Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning.

Introduction: Coronaviruses (CoVs) are naturally found in bats and can occasionally cause infection and transmission in humans and other mammals. Our study aimed to build a deep learning (DL) method to predict the adaptation of bat CoVs to other mammals. Methods: The CoV genome was represented with a method of dinucleotide composition representation (DCR) for the two main viral genes, ORF1ab and Spike. DCR features were first analyzed for their distribution among adaptive hosts and then trained with a DL classifier of convolutional neural networks (CNN) to predict the adaptation of bat CoVs. Results and discussion: The results demonstrated inter-host separation and intra-host clustering of DCR-represented CoVs for six host types: Artiodactyla, Carnivora, Chiroptera, Primates, Rodentia/Lagomorpha, and Suiformes. The DCR-based CNN with five host labels (without Chiroptera) predicted a dominant adaptation of bat CoVs to Artiodactyla hosts, then to Carnivora and Rodentia/Lagomorpha mammals, and later to primates. Moreover, a linear asymptotic adaptation of all CoVs (except Suiformes) from Artiodactyla to Carnivora and Rodentia/Lagomorpha and then to Primates indicates an asymptotic bats-other mammals-human adaptation. Conclusion: Genomic dinucleotides represented as DCR indicate a host-specific separation, and clustering predicts a linear asymptotic adaptation shift of bat CoVs from other mammals to humans via deep learning.

Acceptability and perception of COVID-19 vaccines among foreign medical students in China: A cross-sectional study.

Background: Acceptability and perception of the COVID-19 vaccine among different social groups have been the subject of several studies. However, little is known about foreign medical students in Chinese universities. Aim: This study, therefore, fills the literature gap using a focus group technique to assess the acceptance and perception of the COVID-19 vaccine among foreign medical students in China. Methods: The study adopted an online cross-sectional survey method following the Chinese universities' lockdowns to collect the data between March and April 2022. A data collection questionnaire was developed, and then the link was shared with the respondents through key informants in different universities in China to obtain the data. The data collection process only included foreign medical students who were in China from May 2021 to April 2022. The authors received a total of 403 responses from the respondents. During data processing, we excluded 17 respondents since they were not in China while administering the questionnaire to enhance the data validity. The authors then coded the remaining 386 respondents for the estimation process. We finally applied the multilinear logistics regression technique to model the COVID-19 vaccine acceptance with the response or influencing factors, including the mediating factors among the foreign medical students in China. Results: The data statistics show that 4.9% of the respondents were younger than 20 years, 91.5% were 20-40 years old, and 3.6% were older than 40 years; 36.3% of respondents were female subjects and 63.7% were male subjects. The results also show that the respondents are from six continents, including the African continent, 72.4%, Asia 17.4%, 3.1% from Europe, 2.8% from North America, 1.6% from Australia, and 2.3% from South America. The mediation analysis for the gender variable (ß = 0.235, p = 0.002) suggests that gender is a significant channel in COVID-19 vaccine acceptance and perception among foreign medical students in China. Also, the main analysis shows that opinion on the safety of the vaccine (ß = 0.081, p = 0.043), doses of the vaccine to receive (ß = 0.175, p = 0.001), vaccine safety with some side effects (ß = 0.15, p = 0.000), and the possibility of acquiring COVID-19 after vaccination (ß = 0.062, p = 0.040) are all positive factors influencing vaccine acceptability and perception. Also, the home continent (ß = -0.062, p = 0.071) is a negative factor influencing COVID-19 vaccine acceptance and perception. Furthermore, the finding shows that fear perceptions has affected 200 (51.81%) respondents. The medical students feared that the vaccines might result in future implications such as infertility, impotence, and systemic health conditions such as cardiovascular, respiratory, or deep vein thrombosis. In addition, 186 (48.19%) students feared that the vaccines were intended to shorten life expectancy. Conclusion: COVID-19 vaccination acceptability and perception among medical students in China is high, most predominantly due to their knowledge of medicine composition formulation. Despite widespread acceptance by the general public and private stakeholders, we concluded that vaccination resistance remains a significant factor among medical students and trainees. The study further adds that in considering the COVID-19 vaccine, the factor of the home continent plays a significant role in vaccine hesitancy among foreign medical students. Also, knowledge, information, and education are important pillars confronting new medicine administered among medical trainees. Finally, there is a low rate of COVID-19 vaccine hesitancy among foreign medical students in China. The study, therefore, recommends targeted policy strategies, including sensitization, detailed public information, and education, especially for medical colleges and institutions on the COVID-19 vaccination, to achieve 100%. Furthermore, the study recommends that future researchers explore other factors influencing accurate information and education for successful COVID-19 vaccination implementation.

