NLRP3 Inflammasome: A key contributor to the inflammation formation.

Inflammation is an important part of the development of various organ diseases. The inflammasome, as an innate immune receptor, plays an important role in the formation of inflammation. Among various inflammasomes, the NLRP3 inflammasome is the most well studied. The NLRP3 inflammasome is composed of skeletal protein NLRP3, apoptosis-associated speck-like protein (ASC) and pro-caspase-1. There are three types of activation pathways: (1) "classical" activation pathway; (2) "non-canonical" activation pathway; (3) "alternative" activation pathway. The activation of NLRP3 inflammasome is involved in many inflammatory diseases. A variety of factors (such as genetic factors, environmental factors, chemical factors, viral infection, etc.) have been proved to activate NLRP3 inflammasome and promote the inflammatory response of the lung, heart, liver, kidney and other organs in the body. Especially, the mechanism of NLRP3 inflammation and its related molecules in its associated diseases remains not to be summarized, namely they may promote or delay inflammatory diseases in different cells and tissues. This article reviews the structure and function of the NLRP3 inflammasome and its role in various inflammations, including inflammations caused by chemically toxic substances.

[Management of Obstructive Sleep Apnea Syndrome during the Coronavirus Disease 2019 Pandemic].

The coronavirus disease 2019(COVID-19) pandemic poses a severe threat to global health.As an emerging infectious disease mainly attacking the respiratory tract,it has severely challenged the management of chronic non-infectious respiratory diseases including obstructive sleep apnea(OSA) and asthma.This article reviews the impact of OSA on the incidence of COVID-19 and the underlying pathophysiological mechanism,as well as the effects of OSA on the hospitalization risk and the prognosis and outcome of COVID-19 patients,which will provide novel ideas for the management of OSA during the COVID-19 pandemic.

Sleep changes in Taiwanese first graders before and after school closure during the COVID-19 pandemic.

PURPOSE: The objective of this study was (1) to examine sleep changes in first graders before and after school closure and (2) to examine the association between parental work rearrangement and children's sleep change during the coronavirus disease 2019 (COVID-19) pandemic. DESIGN AND METHODS: This was an observational study. The children's sleep habit questionnaire was completed by 103 parents of first-graders before and after school closure. Paired t-test and the general linear model were applied to data analysis. RESULTS: Children delayed their bedtime and rising time, but total sleep duration increased. Moreover, parents who rearranged their work during the pandemic perceived more child parasomnia symptoms (p = .029) and less delayed sleep-wake patterns in their children. PRACTICAL IMPLICATION: Sleep is an indicator that reflects children's behavioral changes in response to the COVID-19 pandemic. As routine changes, parents should be aware of child's parasomnia symptoms. Nursing interventions could aim at promoting sufficient external cues in the daytime during home confinement.

Urban park use and self-reported physical, mental, and social health during the COVID-19 pandemic: An on-site survey in Beijing, China.

The COVID-19 pandemic created unprecedented challenges for people's health. Studies have demonstrated the positive impact of urban green spaces, particularly urban parks, on physical and mental health. However, few studies have evaluated social health, which is a component of human health, and more understanding of the relationship between urban parks and human health during the COVID-19 pandemic is required. This study examined the effects of urban parks on people's health using a canonical correlation model. Physical, mental, and social health were the dependent health variables, and five factors related to urban parks were the independent variables. This study investigated 22 urban parks inside the Forth Ring Road in Beijing, China using a questionnaire survey. The results demonstrated a positive association between urban parks and human health during the pandemic. Distance to the parks, park area, and park size were positively correlated with physical, mental, and social health. Furthermore, frequency and duration of visits to urban parks were positively associated with mental health and contact with neighbors. The health effects of urban park use varied with park types and locations' urbanization background. These findings can provide insights for health-oriented urban park planning and construction.

Overview of Neutralization Assays and International Standard for Detecting SARS-CoV-2 Neutralizing Antibody.

