Household transmission of SARS-CoV-2 during the Omicron wave in Shanghai, China: A case-ascertained study.

OBJECTIVES: We used a case-ascertained study to determine the features of household transmission of SARS-CoV-2 Omicron variant in Shanghai, China. METHODS: In April 2022, we carried out a household transmission study from 309 households of 335 SARS-CoV-2 pediatric cases referred to a designated tertiary Children's Hospital. The detailed information can be collected from the 297 households for estimating the transmission parameters. The 236 households were qualified for estimating the secondary infection attack rates (SARI ) and secondary clinical attack rates (SARC ) among adult household contacts, characterizing the transmission heterogeneities in infectivity and susceptibility, and assessing the vaccine effectiveness. RESULTS: We estimated the mean incubation period and serial interval of Omicron variant to be 4.6 ± 2.1 and 3.9 ± 3.7 days, respectively, with 57.2% of the transmission events occurring at the presymptomatic phase. The overall SARI and SARC among adult household contacts were 77.11% (95% confidence interval [CI]: 73.58%-80.63%) and 67.03% (63.09%-70.98%). We found higher household susceptibility in females. Infectivity was not significantly different between children and adults and symptomatic and asymptomatic cases. Two-dose and booster-dose of inactivated COVID-19 vaccination were 14.8% (5.8%-22.9%) and 18.9% (9.0%-27.7%) effective against Omicron infection and 21.5% (10.4%-31.2%) and 24.3% (12.3%-34.7%) effective against the symptomatic disease. CONCLUSIONS: We found high household transmission during the Omicron wave in Shanghai due to presymptomatic and asymptomatic transmission despite implementation of strict interventions, indicating the importance of early detection and timely isolation of SARS-CoV-2 infections. Marginal effectiveness of inactivated vaccines against Omicron infection poses a great challenge for outbreak containment.

Discovery of the Cryptic Sites of SARS-CoV-2 Papain-like Protease and Analysis of Its Druggability.

In late 2019, a new coronavirus (CoV) caused the outbreak of a deadly respiratory disease, resulting in the COVID-19 pandemic. In view of the ongoing pandemic, there is an immediate need to find drugs to treat patients. SARS-CoV-2 papain-like cysteine protease (PLpro) not only plays an important role in the pathogenesis of the virus but is also a target protein for the development of inhibitor drugs. Therefore, to develop targeted inhibitors, it is necessary to analyse and verify PLpro sites and explore whether there are other cryptic binding pockets with better activity. In this study, first, we detected the site of the whole PLpro protein by sitemap of Schrödinger (version 2018), the cavity of LigBuilder V3, and DeepSite, and roughly judged the possible activated binding site area. Then, we used the mixed solvent dynamics simulation (MixMD) of probe molecules to induce conformational changes in the protein to find the possible cryptic active sites. Finally, the TRAPP method was used to predict the druggability of cryptic pockets and analyse the changes in the physicochemical properties of residues around these sites. This work will help promote the research of SARS-CoV-2 PLpro inhibitors.

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions.

Background: This study set out to develop a computed tomography (CT)-based wavelet transforming radiomics approach for grading pulmonary lesions caused by COVID-19 and to validate it using real-world data. Methods: This retrospective study analyzed 111 patients with 187 pulmonary lesions from 16 hospitals; all patients had confirmed COVID-19 and underwent non-contrast chest CT. Data were divided into a training cohort (72 patients with 127 lesions from nine hospitals) and an independent test cohort (39 patients with 60 lesions from seven hospitals) according to the hospital in which the CT was performed. In all, 73 texture features were extracted from manually delineated lesion volumes, and 23 three-dimensional (3D) wavelets with eight decomposition modes were implemented to compare and validate the value of wavelet transformation for grade assessment. Finally, the optimal machine learning pipeline, valuable radiomic features, and final radiomic models were determined. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve were used to determine the diagnostic performance and clinical utility of the models. Results: Of the 187 lesions, 108 (57.75%) were diagnosed as mild lesions and 79 (42.25%) as moderate/severe lesions. All selected radiomic features showed significant correlations with the grade of COVID-19 pulmonary lesions (P<0.05). Biorthogonal 1.1 (bior1.1) LLL was determined as the optimal wavelet transform mode. The wavelet transforming radiomic model had an AUC of 0.910 in the test cohort, outperforming the original radiomic model (AUC =0.880; P<0.05). Decision analysis showed the radiomic model could add a net benefit at any given threshold of probability. Conclusions: Wavelet transformation can enhance CT texture features. Wavelet transforming radiomics based on CT images can be used to effectively assess the grade of pulmonary lesions caused by COVID-19, which may facilitate individualized management of patients with this disease.