Seven bacterial response-related genes are biomarkers for colon cancer.

BACKGROUND: Colon cancer (CC) is a common tumor that causes significant harm to human health. Bacteria play a vital role in cancer biology, particularly the biology of CC. Genes related to bacterial response were seldom used to construct prognosis models. We constructed a bacterial response-related risk model based on three Molecular Signatures Database gene sets to explore new markers for predicting CC prognosis. METHODS: The Cancer Genome Atlas (TCGA) colon adenocarcinoma samples were used as the training set, and Gene Expression Omnibus (GEO) databases were used as the test set. Differentially expressed bacterial response-related genes were identified for prognostic gene selection. Univariate Cox regression analysis, least absolute shrinkage and selection operator-penalized Cox regression analysis, and multivariate Cox regression analysis were performed to construct a prognostic risk model. The individual diagnostic effects of genes in the prognostic model were also evaluated. Moreover, differentially expressed long noncoding RNAs (lncRNAs) were identified. Finally, the expression of these genes was validated using quantitative polymerase chain reaction (qPCR) in cell lines and tissues. RESULTS: A prognostic signature was constructed based on seven bacterial response genes: LGALS4, RORC, DDIT3, NSUN5, RBCK1, RGL2, and SERPINE1. Patients were assigned a risk score based on the prognostic model, and patients in the TCGA cohort with a high risk score had a poorer prognosis than those with a low risk score; a similar finding was observed in the GEO cohort. These seven prognostic model genes were also independent diagnostic factors. Finally, qPCR validated the differential expression of the seven model genes and two coexpressed lncRNAs (C6orf223 and SLC12A9-AS1) in 27 pairs of CC and normal tissues. Differential expression of LGALS4 and NSUN5 was also verified in cell lines (FHC, COLO320DM, SW480). CONCLUSIONS: We created a seven-gene bacterial response-related gene signature that can accurately predict the outcomes of patients with CC. This model can provide valuable insights for personalized treatment.

Environmental factors influencing the risk of ANCA-associated vasculitis.

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a group of diseases characterized by inflammation and destruction of small and medium-sized blood vessels. Clinical disease phenotypes include microscopic polyangiitis (MPA), granulomatosis with polyangiitis (GPA), and eosinophilic granulomatosis with polyangiitis (EGPA). The incidence of AAV has been on the rise in recent years with advances in ANCA testing. The etiology and pathogenesis of AAV are multifactorial and influenced by both genetic and environmental factors, as well as innate and adaptive immune system responses. Multiple case reports have shown that sustained exposure to silica in an occupational environment resulted in a significantly increased risk of ANCA positivity. A meta-analysis involving six case-control studies showed that silica exposure was positively associated with AAV incidence. Additionally, exposure to air pollutants, such as carbon monoxide (CO), is a risk factor for AAV. AAV has seasonal trends. Studies have shown that various environmental factors stimulate the body to activate neutrophils and expose their own antigens, resulting in the release of proteases and neutrophil extracellular traps, which damage vascular endothelial cells. Additionally, the activation of complement replacement pathways may exacerbate vascular inflammation. However, the role of environmental factors in the etiology of AAV remains unclear and has received little attention. In this review, we summarized the recent literature on the study of environmental factors, such as seasons, air pollution, latitude, silica, and microbial infection, in AAV with the aim of exploring the relationship between environmental factors and AAV and possible mechanisms of action to provide a scientific basis for the prevention and treatment of AAV.