We aimed to review the existing literature on the different types of neutralization assays and international standards for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We comprehensively summarized the serological assays for detecting neutralizing antibodies against SARS-CoV-2 and demonstrated the importance of an international standard for calibrating the measurement of neutralizing antibodies. Following the coronavirus disease outbreak in December 2019, there was an urgent demand to detect neutralizing antibodies in patients or vaccinated people to monitor disease outcomes and determine vaccine efficacy. Therefore, many approaches were developed to detect neutralizing antibodies against SARS-CoV-2, such as microneutralization assay, SARS-CoV-2 pseudotype virus assay, enzyme-linked immunosorbent assay (ELISA), and rapid lateral flow assay. Given the many types of serological assays for quantifying the neutralizing antibody titer, the comparison of different assay results is a challenge. In 2020, the World Health Organization proposed the first international standard as a common unit to define neutralizing antibody titer and antibody responses against SARS-CoV-2. These standards are useful for comparing the results of different assays and laboratories.

Construction of a new chromosome-scale, long-read reference genome assembly for the Syrian hamster, Mesocricetus auratus.

BACKGROUND: The Syrian hamster (Mesocricetus auratus) has been suggested as a useful mammalian model for a variety of diseases and infections, including infection with respiratory viruses such as SARS-CoV-2. The MesAur1.0 genome assembly was generated in 2013 using whole-genome shotgun sequencing with short-read sequence data. Current more advanced sequencing technologies and assembly methods now permit the generation of near-complete genome assemblies with higher quality and greater continuity. FINDINGS: Here, we report an improved assembly of the M. auratus genome (BCM_Maur_2.0) using Oxford Nanopore Technologies long-read sequencing to produce a chromosome-scale assembly. The total length of the new assembly is 2.46 Gb, similar to the 2.50-Gb length of a previous assembly of this genome, MesAur1.0. BCM_Maur_2.0 exhibits significantly improved continuity, with a scaffold N50 that is 6.7 times greater than MesAur1.0. Furthermore, 21,616 protein-coding genes and 10,459 noncoding genes are annotated in BCM_Maur_2.0 compared to 20,495 protein-coding genes and 4,168 noncoding genes in MesAur1.0. This new assembly also improves the unresolved regions as measured by nucleotide ambiguities, where ∼17.11% of bases in MesAur1.0 were unresolved compared to BCM_Maur_2.0, in which the number of unresolved bases is reduced to 3.00%. CONCLUSIONS: Access to a more complete reference genome with improved accuracy and continuity will facilitate more detailed, comprehensive, and meaningful research results for a wide variety of future studies using Syrian hamsters as models.

The Relationship Between Knowledge, Attitude, Practice, and Fall Prevention for Childhood in Shanghai, China.

Background: Early childhood fall is a pressing global public health problem and one of the leading causes of child injury. China has a high proportion of children and a high burden of illness from falls. Therefore, educational interventions to prevent childhood fall would be beneficial. Methods: We used the outcome of knowledge, attitude and practice questionnaire, which was conducted by Pudong New District of Shanghai Municipal Government, to summarize demographic and baseline characteristics grouped by intervention or not, and analyzed descriptive statistics of continuous and categorical variables. A logistic stepwise function model was established to study the influence of different covariables on the degree of injury, and AIC/BIC/AICC was used to select the optimal model. Finally, we carried out single-factor analysis and established a multifactor model by the stepwise function method. Results: Attitude and actual behavior scores had significant differences. The intervention and control groups had 20.79 ± 3.20 and 20.39 ± 2.89 attitude scores, respectively. Compared to the control group (5.97 ± 1.32), the intervention group had higher actual behavior scores (5.75 ± 1.50). In the univariate analysis results, fathers' education level, mothers' education level, actual behavior and what cares for children had a significant influence on whether children got injured. In multivariate analysis, attitude had a positive influence on whether injured [odds ratio: 1.13 (1.05-1.21), P < 0.001]. Conclusion: Educational intervention for children and their guardians can effectively reduce the risk of childhood falls, and changes in behavior and attitude are the result of educational influence. Education of childhood fall prevention can be used as a public health intervention to improve children's health.

C. acnes qPCR-Based Antibiotics Resistance Assay (ACQUIRE) Reveals Widespread Macrolide Resistance in Acne Patients and Can Eliminate Macrolide Misuse in Acne Treatment.