Extraction-free RT-PCR to Detect SARS-CoV-2 Variants of Concern

Background SARS-CoV-2 variants of concern (VOC) have challenged real-time reverse transcriptase polymerase chain reaction (RT-PCR) methods for the diagnosis of COVID-19. Methods The CDC 2019-Novel Coronavirus real-time RT-PCR panel was modified to create a single-plex extraction-free proxy RT-PCR assay, VOCFast™. This assay uses the nucleocapsid N1 as well as novel primer/probe pairs to target VOC mutations in the Orf1a and spike (S) genes. For analytical validation of VOCFast, synthetic controls for the Wuhan, alpha/B.1.1.7, beta/B.1.351, and gamma/P.1 strains were tested at various concentrations. Clinical validation was performed using patient anterior nares swab and saliva specimens collected in the Denver, CO area between Nov 2020 and Feb 2021 or in March 2021. Orthogonal next-generation sequencing (NGS) was also performed. Results Similar N1 quantification cycle (Cq) values corresponding to viral load were observed for all strains, suggesting that VOC mutations do not affect performance of the N1 primer/probe. Orf1a-mut and S1-mut primer/probes generated a stable high Cq value for the Wuhan strain. Conversely, Orf1a-mut Cq values were inversely correlated with viral load for all VOC. The S1-mut Cq was inversely correlated with viral load of the alpha strain, but did not reliably amplify beta/gamma VOC. The limit of detection was 8 copies/uL. The first set of COVID-19 patient specimens revealed no amplification using Orf1a-mut whereas 53% of specimens collected in Mar 2021 demonstrated amplification by Orf-1a. Orthogonal testing by the SARS-CoV-2 NGS Assay and COVID-DX software demonstrated that 12/12 alpha strains, 2/2 beta/gamma strains, and 33/33 Wuhan strains were correctly identified by VOCFast. Detection of VOC in clinical specimens and validation by NGS Conclusion The combination of the N1, Orf1a-mut, and S1-mut primers/probes in VOCFast can distinguish the Wuhan, alpha, and beta/gamma strains and it consistent with NGS results. Testing of clinical samples revealed that VOC emerged in Denver, CO in March 2021. Future work to discriminate beta, gamma, and emerging VOC is ongoing. In summary, VOCFast is an extraction-free RT-PCR assay for nasal swab and saliva specimens that can identify VOC with a turnaround time suitable for clinical testing. Disclosures Brian L. Harry, MD PhD, Summit Biolabs Inc. (Grant/Research Support, Shareholder) Mara Couto-Rodriguez, MS, Biotia (Employee) Dorottya Nagy-Szakal, MD PhD, Biotia Inc (Employee, Shareholder) Niamh B. O’Hara, PhD, Biotia (Board Member, Employee, Shareholder) Shi-Long Lu, MD PhD, Summit Biolabs Inc. (Grant/Research Support, Shareholder)

The Molecular Mechanism of Multiple Organ Dysfunction and Targeted Intervention of COVID-19 Based on Time-Order Transcriptomic Analysis.