Government Financial Support and Enterprises’ Economic Revitalization and Pollution Discharge Under Normalization of the Epidemic: An Evolutionary Game Analysis Framework

The world economy, since the outbreak of the coronavirus epidemic, has undergone profound changes. Especially since the coronavirus became the norm, how to achieve rapid economic revitalization has become a problem that countries have to face. In order to analyze how the government should promote the enterprises’ economic revitalization and effectively control pollution under the normalized epidemic situation, this paper analyzes the economic revitalization and pollution control problems faced by enterprises under the normalized epidemic situation by using the evolutionary game method. Through the analysis of the evolutionary game model, we draw the following conclusions: (1) Discovered by comparing two different incentives mechanism and penalties mechanism, the dynamic incentives mechanism, and penalties mechanism has a better effect on the process of enterprises’ economic revitalization, and it also can reduce the discharge of enterprises’ pollutants. (2) In terms of discharge reduction effect, penalties have a better effect than incentives. Compared with incentives, in the process of the economic revitalization of enterprises, in order to reduce pollution discharge, the government can adopt dynamic penalties strategies. This paper analyzes what the government should do when enterprises face the problems of economic revitalization and pollution control. This study can not only provide suggestions for the government in the process of governance but also provide countermeasures for the economic revitalization of enterprises.

Preparation of the luciferase-labeled antibody for improving the detection sensitivity of viral antigen.

BACKGROUND: Viral antigen detection test is the most common method used to detect viruses in the field rapidly. However, due to the low sensitivity, it can only be used as an auxiliary diagnosis method for virus infection. Improving sensitivity is crucial for developing more accurate viral antigen tests. Nano luciferase (Nluc) is a sensitive reporter that has not been used in virus detection. RESULTS: In this study, we produced an intracellularly Nluc labeled detection antibody (Nluc-ch2C5) and evaluated its ability to improve the detection sensitivity of respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens. Compared with the traditional horse-radish peroxidase (HRP) labeled antibody (HRP-ch2C5), Nluc-ch2C5 was 41 times more sensitive for inactivated SARS-CoV-2 virus by sandwich chemiluminescence ELISA. Then we applied Nluc-ch2C5 to establish an automatic magnet chemiluminescence immune assay (AMCA) for the SARS-CoV-2 viral spike protein, the limit of detection was 68 pfu/reaction. The clinical sensitivity and specificity reached 75% (24/32) and 100% (48/48) using 32 PCR-positive and 48 PCR-negative swab samples for clinical evaluation, which is more sensitive than the commercial ELSA kit and colloid gold strip kit. CONCLUSIONS: Here, monoclonal antibody ch2C5 served as a model antibody and the SARS-CoV-2 served as a model pathogen. The Nluc labeled detecting antibody (Nluc-ch2C5) significantly improved the detection sensitivity of SARS-CoV-2 antigen. This labeling principle applies to other viral infections, so this labeling and test format could be expected to play an important role in detecting other virus antigens.

Biosynthesis of a water solubility-enhanced succinyl glucoside derivative of luteolin and its neuroprotective effect.

The natural flavonoids luteolin and luteoloside have anti-bacterial, anti-inflammatory, anti-oxidant, anti-tumour, hypolipidemic, cholesterol lowering and neuroprotective effects, but their poor water solubility limits their application in industrial production and the pharmaceutical industry. In this study, luteolin-7-O-ß-(6″-O-succinyl)-d-glucoside, a new compound that was prepared by succinyl glycosylation of luteolin by the organic solvent tolerant bacterium Bacillus amyloliquefaciens FJ18 in an 8.0% DMSO (v/v) system, was obtained and identified. Its greater water solubility (2293 times that of luteolin and 12 232 times that of luteoloside) provides the solution to the application problems of luteolin and luteoloside. The conversion rate of luteolin (1.0 g l-1 ) was almost 100% at 24 h, while the yield of luteolin-7-O-ß-(6″-O-succinyl)-d-glucoside reached 76.2%. In experiments involving the oxygen glucose deprivation/reoxygenation injury model of mouse hippocampal neuron cells, the cell viability was significantly improved with luteolin-7-O-ß-(6″-O-succinyl)-d-glucoside dosing, and the expressions of the anti-oxidant enzyme HO-1 in the nucleus increased, providing a neuroprotective effect for ischemic cerebral cells. The availability of biosynthetic luteolin-7-O-ß-(6″-O-succinyl)-d-glucoside, which is expected to replace luteolin and luteoloside, would effectively expand the clinical application value of luteolin derivatives.