Background: Macrolides have been widely used to treat moderate-to-severe acne for more than 50 years. However, the prevalent antibiotic resistance of Propionibacterium acnes, along with the absence of clinically available resistance tests, has made macrolide misuse a frequent occurrence around the globe, with serious consequences. Objective: We developed Cutibacterium acnes quantitative PCR (qPCR)-based antibiotics resistance assay (ACQUIRE) to enable fast and accurate detection of C. acnes macrolide resistance in clinical settings, representing an opportunity to administer antibiotics more wisely and improve the quality of care. Methods: A cross-sectional observational study (n = 915) was conducted to probe into the macrolide resistance of C. acnes in patients with acne. Results: The high sensitivity of ACQUIRE enabled us to reveal a much higher C. acnes 23S recombinant DNA (rDNA) point mutation rate (52%) and thus a higher macrolide resistance (75.5%) compared to previous reports. Carriage of ermX gene was discovered on 472 (53%) subjects, which concurs with previous studies. Conclusion: The macrolide resistance of C. acnes is much higher than previously reported. Integrating ACQUIRE into acne treatment modalities may eliminate macrolide misuse and achieve better clinical improvements.

Artificial intelligence computed tomography helps evaluate the severity of COVID-19 patients: A retrospective study.

BACKGROUND: Computed tomography (CT) is a noninvasive imaging approach to assist the early diagnosis of pneumonia. However, coronavirus disease 2019 (COVID-19) shares similar imaging features with other types of pneumonia, which makes differential diagnosis problematic. Artificial intelligence (AI) has been proven successful in the medical imaging field, which has helped disease identification. However, whether AI can be used to identify the severity of COVID-19 is still underdetermined. METHODS: Data were extracted from 140 patients with confirmed COVID-19. The severity of COVID-19 patients (severe vs. non-severe) was defined at admission, according to American Thoracic Society (ATS) guidelines for community-acquired pneumonia (CAP). The AI-CT rating system constructed by Hangzhou YITU Healthcare Technology Co., Ltd. was used as the analysis tool to analyze chest CT images. RESULTS: A total of 117 diagnosed cases were enrolled, with 40 severe cases and 77 non-severe cases. Severe patients had more dyspnea symptoms on admission (12 vs. 3), higher acute physiology and chronic health evaluation (APACHE) II (9 vs. 4) and sequential organ failure assessment (SOFA) (3 vs. 1) scores, as well as higher CT semiquantitative rating scores (4 vs. 1) and AI-CT rating scores than non-severe patients (P<0.001). The AI-CT score was more predictive of the severity of COVID-19 (AUC=0.929), and ground-glass opacity (GGO) was more predictive of further intubation and mechanical ventilation (AUC=0.836). Furthermore, the CT semiquantitative score was linearly associated with the AI-CT rating system (Adj R 2=75.5%, P<0.001). CONCLUSIONS: AI technology could be used to evaluate disease severity in COVID-19 patients. Although it could not be considered an independent factor, there was no doubt that GGOs displayed more predictive value for further mechanical ventilation.

Vaccination with SARS-CoV-2 spike protein lacking glycan shields elicits enhanced protective responses in animal models.

A major challenge to end the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is to develop a broadly protective vaccine that elicits long-term immunity. As the key immunogen, the viral surface spike (S) protein is frequently mutated, and conserved epitopes are shielded by glycans. Here, we revealed that S protein glycosylation has site-differential effects on viral infectivity. We found that S protein generated by lung epithelial cells has glycoforms associated with increased infectivity. Compared to the fully glycosylated S protein, immunization of S protein with N-glycans trimmed to the mono-GlcNAc-decorated state (SMG) elicited stronger immune responses and better protection for human angiotensin-converting enzyme 2 (hACE2) transgenic mice against variants of concern (VOCs). In addition, a broadly neutralizing monoclonal antibody was identified from SMG-immunized mice that could neutralize wild-type SARS-CoV-2 and VOCs with subpicomolar potency. Together, these results demonstrate that removal of glycan shields to better expose the conserved sequences has the potential to be an effective and simple approach for developing a broadly protective SARS-CoV-2 vaccine.