Coronavirus disease 2019 (COVID-19) pandemic is caused by the novel coronavirus that has spread rapidly around the world, leading to high mortality because of multiple organ dysfunction; however, its underlying molecular mechanism is unknown. To determine the molecular mechanism of multiple organ dysfunction, a bioinformatics analysis method based on a time-order gene co-expression network (TO-GCN) was performed. First, gene expression profiles were downloaded from the gene expression omnibus database (GSE161200), and a TO-GCN was constructed using the breadth-first search (BFS) algorithm to infer the pattern of changes in the different organs over time. Second, Gene Ontology enrichment analysis was used to analyze the main biological processes related to COVID-19. The initial gene modules for the immune response of different organs were defined as the research object. The STRING database was used to construct a protein-protein interaction network of immune genes in different organs. The PageRank algorithm was used to identify five hub genes in each organ. Finally, the Comparative Toxicogenomics Database played an important role in exploring the potential compounds that target the hub genes. The results showed that there were two types of biological processes: the body's stress response and cell-mediated immune response involving the lung, trachea, and olfactory bulb (olf) after being infected by COVID-19. However, a unique biological process related to the stress response is the regulation of neuronal signals in the brain. The stress response was heterogeneous among different organs. In the lung, the regulation of DNA morphology, angiogenesis, and mitochondrial-related energy metabolism are specific biological processes related to the stress response. In particular, an effect on tracheal stress response was made by the regulation of protein metabolism and rRNA metabolism-related biological processes, as biological processes. In the olf, the distinctive stress responses consist of neural signal transmission and brain behavior. In addition, myeloid leukocyte activation and myeloid leukocyte-mediated immunity in response to COVID-19 can lead to a cytokine storm. Immune genes such as SRC, RHOA, CD40LG, CSF1, TNFRSF1A, FCER1G, ICAM1, LAT, LCN2, PLAU, CXCL10, ICAM1, CD40, IRF7, and B2M were predicted to be the hub genes in the cytokine storm. Furthermore, we inferred that resveratrol, acetaminophen, dexamethasone, estradiol, statins, curcumin, and other compounds are potential target drugs in the treatment of COVID-19.

Gordian Knot: Gastrointestinal lesions caused by three highly pathogenic coronaviruses from SARS-CoV and MERS-CoV to SARS-CoV-2.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of 2019 novel coronavirus disease (COVID-19), is currently spreading around the world. The WHO declared on January 31 that the outbreak of SARS-CoV-2 was a public health emergency. SARS-Cov-2 is a member of highly pathogenic coronavirus group that also consists of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV). Although respiratory tract lesions were regarded as main manifestation of SARS-Cov-2 infection, gastrointestinal lesions were also reported. Similarly, patients with SARS-CoV and MERS-CoV were also observed. Common gastrointestinal symptoms of patients mainly included diarrhea, vomiting and abdominal pain. Gastrointestinal lesions could be used as basis for early diagnosis of patients, and at the same time, controlling gastrointestinal lesions better facilitated to cut off the route of fecal-oral transmission. Hence, this review summarizes the characteristics and mechanism of gastrointestinal lesions caused by three highly pathogenic human coronavirus infections including SARS-CoV, MERS-CoV, as well as SARS-CoV-2. Furthermore, it is expected to gain experience from gastrointestinal lesions caused by SARS-CoV and MERS-CoV infections in order to be able to better relieve SARS-CoV-2 epidemic. Targetin gut microbiota to regulate the process of SARS-CoV-2 infection should be a concern. Especially, the application of nanotechnology may provide help for further controlling COVID-19.

Peripheral immunological features of COVID-19 patients in Taizhou, China: A retrospective study.

BACKGROUND: Abnormal peripheral immunological features are associated with the progression of coronavirus disease 2019 (COVID-19). METHODS: Clinical and laboratory data were retrieved in a cohort of 146 laboratory-confirmed COVID-19 patients. Potential risk factors for the development of severe COVID-19 were evaluated. RESULTS: On admission, lymphocytes, CD3+, CD4+ and CD8+ T cells, eosinophils, and albumin and pre-albumin were dramatically lower, whereas neutrophils, and interleukin (IL)-10, C-reactive protein (CRP), aspartate aminotransferase (AST) and gamma-glutamyltransferase (GGT) were significantly higher in severe cases. By the second week after discharge, all variables improved to normal levels. Covariate logistic regression results showed that the CD8+ cell count and CRP level were independent risk factors for severe COVID-19. CONCLUSION: Lower peripheral immune cell subsets in patients with severe disease recovered to normal levels as early as the second week after discharge. CD8+ T cell counts and CRP levels on admission are independent predictive factors for severe COVID-19.

A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing.

