenge, we propose an attention-based bidirectional gated recurrent unit (GRU) model for POI category prediction (ABG_poic). We regard the user's POI category as the user's interest preference because the fuzzy POI category is easier to reflect the user's interest than the POI. This method can alleviate the data sparsity, and protect users' location privacy. Since users' preferences are variable, we utilize a bidirectional GRU to capture the dynamic dependence of users' check-ins. Furthermore, since the neural network is similar to a “black box” in feature learning, the decision-making stage is opaque. Thus, we combine the attention mechanism with bidirectional GRU to selectively focus on historical check-in records, which can improve the interpretability of the model. Considering the time impact on users' check-in, we utilize the time sliding window in the ABG_poic model. Experiments on two data sets demonstrate that our ABG_poic outperforms the comparison models for POI category prediction on sparse check-in data. © 2021 Wiley Periodicals LLC

Detection of SARS-CoV-2 in Wastewater: Community Variability, Temporal Dynamics, and Genotype Diversity

A systematic investigation of SARS-CoV-2 genome concentrations in nine Southern California wastewater treatment plants (WWTPs) was carried out. The goal was to compare the variability of the viral concentration among different WWTPs that serve different communities and to correlate the concentration of virus in wastewater with the community level COVID-19 epidemic. The results showed that there were significant differences in viral concentration detected in different plants. However, the variability of SARS-CoV-2 concentration cannot easily be explained by the COVID-19 case reports because a large number of sewer-shed characteristics have important influences on the quantifiable viral concentration in the sewage samples. The results from year-long tracking of SARS-CoV-2 concentration in two large WWTPs, however, matched fairly well with the county level COVID-19 case reports. This suggests continuous monitoring of SARS-CoV-2 in the same treatment plant can offer insights into community level trends of COVID-19 and can serve as an alert for a possible resurgence of the disease. Amplicon-based sequencing targeting two SARS-CoV-2 single nucleotide polymorphism (SNP) mutations was shown as a proof-of-concept for identifying SARS-CoV-2 variants in wastewater. The outcomes of this research contribute to the advancement of wastewater-based epidemiology for COVID-19 and other infectious diseases.

Current global status and future development of traditional Chinese medicine in the prevention and treatment of coronavirus disease 2019

Recognizing the potential of globalizing traditional Chinese medicine (TCM) for coronavirus disease 2019 (COVID-19), this review summarizes the current global use of TCM for COVID-19, status of the internationalization of TCM, and strategy on globalizing TCM for COVID-19. COVID-19 is a novel coronavirus disease that has sparked a pandemic in March 2020 affecting over 110 countries. Although researchers have been racing to develop targeted anti-COVID-19 treatment and vaccine, they are still currently unavailable. Thus, in treating patients infected with COVID-19, western medicine mainly recommended symptomatic treatment and monitoring. Existing drugs are used in the context of clinical trials, and the FDA still not yet approved these drugs for COVID-19. On the other hand, TCM proposed herbal treatment for suspected individuals to take within their observation period, as well as for confirmed COVID-19 patients to take during active phase of disease and during recovery. In China, Integrative Chinese-Western Medicine had been used in treating 91.5% of COVID-19 patients and showed its strength in reducing disease severity, shortening recovery time, and lowering the mortality rate. Despite obstacles in globalizing TCM for the treatment of COVID-19, promoting appropriate use of locally registered TCM products may prevent disease in susceptible individuals, improve the quality of life, and assist in the recovery of COVID-19 patients. With increasing global acceptance, TCM may play a more important role in the battle against infectious disease in the future. ©2021 World Journal of Traditional Chinese Medicine ;Published by Wolters Kluwer . Medknow.

Mechanism prediction of Zhikepipa Mixture in the treatment of COVID-19

Objective: To explore the effect of Zhikepipa Mixture on the treatment of COVID-19 through network pharmacology analysis and molecular docking technology. Methods: First, we performed the network pharmacology method to screen active compounds and targets so as to explore the mixture's potential mechanisms in the treatment of COVID-19. In line with ADME screening index, like oral bioavailability (OB) ≥30% or drug likeness index (DL) ≥0.18, the active compounds against COVID-19 related targets were selected to construct the 'herb-compound-target' network. Mechanism prediction of Zhikepipa mixture in the treatment of COVID-19 was analyzed by the interaction of the target sites, the bioinformatic annotation, and the metabolic pathway. Then, we used a molecular docking model to evaluate the binding ability between active compounds and 2019-nCoV (SARS-CoV-2) 3-chymotrypsin-like cysteine protease (3CLpro) receptor-binding domain (PBD ID 6LU7), which was involved in mediating viral replication and transcription functions. Results: The'herb-compound-target' network showed 34 key compounds and 30 disease targets after overlapping with disease targets. The network topology analysis showed that those selected compounds with higher degree would produce marked anti-inflammatory effects by regulating 30 targets like PTGS1, IL1B, IL6, IL10, CXCL8 and JUN. AGE-RAGE signaling pathway, IL-17 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, and MAPK signaling pathway were involved in regulating hepatitis B and diabetic complications. In addition, Folium eriobotryae and Radix stemonae played important roles in the network. The molecular docking results showed that nine compounds were identified with higher docking score rank against 2019-nCoV 3CLpro protease, and most of them were attributed to flavonoids. Conclusion: Zhikepipa Mixture could exhibit both anti-inflammatory and anti-virus actions through multi-component, multi-target, and multi-pathway. © 2021, Editorial Board of Journal of Xi'an Jiaotong University (Medical Sciences). All right reserved.

