A droplet digital PCR chip with passive bubble removal for absolute nucleic acid quantification

Droplet digital polymerase chain reaction (ddPCR) is an extremely sensitive method for the precisely determining the concentration of target nucleic acids. However, air bubbles between droplets during amplification can cause significant droplet loss and decreased accuracy in results. In the present study, an all-in-one microfluidic chip that integrates emulsification, passive bubble removal, droplet monolayer storage, on-chip nucleic acid amplification, and droplet fluorescence signal readout is proposed. The integrated passive bubble removal structures automatically complete the trapping and guiding of the bubbles, ensuring that the droplets do not touch the bubbles during amplification and thus is not lost. The ddPCR device with optimized key parameters proved to be effective and efficient by completely removing bubbles between droplets and having a dead volume of less than 1 %. The ability of the ddPCR chip to accurately quantify nucleic acids was evaluated by measuring plasmids with the SARS-CoV-2N gene at concentrations ranging from 10 to 50 000 copies/μL. The innovative ddPCR device satisfies the requirement for accurate nucleic acid quantification and is expected to accelerate the popularity of dPCR due to its low processing difficulty, ease of use and high robustness.

Evaluation of RT-LAMP Assay for Rapid Detection of SARS-CoV-2.

OBJECTIVE: To evaluate the accuracy of the reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay for rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in community or primary-care settings. METHOD: We systematically searched the Web of Science, Embase, PubMed, and Cochrane Library databases. We conducted quality evaluation using ReviewManager software (version 5.0). We then used MetaDisc software (version 1.4) and Stata software (version 12.0) to build forest plots, along with a Deeks funnel plot and a bivariate boxplot for analysis. RESULT: Overall, the sensitivity, specificity, and diagnostic odds ratio were 0.79, 0.97, and 328.18, respectively. The sensitivity for the subgroup with RNA extraction appeared to be higher, at 0.88 (0.86-0.90), compared to the subgroup without RNA extraction, at 0.50 (0.45-0.55), with no significant difference in specificity. CONCLUSION: RT-LAMP assay exhibited high specificity regarding current SARS-CoV-2 infection. However, its overall sensitivity was relatively moderate. Extracting RNA was found to be beneficial in improving sensitivity.

Omicron variants breakthrough infection elicited higher specific memory immunity than third dose booster in healthy vaccinees.

Homologous booster, heterologous booster, and Omicron variants breakthrough infection (OBI) could improve the humoral immunity against Omicron variants. Questions concerning about memory B cells (MBCs) and T cells immunity against Omicron variants, features of long-term immunity, after booster and OBI, needs to be explored. Here, comparative analysis demonstrate antibody and T cell immunity against ancestral strain, Delta and Omicron variants in Omicron breakthrough infected patients (OBIPs) are comparable to that in Ad5-nCoV boosted healthy volunteers (HVs), higher than that in inactivated vaccine (InV) boosted HVs. However, memory B cells (MBCs) immunity against Omicron variants was highest in OBIPs, followed by Ad5-nCoV boosted and InV boosted HVs. OBIPs and Ad5-nCoV boosted HVs have higher classical MBCs and activated MBCs, and lower naïve MBCs and atypical MBCs relative to both vaccine boosted HVs. Collectively, these data indicate Omicron breakthrough infection elicit higher MBCs and T cells against SARS-CoV-2 especially Omicron variants relative to homologous InV booster and heterologous Ad5-nCoV booster.

Identification of the Hub Genes and the Signaling Pathways in Human iPSC-Cardiomyocytes Infected by SARS-CoV-2.

Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV-2) is an enveloped single-stranded RNA virus that can lead to respiratory symptoms and damage many organs such as heart, kidney, intestine, brain and liver. It has not been clearly documented whether myocardial injury is caused by direct infection of cardiomyocytes, lung injury, or other unknown mechanisms. The gene expression profile of GSE150392 was obtained from the Gene Expression Omnibus (GEO) database. The processing of high-throughput sequencing data and the screening of differentially expressed genes (DEGs) were implemented by R software. The R software was employed to analyze the Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The protein-protein interaction (PPI) network of the DEGs was constructed by the STRING website. The Cytoscape software was applied for the visualization of PPI network and the identification of hub genes. The statistical analysis was performed by the GraphPad Prism software to verify the hub genes. A total of 516 up-regulated genes and 191 down-regulated genes were screened out. The top 1 enrichment items of GO in biological process (BP), Cellular Component (CC), and Molecular Function (MF) were type I interferon signaling pathway, sarcomere, and receptor ligand activity, respectively. The top 10 enrichment pathways, including TNF signaling pathway, were identified by KEGG enrichment analysis. A PPI network was established, consisting of 613 nodes and 3,993 edges. The 12 hub genes were confirmed as statistically significant, which was verified by GSE151879 dataset. In conclusion, the hub genes of human iPSC-cardiomyocytes infected with SARS-CoV-2 were identified through bioinformatics analysis, which may be used as biomarkers for further research.

Screening the hub genes and analyzing the mechanisms in discharged COVID-19 patients retesting positive through bioinformatics analysis.

BACKGROUND: After encountering COVID-19 patients who test positive again after discharge, our study analyzed the pathogenesis to further assess the risk and possibility of virus reactivation. METHODS: A separate microarray was acquired from the Gene Expression Omnibus (GEO), and its samples were divided into two groups: a "convalescent-RTP" group consisting of convalescent and "retesting positive" (RTP) patients (group CR) and a "healthy-RTP" group consisting of healthy control and RTP patients (group HR). The enrichment analysis was performed with R software, obtaining the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, the protein-protein interaction (PPI) networks of each group were established, and the hub genes were discovered using the cytoHubba plugin. RESULTS: In this study, 6622 differentially expressed genes were identified in the group CR, among which RAB11B-AS1, DISP1, MICAL3, PSMG1, and DOCK4 were up-regulated genes, and ANAPC1, IGLV1-40, SORT1, PLPPR2, and ATP1A1-AS1 were down-regulated. 7335 genes were screened in the group HR, including the top 5 up-regulated genes ALKBH6, AMBRA1, MIR1249, TRAV18, and LRRC69, and the top 5 down-regulated genes FAM241B, AC018529.3, AL031963.3, AC006946.1, and FAM149B1. The GO and KEGG analysis of the two groups revealed a significant enrichment in immune response and apoptosis. In the PPI network constructed, group CR and group HR identified 10 genes, respectively, and TP53BP1, SNRPD1, and SNRPD2 were selected as hub genes. CONCLUSIONS: Using the messenger ribonucleic acid (mRNA) expression data from GSE166253, we found TP53BP1, SNRPD1, and SNRPD2 as hub genes in RTP patients, which is vital to the management and prognostic prediction of RTP patients.

Effect of BCG Vaccination against SARS-CoV-2 Infection.

Based on previous studies, we found that Bacillus Calmette-Guérin (BCG) vaccination may play a role in preventing SARS-CoV-2 infection. Therefore, we conducted a meta-analysis to investigate this protective effect. We searched the Embase, PubMed, Web of Science, Cochrane Library, BioRxiv, and MedRxiv databases for studies that evaluated the relationship between BCG vaccination and SARS-CoV-2 infection or COVID-19 disease. The quality of all included studies was assessed using the Risk of Bias in Non-randomized Studies of Interventions and the Agency for Healthcare Research and Quality data tools. Review Manager (Version 5.3) was used to conduct all the data analyses. A total of eight studies were ultimately included in our meta-analysis. Our primary analysis found a significantly lower SARS-CoV-2 infection rate in the BCG vaccination group compared to the control group, with an odds ratio of 0.61, (95% confidence interval 0.39 to 0.95, P = 0.03; I2 = 31%, and P = 0.21, respectively). Our study indicates that BCG vaccination can protect against SARS-CoV-2 infection. However, there is insufficient evidence that BCG vaccination can reduce the severity of COVID-19.

Evaluation of CRISPR-Based Assays for Rapid Detection of SARS-CoV-2: A Systematic Review and Meta-Analysis.