Target-Based Virtual Screening and LC/MS-Guided Isolation Procedure for Identifying Phloroglucinol-Terpenoid Inhibitors of SARS-CoV-2.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to more than 5 million deaths worldwide to date. Due to the limited therapeutic options so far available, target-based virtual screening with LC/MS support was applied to identify the novel and high-content compounds 1-4 with inhibitory effects on SARS-CoV-2 in Vero E6 cells from the plant Dryopteris wallichiana. These compounds were also evaluated against SARS-CoV-2 in Calu-3 cells and showed unambiguous inhibitory activity. The inhibition assay of targets showed that compounds 3 and 4 mainly inhibited SARS-CoV-2 3CLpro, with effective Kd values. Through docking and molecular dynamics modeling, the binding site is described, providing a comprehensive understanding of 3CLpro and interactions for 3, including hydrogen bonds, hydrophobic bonds, and the spatial occupation of the B ring. Compounds 3 and 4 represent new, potential lead compounds for the development of anti-SARS-CoV-2 drugs. This study has led to the development of a target-based virtual screening method for exploring the potency of natural products and for identifying natural bioactive compounds for possible COVID-19 treatment.

Machine learning for emerging infectious disease field responses.

Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the optimized allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study has aimed to cope with this challenge from the aspect of preventive medicine by exploiting machine learning technologies. The study has been based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. The experimental results revealed that the decision rules derived from the machine learning based prediction models can provide valuable guidelines for the healthcare policy makers to develop an effective vaccination strategy. Furthermore, in case the healthcare facilities are overwhelmed by patients with EID, which frequently occurred in the recent COVID-19 pandemic, the frontline physicians can incorporate the proposed prediction models to triage patients suffering minor symptoms without laboratory tests, which may become scarce during an EID disaster. In conclusion, our study has demonstrated an effective approach to exploit machine learning technologies to cope with the challenges faced during the outbreak of an EID.

Global COVID-19 pandemic trends and their relationship with meteorological variables, air pollutants and socioeconomic aspects.

Meteorological variables, air pollutants, and socioeconomic factors are associated with COVID-19 transmission. However, it is unclear what impact their interactions have on COVID-19 transmission, whether their impact on COVID-19 transmission is linear or non-linear, and where the inflexion points are. This study examined 1) the spatial and temporal trends in COVID-19 monthly infection rate of new confirmed cases per 100,000 people (Rn) in 188 countries/regions worldwide from March to November 2020; 2) the linear correlation between meteorological variables (temperature (T), rainfall (R), wind speed (WS), relative humidity (RH), air pressure (AP)), air pollutants (nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3)) and socioeconomic aspects (population density (PD), gross domestic product per capita (GDP), domestic general government health expenditure per capita (GHE)) and Rn, and 3) the interaction and non-linear effects of the different variables on Rn, based on GeoDetector and Boosted regression tree. The results showed that the global Rn had was spatially clustered, and the average Rn increased From March to November 2020. Global Rn was negatively correlated with meteorological variables (T, R, WS, AP) and positively correlated with air pollutants (NO2, SO2, O3) and socioeconomic aspects (GDP, GHE). The interaction of SO2 and O3, SO2 and RH, and O3 and T strongly affected Rn. The variables effect on COVID-19 transmission was non-linear, with one or more inflexion points. The findings of this work can provide a basis for developing a global response to COVID-19 for global sustainable development.

Reflections Based on Pollution Changes Brought by COVID-19 Lockdown in Shanghai.

COVID-19 and its variants have been changing the world. The spread of variants brings severe effects to the global economy and to human's lives and health, as well as to society. Lockdown is proven to be effective in stopping the spread. It also provides a chance to study natural environmental changes with humanity's limited interference. This paper aims to evaluate the impact of lockdown on five major airborne pollutants, i.e., NO2, SO2, O3, PM2.5 and PM10, in the three different functional regions of Chongming, Xuhui and Jinshan of Shanghai. Changes in the same pollutants from the three regions over the same/different periods were all studied and compared. Overall, the COVID-19 lockdown has changed pollutant concentrations in the long and short terms. Concentrations of four pollutants decreased, except for that of earth surface O3, which increased. SO2 had significant correlations with all other pollutants. PM2.5 and PM10 are more externally input than locally produced. NO2, SO2 and PM levels sharply reduced in Jinshan and Xuhui due to the limited usage of fossil fuel. Lockdown improved the air quality. People now have a chance to rethink the value of life and the harmony between economic progress and environmental protection. This is helpful to establish sustainable societies.