Preparation of the luciferase-labeled antibody for improving the detection sensitivity of viral antigen

Viral antigen detection test is the most common method used to detect viruses in the field rapidly. However, due to the low sensitivity, it can only be used as an auxiliary diagnosis method for virus infection. Improving sensitivity is crucial for developing more accurate viral antigen tests. Nano luciferase (Nluc) is a sensitive reporter that has not been used in virus detection. In this study, we produced an intracellularly Nluc labeled detection antibody (Nluc-ch2C5) and evaluated its ability to improve the detection sensitivity of respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens. Compared with the traditional horse-radish peroxidase (HRP) labeled antibody (HRP-ch2C5), Nluc-ch2C5 was 41 times more sensitive for inactivated SARS-CoV-2 virus by sandwich chemiluminescence ELISA. Then we applied Nluc-ch2C5 to establish an automatic magnet chemiluminescence immune assay (AMCA) for the SARS-CoV-2 viral spike protein, the limit of detection was 68 pfu/reaction. The clinical sensitivity and specificity reached 75% (24/32) and 100% (48/48) using 32 PCR-positive and 48 PCR-negative swab samples for clinical evaluation, which is more sensitive than the commercial ELSA kit and colloid gold strip kit. Here, monoclonal antibody ch2C5 served as a model antibody and the SARS-CoV-2 served as a model pathogen. The Nluc labeled detecting antibody (Nluc-ch2C5) significantly improved the detection sensitivity of SARS-CoV-2 antigen. This labeling principle applies to other viral infections, so this labeling and test format could be expected to play an important role in detecting other virus antigens.

Convolutional Neural Networks Based on Sequential Spike Predict the High Human Adaptation of SARS-CoV-2 Omicron Variants.

The COVID-19 pandemic has frequently produced more highly transmissible SARS-CoV-2 variants, such as Omicron, which has produced sublineages. It is a challenge to tell apart high-risk Omicron sublineages and other lineages of SARS-CoV-2 variants. We aimed to build a fine-grained deep learning (DL) model to assess SARS-CoV-2 transmissibility, updating our former coarse-grained model, with the training/validating data of early-stage SARS-CoV-2 variants and based on sequential Spike samples. Sequential amino acid (AA) frequency was decomposed into serially and slidingly windowed fragments in Spike. Unsupervised machine learning approaches were performed to observe the distribution in sequential AA frequency and then a supervised Convolutional Neural Network (CNN) was built with three adaptation labels to predict the human adaptation of Omicron variants in sublineages. Results indicated clear inter-lineage separation and intra-lineage clustering for SARS-CoV-2 variants in the decomposed sequential AAs. Accurate classification by the predictor was validated for the variants with different adaptations. Higher adaptation for the BA.2 sublineage and middle-level adaptation for the BA.1/BA.1.1 sublineages were predicted for Omicron variants. Summarily, the Omicron BA.2 sublineage is more adaptive than BA.1/BA.1.1 and has spread more rapidly, particularly in Europe. The fine-grained adaptation DL model works well for the timely assessment of the transmissibility of SARS-CoV-2 variants, facilitating the control of emerging SARS-CoV-2 variants.

Convolutional Neural Networks Based on Sequential Spike Predict the High Human Adaptation of SARS-CoV-2 Omicron Variants

The COVID-19 pandemic has frequently produced more highly transmissible SARS-CoV-2 variants, such as Omicron, which has produced sublineages. It is a challenge to tell apart high-risk Omicron sublineages and other lineages of SARS-CoV-2 variants. We aimed to build a fine-grained deep learning (DL) model to assess SARS-CoV-2 transmissibility, updating our former coarse-grained model, with the training/validating data of early-stage SARS-CoV-2 variants and based on sequential Spike samples. Sequential amino acid (AA) frequency was decomposed into serially and slidingly windowed fragments in Spike. Unsupervised machine learning approaches were performed to observe the distribution in sequential AA frequency and then a supervised Convolutional Neural Network (CNN) was built with three adaptation labels to predict the human adaptation of Omicron variants in sublineages. Results indicated clear inter-lineage separation and intra-lineage clustering for SARS-CoV-2 variants in the decomposed sequential AAs. Accurate classification by the predictor was validated for the variants with different adaptations. Higher adaptation for the BA.2 sublineage and middle-level adaptation for the BA.1/BA.1.1 sublineages were predicted for Omicron variants. Summarily, the Omicron BA.2 sublineage is more adaptive than BA.1/BA.1.1 and has spread more rapidly, particularly in Europe. The fine-grained adaptation DL model works well for the timely assessment of the transmissibility of SARS-CoV-2 variants, facilitating the control of emerging SARS-CoV-2 variants.