PURPOSE: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen of coronavirus disease 2019. Diagnostic methods based on the clustered regularly interspaced short palindromic repeats (CRISPR) have been developed to detect SARS-CoV-2 rapidly. Therefore, a systematic review and meta-analysis were performed to assess the diagnostic accuracy of CRISPR for detecting SARS-CoV-2 infection. MATERIALS AND METHODS: Studies published before August 2021 were retrieved from four databases, using the keywords "SARS-CoV-2" and "CRISPR." Data were collected from these publications, and the sensitivity, specificity, negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR) were calculated. The summary receiver operating characteristic curve was plotted for analysis with MetaDiSc 1.4. The Stata 15.0 software was used to draw Deeks' funnel plots to evaluate publication bias. RESULTS: We performed a pooled analysis of 38 independent studies shown in 30 publications. The reference standard was reverse transcription-quantitative PCR. The results indicated that the sensitivity of CRISPR-based methods for diagnosis was 0.94 (95% CI 0.93-0.95), the specificity was 0.98 (95% CI 0.97-0.99), the PLR was 34.03 (95% CI 20.81-55.66), the NLR was 0.08 (95% CI 0.06-0.10), and the DOR was 575.74 (95% CI 382.36-866.95). The area under the curve was 0.9894. CONCLUSION: Studies indicate that a diagnostic method based on CRISPR has high sensitivity and specificity. Therefore, this would be a potential diagnostic tool to improve the accuracy of SARS-CoV-2 detection.

The Diagnostic Accuracy of Xpert Xpress to SARS-CoV-2: A systematic review.

The SARS-CoV-2 infection rate, as well as mortality rate, is high. There is an urgent need for a high-throughput, accurate and reliable method of diagnosing COVID-19 pneumonia. We included references from databases, such as PubMed, Cochrane Library, Web of Science, and Embase, and extracted data. Then, MetaDisc and STATA were used to establish forest plots and funnel plots for meta-analysis. We collected 14 articles and performed a systematic review. The following results were obtained: sensitivity and specificity were 0.97 (0.96 to 0.98) and 0.97 (0.96 to 0.98) respectively; PLR and NLR were 24.51 (16.63-36.12) and 0.03 (0.01 to 0.10) respectively, DOR was 975.15 (430.11-2210.88), and AUC was 0.9926. When Xpress detects SARS-CoV-2 in different samples, the heterogeneity is small and the specificity and sensitivity are extremely high. We recommend the employment of Xpert Xpress analysis in rapid screening.

An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2.

BACKGROUND: At the end of 2019, the world witnessed the emergence and ravages of a viral infection induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Also known as the coronavirus disease 2019 (COVID-19), it has been identified as a public health emergency of international concern (PHEIC) by the World Health Organization (WHO) because of its severity. METHODS: The gene data of 51 samples were extracted from the GSE150316 and GSE147507 data set and then processed by means of the programming language R, through which the differentially expressed genes (DEGs) that meet the standards were screened. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the selected DEGs to understand the functions and approaches of DEGs. The online tool STRING was employed to construct a protein-protein interaction (PPI) network of DEGs and, in turn, to identify hub genes. RESULTS: A total of 52 intersection genes were obtained through DEG identification. Through the GO analysis, we realized that the biological processes (BPs) that have the deepest impact on the human body after SARS-CoV-2 infection are various immune responses. By using STRING to construct a PPI network, 10 hub genes were identified, including IFIH1, DDX58, ISG15, EGR1, OASL, SAMD9, SAMD9L, XAF1, IFITM1, and TNFSF10. CONCLUSION: The results of this study will hopefully provide guidance for future studies on the pathophysiological mechanism of SARS-CoV-2 infection.

Identification of Key Pathways and Genes in SARS-CoV-2 Infecting Human Intestines by Bioinformatics Analysis.