Blood molecular markers associated with COVID-19 immunopathology and multi-organ damage

COVID-19 is characterized by dysregulated immune responses, metabolic dysfunction and adverse effects on the function of multiple organs. To understand host responses to COVID-19 pathophysiology, we combined transcriptomics, proteomics, and metabolomics to identify molecular markers in peripheral blood and plasma samples of 66 COVID-19-infected patients experiencing a range of disease severities and 17 healthy controls. A large number of expressed genes, proteins, metabolites, and extracellular RNAs (exRNAs) exhibit strong associations with various clinical parameters. Multiple sets of tissue-specific proteins and exRNAs varied significantly in both mild and severe patients suggesting a potential impact on tissue function. Chronic activation of neutrophils, IFN-I signaling, and a high level of inflammatory cytokines were observed in patients with severe disease progression. In contrast, COVID-19-infected patients experiencing milder disease symptoms showed robust T-cell responses. Finally, we identified genes, proteins, and exRNAs as potential biomarkers that might assist in predicting the prognosis of SARS-CoV-2 infection. These data refine our understanding of the pathophysiology and clinical progress of COVID-19. SYNOPSIS image Proteomics, metabolomics and RNAseq data map immune responses in COVID-19 patients with different disease severity, revealing molecular makers associated with disease progression and alterations of tissue-specific proteins. A multi-omics profiling of the host response to SARS-CoV2 infection in 66 clinically diagnosed and laboratory confirmed COVID-19 patients and 17 uninfected controls. Significant correlations between multi-omics data and key clinical parameters. Alteration of tissue-specific proteins and exRNAs. Enhanced activation of immune responses is associated with COVID-19 pathogenesis. Biomarkers to predict COVID-19 clinical outcomes pending clinical validation as prospective marker.

The Course of Mild and Moderate COVID-19 Infections-The Unexpected Long-Lasting Challenge

Background. The course of disease in mild and moderate COVID-19 has many implications for mobile patients, such as the risk of spread of the infection, precautions taken, and investigations targeted at preventing transmission. Methods. Three hundred thirty-one adults were hospitalized from January 21 to February 22, 2020, and classified as severe (10%) or critical (4.8%) cases;1.5% died. Two hundred eighty-two (85.2%) mild or moderate cases were admitted to regular wards. Epidemiological, demographic, clinical, chest computed tomography (CT) scan, laboratory, treatment, and outcome data from patient records were analyzed retrospectively. Results. Patients were symptomatic for 9.82 +/- 5.75 (1-37) days. Pulmonary involvement was demonstrated on a chest CT scan in 97.9% of cases. It took 16.81 +/- 8.54 (3-49) days from the appearance of the first symptom until 274 patients tested virus-negative in naso- and oropharyngeal (NP) swabs, blood, urine, and stool, and 234 (83%) patients were asymptomatic for 9.09 +/- 7.82 (1-44) days. Subsequently, 131 patients were discharged. One hundred sixty-nine remained in the hospital;these patients tested virus-free and were clinically asymptomatic because of widespread persisting or increasing pulmonary infiltrates. Hospitalization took 16.24 +/- 7.57 (2-47) days;the time interval from the first symptom to discharge was 21.37 +/- 7.85 (3-52) days. Conclusions. With an asymptomatic phase, disease courses are unexpectedly long until the stage of virus negativity. NP swabs are not reliable in the later stages of COVID-19. Pneumonia outlasts virus-positive tests if sputum is not acquired. Imminent pulmonary fibrosis in high-risk groups demands follow-up examinations. Investigation of promising antiviral agents should heed the specific needs of mild and moderate COVID-19 patients.

Evidence-Based Evaluation of Vitamin C and D in the Treatment and Prevention of Acute Viral Respiratory Infectious Disease

OBJECTIVE: To evaluate the evidence supporting vitamin C and D medication in the treatment and prevention of acute viral respiratory infectious disease, and to provide evidence for clinical rational medication and potential treatment option for COVID-19 especially. METHODS: PubMed database was retrieved from inception to February 13, 2020, and the references in the reviews and those not included in the database were manually retrieved. RESULTS: Eight literatures were included finally. In terms of COVID-19, MERS, and SARS, none literature about vitamin C and D medication was available. In terms of influenza, one prospective controlled trial proved vitamin C's efficacy. For vitamin D in influenza, one systematic review and one randomized controlled trial proved its efficacy, while three randomized controlled trials showed no efficacy. Additionally, one case-control study showed no statistical association between vitamin D level and the risk of influenza, and one randomized controlled trial showed no significant difference between high-dose and standard-dose vitamin D supplementation. CONCLUSION: Currently, no evidence supports the effectiveness of vitamin C and D medication in the prevention and treatment of COVID-19, MERS and SARS. However, limited evidence supports potential benefit of vitamin C and D medication in influenza. Physicians and pharmacists should take relevant clinical practice guidelines and supporting evidence into consideration when using vitamin C or D (especially high-dose medication), and pay attention to monitoring drug adverse reactions and drug interactions.