The characteristics and implications of epidemic reports during COVID-19 in the United States.

We summarized the basic practices and characteristics of epidemic reporting during the COVID-19 pandemic in the United States. Based on the analysis of the advantages and disadvantages of epidemic data reporting, we put forward some suggestions that should be used for reference and thus improve the epidemic data reports of infectious diseases.

Supervised Machine Learning for Risk Stratification of Influenza-Like Illness: A Model to Prioritize Emerging Infectious Disease Disaster Responses (preprint)

Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the best allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study aimed to exploit machine learning technologies to cope with this challenge. The study was based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. The experimental results revealed that the conventional decision tree (DT) models built with only 6 features, including age, gender, and four comorbidities, delivered the same level of prediction accuracy as the state-of-the-art deep neural network models built with 18 features. Accordingly, we further studied how to exploit the DT models with different sensitivity levels to determine patient triage and optimize medical resource allocation in different stages of an EID disaster to aid the frontline clinicians and policy-makers. In conclusion, our study demonstrated an approach to exploit machine learning technologies to cope with the challenges during the outbreak of an EID.

Predictive Significance of the Prognostic Nutritional Index (PNI) in Patients with Severe COVID-19.

BACKGROUND: The prognostic nutritional index (PNI) has been reported to significantly correlate with poor survival and postoperative complications in patients with various diseases, but its relationship with mortality in COVID-19 patients has not been addressed. METHOD: A multicenter retrospective study involving patients with severe COVID-19 was conducted to investigate whether malnutrition and other clinical characteristics could be used to stratify the patients based on risk. RESULTS: A total of 395 patients were included in our study, with 236 patients in the training cohort, 59 patients in the internal validation cohort, and 100 patients in the external validation cohort. During hospitalization, 63/236 (26.69%) and 14/59 (23.73%) patients died in the training and validation cohorts, respectively. PNI had the strongest relationships with the neutrophil-lymphocyte ratio (NLR) and lactate dehydrogenase (LDH) level but was less strongly correlated with the CURB65, APACHE II, and SOFA scores. The baseline PNI score, platelet (PLT) count, LDH level, and PaO2/FiO2 (P/F) ratio were independent predictors of mortality in COVID-19 patients. A nomogram incorporating these four predictors showed good calibration and discrimination in the derivation and validation cohorts. A PNI score less than 33.405 was associated with a higher risk of mortality in severe COVID-19 patients in the Cox regression analysis. CONCLUSION: These findings have implications for predicting the risk of mortality in COVID-19 patients at the time of admission and provide the first direct evidence that a lower PNI is related to a worse prognosis in severe COVID-19 patients.

[An Extrapulmonary Manifestation of Coronavirus Disease 2019:Olfactory Dysfunction].

In addition to acute respiratory symptoms,coronavirus disease 2019(COVID-19)could cause olfactory dysfunction,which becomes the only clinical manifestation of COVID-19 in some cases.We review the epidemiological characteristics,pathological mechanism,screening value,treatment and prognosis of olfactory dysfunction in patients with COVID-19,aiming to achieve an in-depth understanding of the early diagnosis,quarantine,scientific treatment and prognosis of COVID-19.

Impact of Glycosylation on SARS-CoV-2 Infection and Broadly Protective Vaccine Design (preprint)