Phenotype-based compound screening has advantages over target-based drug discovery, but is unscalable and lacks understanding of mechanism. Chemical-induced gene expression profile provides a mechanistic signature of phenotypic response. However, the use of such data is limited by their sparseness, unreliability, and relatively low throughput. Few methods can perform phenotype-based de novo chemical compound screening. Here, we propose a mechanism-driven neural network-based method DeepCE, which utilizes graph neural network and multi-head attention mechanism to model chemical substructure-gene and gene-gene associations, for predicting the differential gene expression profile perturbed by de novo chemicals. Moreover, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves superior performances to state-of-the-art methods. The effectiveness of gene expression profiles generated from DeepCE is further supported by comparing them with observed data for downstream classification tasks. To demonstrate the value of DeepCE, we apply it to drug repurposing of COVID-19, and generate novel lead compounds consistent with clinical evidence. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data and screening novel chemicals for the modulation of a systemic response to disease.

A deep learning framework for high-throughput mechanism-driven phenotype compound screening.

Target-based high-throughput compound screening dominates conventional one-drug-one-gene drug discovery process. However, the readout from the chemical modulation of a single protein is poorly correlated with phenotypic response of organism, leading to high failure rate in drug development. Chemical-induced gene expression profile provides an attractive solution to phenotype-based screening. However, the use of such data is currently limited by their sparseness, unreliability, and relatively low throughput. Several methods have been proposed to impute missing values for gene expression datasets. However, few existing methods can perform de novo chemical compound screening. In this study, we propose a mechanism-driven neural network-based method named DeepCE (Deep Chemical Expression) which utilizes graph convolutional neural network to learn chemical representation and multi-head attention mechanism to model chemical substructure-gene and gene-gene feature associations. In addition, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves the superior performances not only in de novo chemical setting but also in traditional imputation setting compared to state-of-the-art baselines for the prediction of chemical-induced gene expression. We further verify the effectiveness of gene expression profiles generated from DeepCE by comparing them with gene expression profiles in L1000 dataset for downstream classification tasks including drug-target and disease predictions. To demonstrate the value of DeepCE, we apply it to patient-specific drug repurposing of COVID-19 for the first time, and generate novel lead compounds consistent with clinical evidences. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data as well as screening novel chemicals for the modulation of systemic response to disease.

Tailoring steroids in the treatment of COVID-19 pneumonia assisted by CT scans: three case reports.

In this article, we analyze and report cases of three patients who were admitted to Renmin Hospital, Wuhan University, China, for treating COVID-19 pneumonia in February 2020 and were unresponsive to initial treatment of steroids. They were then received titrated steroids treatment based on the assessment of computed tomography (CT) images augmented and analyzed with the artificial intelligence (AI) tool and output. Three patients were finally recovered and discharged. The result indicated that sufficient steroids may be effective in treating the COVID-19 patients after frequent evaluation and timely adjustment according to the disease severity assessed based on the quantitative analysis of the images of serial CT scans.

Mask wearing in pre-symptomatic patients prevents SARS-CoV-2 transmission: An epidemiological analysis.

OBJECTIVES: Pandemic COVID-19 has become a seriously public health priority worldwide. Comprehensive strategies including travel restrictions and mask-wearing have been implemented to mitigate the virus circulation. However, detail information on community transmission is unavailable yet. METHODS: From January 23 to March 1, 2020, 127 patients (median age: 46 years; range: 11-80) with 71 male and 56 female, were confirmed to be infected with the SARS-CoV-2 in Taizhou, Zhejiang, China. Epidemiological trajectory and clinical features of these COVID-19 cases were retrospectively retrieved from electronic medical records and valid individual questionnaire. RESULTS: The disease onset was between January 9 to February 14, 2020. Among them, 64 patients are local residents, and 63 patients were back home from Wuhan from January 10 to 24, 2020 before travel restriction. 197 local residents had definite close-contact with 41 pre-symptomatic patients back from Wuhan. 123 and 74 of them contact with mask-wearing or with no mask-wearing pre-symptomatic patients back from Wuhan, respectively. Data showed that incidence of COVID-19 was significantly higher for local residents close-contact with no mask-wearing Wuhan returned pre-symptomatic patients (19.0% vs. 8.1%, p < 0.001). Among 57 close-contact individuals, 21 sequential local COVID-19 patients originated from a pre-symptomatic Wuhan returned couple, indicated dense gathering in congested spaces is a high risk for SARS-CoV-2 transmission. CONCLUSIONS: Our findings provided valuable details of pre-symptomatic patient mask-wearing and restriction of mass gathering in congested spaces particularly, are important interventions to mitigate the SARS-CoV-2 transmission.