COVID-19 is a serious infectious disease that has recently swept the world, and research on its causative virus, SARS-CoV-2, remains insufficient. Therefore, this study uses bioinformatics analysis techniques to explore the human digestive tract diseases that may be caused by SARS-CoV-2 infection. The gene expression profile data set, numbered GSE149312, is from the Gene Expression Omnibus (GEO) database and is divided into a 24-h group and a 60-h group. R software is used to analyze and screen out differentially expressed genes (DEGs) and then gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses are performed. In KEGG, the pathway of non-alcoholic fatty liver disease exists in both the 24-h group and 60-h group. STRING is used to establish a protein-protein interaction (PPI) network, and Cytoscape is then used to visualize the PPI and define the top 12 genes of the node as the hub genes. Through verification, nine statistically significant hub genes are identified: AKT1, TIMP1, NOTCH, CCNA2, RRM2, TTK, BUB1B, KIF20A, and PLK1. In conclusion, the results of this study can provide a certain direction and basis for follow-up studies of SARS-CoV-2 infection of the human digestive tract and provide new insights for the prevention and treatment of diseases caused by SARS-CoV-2.

Liver injury in COVID-19: clinical features and treatment management.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has spread to many countries around the world. In addition to lung disease, severe cases also displayed varying degrees of liver injury. This article will describe the latest developments regarding coronavirus and the pathogenesis of liver injury, the prone population and clinical characteristics of these patients, as well as providing some suggestions for clinical treatment.

Exploration and validation of related hub gene expression during SARS-CoV-2 infection of human bronchial organoids.

BACKGROUND: In the novel coronavirus pandemic, the high infection rate and high mortality have seriously affected people's health and social order. To better explore the infection mechanism and treatment, the three-dimensional structure of human bronchus has been employed in a better in-depth study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We downloaded a separate microarray from the Integrated Gene Expression System (GEO) on a human bronchial organoids sample to identify differentially expressed genes (DEGS) and analyzed it with R software. After processing with R software, Gene Ontology (GO) and Kyoto PBMCs of Genes and Genomes (KEGG) were analyzed, while a protein-protein interaction (PPI) network was constructed to show the interactions and influence relationships between these differential genes. Finally, the selected highly connected genes, which are called hub genes, were verified in CytoHubba plug-in. RESULTS: In this study, a total of 966 differentially expressed genes, including 490 upregulated genes and 476 downregulated genes were used. Analysis of GO and KEGG revealed that these differentially expressed genes were significantly enriched in pathways related to immune response and cytokines. We construct protein-protein interaction network and identify 10 hub genes, including IL6, MMP9, IL1B, CXCL8, ICAM1, FGF2, EGF, CXCL10, CCL2, CCL5, CXCL1, and FN1. Finally, with the help of GSE150728, we verified that CXCl1, CXCL8, CXCL10, CCL5, EGF differently expressed before and after SARS-CoV-2 infection in clinical patients. CONCLUSIONS: In this study, we used mRNA expression data from GSE150819 to preliminarily confirm the feasibility of hBO as an in vitro model to further study the pathogenesis and potential treatment of COVID-19. Moreover, based on the mRNA differentiated expression of this model, we found that CXCL8, CXCL10, and EGF are hub genes in the process of SARS-COV-2 infection, and we emphasized their key roles in SARS-CoV-2 infection. And we also suggested that further study of these hub genes may be beneficial to treatment, prognostic prediction of COVID-19.

Detection of COVID-19: A review of the current literature and future perspectives.

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the coronavirus disease 2019 (COVID-19) worldwide pandemic. This unprecedented situation has garnered worldwide attention. An effective strategy for controlling the COVID-19 pandemic is to develop highly accurate methods for the rapid identification and isolation of SARS-CoV-2 infected patients. Many companies and institutes are therefore striving to develop effective methods for the rapid detection of SARS-CoV-2 ribonucleic acid (RNA), antibodies, antigens, and the virus. In this review, we summarize the structure of the SARS-CoV-2 virus, its genome and gene expression characteristics, and the current progression of SARS-CoV-2 RNA, antibodies, antigens, and virus detection. Further, we discuss the reasons for the observed false-negative and false-positive RNA and antibody detection results in practical clinical applications. Finally, we provide a review of the biosensors which hold promising potential for point-of-care detection of COVID-19 patients. This review thereby provides general guidelines for both scientists in the biosensing research community and for those in the biosensor industry to develop a highly sensitive and accurate point-of-care COVID-19 detection system, which would be of enormous benefit for controlling the current COVID-19 pandemic.