Principles of diagnosis and treatment of orthopaedic diseases during coronavirus disease 2019 (COVID-19)

The epidemic of corona virus disease 2019 (COVID-19) is still ongoing, and infections among health care workers are not uncommon. How to complete the diagnosis and treatment of orthopaedic diseases and prevent the spread of the epidemic is a great challenge for orthopaedic surgeons. On the basis of exposure history and medical conditions of orthopaedic patients, the hierarchical control is very important for reduction of the exposure for the medical staff and patients. In order to ensure medical safety and reduce the consumption of materials for the epidemic prevention, we classified the hospitalized orthopaedic patients into three classes and six levels, to provide effective references for all levels of hospitals in orthopaedic clinical work. Copyright © 2020 by the Chinese Medical Association.

Potential SARS-CoV-2 3CL protease inhibitors selection from TCMSP platform by molecular docking approach [Chinese]

Objective: To identify potential SARS-CoV-2 3CL protease inhibitors from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) by molecular docking approach. Method(s): To alternate extensive compounds experimental screening processes, a Computer-Aided Drug Design (CADD) based molecular docking technology was performed to explore existing drug repurposing possibilities. Molecular docking model with Schrodinger suit 2018 was used to evaluate the binding abilities between TCMSP 13 143 compounds and SARS-CoV-2 3CL protease receptor-binding domain (PBD ID 6LU7), which involving in mediating viral replication and transcription functions. According to the constructed docking system, potential compounds were screened according to docking score, oral bioavailability (OB), and drug-likeness (DL). At last, a compounds-herb-target organ-function network was constructed. Result(s): Compared with 6LU7 original ligand docking score (-7.734), a total of 498 compounds were identified with lower docking score against 6LU7 targets. These compounds were further reduced to 60 high-priority compounds, based on OB (more than 30) and DL (more than 0.18). Meanwhile, these 60 compounds were found to interact with the amino acid residues (GLU166, GLY143, ASP187, CYS145, GLN189, LEU141, etc.) which were critically involved in the 6LU7 domain mainly by hydrogen-bonded interaction. The network exploring results revealed that these potential compounds were mainly attributed to Glycyrrhizae Radix et Rhizoma, Mori Cortex, Rhododendron dauricum, Polygoni Cuspidati Rhizoma et Radix, and Plantaginis Herba, etc., which associates with acute lung syndromes induced by SARS-CoV-2, with the effect of clearing heat and removing toxin, relieving cough and dispelling phlegm and lung-draining and relieving asthma. Conclusion(s): Molecular docking method provides a useful tool for the screening of SARS-CoV-2 3CL protease inhibitors from TCMSP platform. Copyright © 2020, Editorial Office of Chinese Traditional and Herbal Drugs. All right reserved.

Emergency management of 2019 novel coronavirus: implications for the dermatology department.

Since cases first emerged in December 2019, COVID-19 (a type of coronavirus) has rapidly become pandemic. This fast-tracked paper (published quickly) from China on COVID-19 is written by dermatologists at the epicentre of the outbreak in Wuhan. Dermatology clinic staff may be at risk because protective equipment is not routinely available, and skin lesions might possibly transmit the virus indirectly. These authors suggest preventive measures based on experience in this and previous coronavirus outbreaks. Online consultation for non-urgent patients reduces the numbers of patients attending clinics. Nurse-led triage, to identify patients with possible COVID-19, at the entrances of hospital and skin clinics directs patients with a cough or fever to a specific COVID-19 area and a dermatologist is consulted if the fever might be related to skin disease. Clinic staff wear N95 masks and observe hand hygiene during consultations. Patients are admitted to a ward only if routine blood tests and chest CT scans exclude COVID-19. Triage will not detect patients who are showing no symptoms but who are developing the disease, so the hospital should provide an on-call expert team to discuss inpatients suspected or diagnosed with COVID-19 and refer them to radiology, respiratory or intensive care colleagues as required. Confirmed cases are managed following local policies. Skin disorders in COVID-19 inpatients can usually be managed remotely using photographs, email and teleconferencing. If necessary a multidisciplinary team (a team of medical staff from different specialties) can meet in the clean area of the isolation ward. If the dermatologist must see the patient, all records should be provided in advance to minimise exposure time. With these precautions, as of 20th February 2020 no infected patients were detected in the Wuhan Dermatology Department. This is a summary of the study: Emergency management for preventing and controlling nosocomial infection of 2019 novel coronavirus: implications for the dermatology department.