Rapid and Accurate Detection of SARS Coronavirus 2 by Nanopore Amplicon Sequencing.

Objective: We aimed to evaluate the performance of nanopore amplicon sequencing detection for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in clinical samples. Method: We carried out a single-center, prospective cohort study in a Wuhan hospital and collected a total of 86 clinical samples, including 54 pharyngeal swabs, 31 sputum samples, and 1 fecal sample, from 86 patients with coronavirus disease 2019 (COVID-19) from Feb 20 to May 15, 2020. We performed parallel detection with nanopore-based genome amplification and sequencing (NAS) on the Oxford Nanopore Technologies (ONT) minION platform and routine reverse transcription quantitative polymerase chain reaction (RT-qPCR). In addition, 27 negative control samples were detected using the two methods. The sensitivity and specificity of NAS were evaluated and compared with those of RT-qPCR. Results: The viral read number and reference genome coverage were both significantly different between the two groups of samples, and the latter was a better indicator for SARS-CoV-2 detection. Based on the reference genome coverage, NAS revealed both high sensitivity (96.5%) and specificity (100%) compared with RT-qPCR (80.2 and 96.3%, respectively), although the samples had been stored for half a year before the detection. The total time cost was less than 15 h, which was acceptable compared with that of RT-qPCR (∼2.5 h). In addition, the reference genome coverage of the viral reads was in line with the cycle threshold value of RT-qPCR, indicating that this number could also be used as an indicator of the viral load in a sample. The viral load in sputum might be related to the severity of the infection, particularly in patients within 4 weeks after onset of clinical manifestations, which could be used to evaluate the infection. Conclusion: Our results showed the high sensitivity and specificity of the NAS method for SARS-CoV-2 detection compared with RT-qPCR. The sequencing results were also used as an indicator of the viral load to display the viral dynamics during infection. This study proved the wide application prospect of nanopore sequencing detection for SARS-CoV-2 and may more knowledge about the clinical characteristics of COVID-19.

`Development of a rapid neutralizing antibody test for SARS-CoV-2 and its application for neutralizing antibody screening and vaccinated serum testing

Background : Since the outbreak of coronavirus disease (COVID-19), the high infection rate and mutation frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent, have contributed to the ongoing global pandemic. Vaccination has become the most effective means of controlling COVID-19. Traditional neutralizing tests of sera are complex and labor-intensive, therefore, a rapid test for detecting neutralizing antibodies and antibody status post-immunization is needed. Methods : Based on the fact that antibodies exhibit neutralizing activity by blocking the binding of the S protein receptor-binding domain (S-RBD) to ACE2, we developed a rapid neutralizing antibody test, ACE2-Block-ELISA. To evaluate the sensitivity and specificity, we used 54 positive and 84 negative serum samples. We also tested the neutralizing activities of monoclonal antibodies (mAbs) and 214 sera samples from healthy individuals immunized with the inactivated SARS-CoV-2 vaccine. Results : The sensitivity and specificity of the ACE2-Block ELISA were 96.3% and 100%, respectively. For neutralizing mAb screening, ch-2C5 was selected for its ability to block the ACE2–S-RBD interaction. A plaque assay confirmed that ch-2C5 neutralized SARS-CoV-2, with NT50 values of 4.19, 10.63, and 1.074 μg/mL against the SARS-CoV-2 original strain, and the Beta and Delta variants, respectively. For the immunized sera samples, the neutralizing positive rate dropped from 82.14% to 32.16% within 4 months post-vaccination. Conclusions : This study developed and validated an ACE2-Block-ELISA to test the neutralizing activities of antibodies. As a rapid, inexpensive and easy-to-perform method, this ACE2-Block-ELISA has potential applications in rapid neutralizing mAb screening and SARS-CoV-2 vaccine evaluation.

Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants.