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 167 million confirmed cases and over 3 million deaths so far. This global pandemic has led to great efforts directed toward the study of this virus and its infection mechanism as well as development of effective means to control this devastating infectious disease. Like many other viral surface proteins, the trimeric SARS-CoV-2 spike (S) protein is heavily glycosylated with 22 N- and 2 O-glycosites per monomer which are likely to influence S protein folding and evade host immune response. More than one million S protein sequences with over 1,000 sites of mutation in its 1,273 amino acids have been reported to the GISAID database, including the highly transmissible variant strains found in the UK and South Africa. This high frequency of transmission and mutation is a major challenge in the development of broadly protective vaccines to control the pandemic. We have studied the impact of glycosylation on receptor-ligand interaction through evaluation of ACE2 and S protein expressed in different cell lines. Of different S protein glycoforms, the one expressed from lung epithelial cells, the primary cells for infection, has more complex-type glycans and higher binding avidity to the receptor as compared with the S protein from HEK293T cells which have more high-mannose or hybrid-type glycoforms. We also found that most of the S protein glycosites are highly conserved and the glycosites at positions 801 and 1194 are essential for viral entry. In addition, the RBD of S1 and the HR regions of S2 contain most of highly conserved sequences, and removal of each glycosite on pseudotyped SARS-CoV-2 virus for evaluation of the impact on structure and function provides insights into the design of broadly protective vaccines. In an effort to develop such universal vaccines, we found that mice immunized with monoglycosylated S protein (Smg) elicited better antibody responses capable of neutralizing not only the wild type but also the variants from the UK and South Africa than those with the fully-glycosylated S protein (Sfg), and strikingly, Smg vaccination provides better survival for hACE2 transgenic mice when challenged with lethal dose of SARS-CoV-2. Moreover, using single B cell technology, we isolated a monoclonal antibody from Smg immunized mice which was also able to neutralize the wild type and variants, suggesting that removal of unnecessary glycans from S protein to better expose the highly conserved sequences is an effective approach to developing broadly protective vaccines against SARS-CoV-2 and variants.

Implications of cardiac markers in risk-stratification and management for COVID-19 patients.

BACKGROUND: COVID-19 has resulted in high mortality worldwide. Information regarding cardiac markers for precise risk-stratification is limited. We aim to discover sensitive and reliable early-warning biomarkers for optimizing management and improving the prognosis of COVID-19 patients. METHODS: A total of 2954 consecutive COVID-19 patients who were receiving treatment from the Wuhan Huoshenshan Hospital in China from February 4 to April 10 were included in this retrospective cohort. Serum levels of cardiac markers were collected after admission. Coronary artery disease diagnosis and survival status were recorded. Single-cell RNA-sequencing and bulk RNA-sequencing from different cohorts of non-COVID-19 were performed to analyze SARS-CoV-2 receptor expression. RESULTS: Among 2954 COVID-19 patients in the analysis, the median age was 60 years (50-68 years), 1461 (49.5%) were female, and 1515 (51.3%) were severe/critical. Compared to mild/moderate (1439, 48.7%) patients, severe/critical patients showed significantly higher levels of cardiac markers within the first week after admission. In severe/critical COVID-19 patients, those with abnormal serum levels of BNP (42 [24.6%] vs 7 [1.1%]), hs-TNI (38 [48.1%] vs 6 [1.0%]), α- HBDH (55 [10.4%] vs 2 [0.2%]), CK-MB (45 [36.3%] vs 12 [0.9%]), and LDH (56 [12.5%] vs 1 [0.1%]) had a significantly higher mortality rate compared to patients with normal levels. The same trend was observed in the ICU admission rate. Severe/critical COVID-19 patients with pre-existing coronary artery disease (165/1,155 [10.9%]) had more cases of BNP (52 [46.5%] vs 119 [16.5%]), hs-TNI (24 [26.7%] vs 9.6 [%], α- HBDH (86 [55.5%] vs 443 [34.4%]), CK-MB (27 [17.4%] vs 97 [7.5%]), and LDH (65 [41.9%] vs 382 [29.7%]), when compared with those without coronary artery disease. There was enhanced SARS-CoV-2 receptor expression in coronary artery disease compared with healthy controls. From regression analysis, patients with five elevated cardiac markers were at a higher risk of death (hazards ratio 3.4 [95% CI 2.4-4.8]). CONCLUSIONS: COVID-19 patients with pre-existing coronary artery disease represented a higher abnormal percentage of cardiac markers, accompanied by high mortality and ICU admission rate. BNP together with hs-TNI, α- HBDH, CK-MB and LDH act as a prognostic biomarker in COVID-19 patients with or without pre-existing coronary artery disease.