Explosively emerging SARS-CoV-2 variants challenge current nomenclature schemes based on genetic diversity and biological significance. Genomic composition-based machine learning methods have recently performed well in identifying phenotype-genotype relationships. We introduced a framework involving dinucleotide (DNT) composition representation (DCR) to parse the general human adaptation of RNA viruses and applied a three-dimensional convolutional neural network (3D CNN) analysis to learn the human adaptation of other existing coronaviruses (CoVs) and predict the adaptation of SARS-CoV-2 variants of concern (VOCs). A markedly separable, linear DCR distribution was observed in two major genes-receptor-binding glycoprotein and RNA-dependent RNA polymerase (RdRp)-of six families of single-stranded (ssRNA) viruses. Additionally, there was a general host-specific distribution of both the spike proteins and RdRps of CoVs. The 3D CNN based on spike DCR predicted a dominant type II adaptation of most Beta, Delta and Omicron VOCs, with high transmissibility and low pathogenicity. Type I adaptation with opposite transmissibility and pathogenicity was predicted for SARS-CoV-2 Alpha VOCs (77%) and Kappa variants of interest (58%). The identified adaptive determinants included D1118H and A570D mutations and local DNTs. Thus, the 3D CNN model based on DCR features predicts SARS-CoV-2, a major type II human adaptation and is qualified to predict variant adaptation in real time, facilitating the risk-assessment of emerging SARS-CoV-2 variants and COVID-19 control.

A Novel Formononetin Derivative Promotes Anti-ischemic Effects on Acute Ischemic Injury in Mice.

Natural flavonoids, formononetin and ononin, possess antioxidant, antibacterial, anti-inflammatory and neuroprotective effects. Many complications caused by SARS-CoV-2 make patients difficult to recover. Flavonoids, especially formononetin and ononin, have the potential to treat SARS-CoV-2 and improve myocardial injury. However, their poor water solubility, poor oral absorption, high toxicity, and high-cost purification limit industrial practical application. Succinylation modification provides a solution for the above problems. Formononetin-7-O-ß-(6″-O-succinyl)-D-glucoside (FMP), a new compound, was succinyl glycosylated from formononetin by the organic solvent tolerant bacteria Bacillus amyloliquefaciens FJ18 in a 10.0% DMSO (v/v) system. The water solubility of the new compound was improved by over 106 times compared with formononetin, which perfectly promoted the application of formononetin and ononin. The conversion rate of formononetin (0.5 g/L) was almost 94.2% at 24 h, while the yield of formononetin-7-O-ß-(6″-O-succinyl)-D-glucoside could achieve 97.2%. In the isoproterenol (ISO)-induced acute ischemia mice model, the myocardial injury was significantly improved with a high dose (40 mg/kg) of formononetin-7-O-ß-(6″-O-succinyl)-D-glucoside. The lactate dehydrogenase level was decreased, and the catalase and superoxide dismutase levels were increased after formononetin-7-O-ß-(6″-O-succinyl)-D-glucoside treatment. Thus, formononetin-7-O-ß-(6″-O-succinyl)-D-glucoside has high water solubility, low toxicity, and shows significant antimyocardial ischemia effects.

Development of an automatic integrated gene detection system for novel severe acute respiratory syndrome-related coronavirus (SARS-CoV2).

In December 2019, Wuhan, China suffered a serious outbreak of a novel coronavirus infectious disease (COVID) caused by novel severe acute respiratory syndrome-related coronavirus (SARS-CoV 2). To quickly identify the pathogen, we designed and screened primer sets, and established a sensitive and specific qRT-PCR assay for SARS-CoV 2; the lower limit of detection (LOD) was 14.8 (95% CI: 9.8-21) copies per reaction. We combined this qRT-PCR assay with an automatic integration system for nucleic acid extraction and amplification, thereby establishing an automatic integrated gene detection system (AIGS) for SARS-CoV 2. Cross reactive analysis performed in 20 other respiratory viruses and 37 nasopharyngeal swabs confirmed a 100% specificity of the assay. Using two fold diluted SARS-CoV 2 culture, the LOD of AIGS was confirmed to be 365 copies/ml (95% CI: 351-375), which was Comparable to that of conventional q RT-PCR (740 copies/ml, 95% CI: 689-750). Clinical performances of AIGS assay were assessed in 266 suspected COVID-19 clinical respiratory tract samples tested in parallel with a commercial kit. The clinical sensitivity of the AIGS test was 97.62% (95% CI: 0.9320-0.9951) based on the commercial kit test result, and concordance analysis showed a high agreement in SARS-CoV-2 detection between the two assays, Pearson R was 0.9623 (95% CI: 0.9523-0.9703). The results indicated that this AIGS could be used for rapid detection of SARS-CoV 2. With the advantage of simple operation and less time consuming, AIGS could be suitable for SARS-CoV2 detection in primary medical institutions, thus would do a great help to improve detection efficiency and control the spread of COVID-19.