One-step synthesis of nanosilver embedding laser-induced graphene for H2O2 sensor.

During the novel coronavirus pandemic, hydrogen peroxide (H2O2) played an important role as a disinfectant. However, high concentrations of H2O2 can also cause damage to the skin and eyes. Therefore, the quantitative and qualitative detection of H2O2 is an important research direction. In this work, we report a one-step laser-induced synthesis of graphene doped with Ag NPs composites. It directly trims screen printed electrodes (SPE). Firstly, we did the timekeeping current method (CA) test on H2O2 using a conventional platinum sheet as the counter electrode, and obtained linear ranges of 1-110 µM and 110-800 µM with a sensitivity of 118.7 and 96.3 µAmM-1cm-2 and a low detection limit of (LOD) 0.24 µM and 0.31 µM. On this basis we have also achieved a good result in CA testing using Screen printed carbon electrodes (SPCE), laying the foundation for portable testing. The sensor has excellent interference immunity and high selectivity.

The SARS-CoV-2 spike protein binds and modulates estrogen receptors.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein binds angiotensin-converting enzyme 2 as its primary infection mechanism. Interactions between S and endogenous proteins occur after infection but are not well understood. We profiled binding of S against >9000 human proteins and found an interaction between S and human estrogen receptor α (ERα). Using bioinformatics, supercomputing, and experimental assays, we identified a highly conserved and functional nuclear receptor coregulator (NRC) LXD-like motif on the S2 subunit. In cultured cells, S DNA transfection increased ERα cytoplasmic accumulation, and S treatment induced ER-dependent biological effects. Non-invasive imaging in SARS-CoV-2-infected hamsters localized lung pathology with increased ERα lung levels. Postmortem lung experiments from infected hamsters and humans confirmed an increase in cytoplasmic ERα and its colocalization with S in alveolar macrophages. These findings describe the discovery of a S-ERα interaction, imply a role for S as an NRC, and advance knowledge of SARS-CoV-2 biology and coronavirus disease 2019 pathology.

C-to-U RNA deamination is the driving force accelerating SARS-CoV-2 evolution.

Understanding the molecular mechanism underlying the rampant mutation of SARS-CoV-2 would help us control the COVID-19 pandemic. The APOBEC-mediated C-to-U deamination is a major mutation type in the SARS-CoV-2 genome. However, it is unclear whether the novel mutation rate u is higher for C-to-U than for other mutation types, and what the detailed driving force is. By analyzing the time course SARS-CoV-2 global population data, we found that C-to-U has the highest novel mutation rate u among all mutation types and that this u is still increasing with time (du/dt > 0). Novel C-to-U events, rather than other mutation types, have a preference over particular genomic regions. A less local RNA structure is correlated with a high novel C-to-U mutation rate. A cascade model nicely explains the du/dt > 0 for C-to-U deamination. In SARS-CoV-2, the RNA structure serves as the molecular basis of the extremely high and continuously accelerating C-to-U deamination rate. This mechanism is the driving force of the mutation, adaptation, and evolution of SARS-CoV-2. Our findings help us understand the dynamic evolution of the virus mutation rate.

Genetic diversity and epidemiology of human rhinovirus among children with severe acute respiratory tract infection in Guangzhou, China.

BACKGROUND: Human rhinovirus (HRV) is one of the major viruses of acute respiratory tract disease among infants and young children. This work aimed to understand the epidemiological and phylogenetic features of HRV in Guangzhou, China. In addition, the clinical characteristics of hospitalized children infected with different subtype of HRV was investigated. METHODS: Hospitalized children aged < 14 years old with acute respiratory tract infections were enrolled from August 2018 to December 2019. HRV was screened for by a real-time reverse-transcription PCR targeting the viral 5'UTR. RESULTS: HRV was detected in 6.41% of the 655 specimens. HRV infection was frequently observed in children under 2 years old (57.13%). HRV-A and HRV-C were detected in 18 (45%) and 22 (55%) specimens. All 40 HRV strains detected were classified into 29 genotypes. The molecular evolutionary rate of HRV-C was estimated to be 3.34 × 10-3 substitutions/site/year and was faster than HRV-A (7.79 × 10-4 substitutions/site/year). Children who experienced rhinorrhoea were more common in the HRV-C infection patients than HRV-A. The viral load was higher in HRV-C detection group than HRV-A detection group (p = 0.0148). The median peak symptom score was higher in patients with HRV-C infection as compared to HRV-A (p = 0.0543), even though the difference did not significance. CONCLUSION: This study revealed the molecular epidemiological characteristics of HRV in patients with respiratory infections in southern China. Children infected with HRV-C caused more severe disease characteristics than HRV-A, which might be connected with higher viral load in patients infected with HRV-C. These findings will provide valuable information for the pathogenic mechanism and treatment of HRV infection.

SARS-CoV-2 competes with host mRNAs for efficient translation by maintaining the mutations favorable for translation initiation.

During SARS-CoV-2 proliferation, the translation of viral RNAs is usually the rate-limiting step. Understanding the molecular details of this step is beneficial for uncovering the origin and evolution of SARS-CoV-2 and even for controlling the pandemic. To date, it is unclear how SARS-CoV-2 competes with host mRNAs for ribosome binding and efficient translation. We retrieved the coding sequences of all human genes and SARS-CoV-2 genes. We systematically profiled the GC content and folding energy of each CDS. Considering that some fixed or polymorphic mutations exist in SARS-CoV-2 and human genomes, all algorithms and analyses were applied to both pre-mutate and post-mutate versions. In SARS-CoV-2 but not human, the 5-prime end of CDS had lower GC content and less RNA structure than the 3-prime part, which was favorable for ribosome binding and efficient translation initiation. Globally, the fixed and polymorphic mutations in SARS-CoV-2 had created an even lower GC content at the 5-prime end of CDS. In contrast, no similar patterns were observed for the fixed and polymorphic mutations in human genome. Compared with human RNAs, the SARS-CoV-2 RNAs have less RNA structure in the 5-prime end and thus are more favorable of fast translation initiation. The fixed and polymorphic mutations in SARS-CoV-2 are further amplifying this advantage. This might serve as a strategy for SARS-CoV-2 to adapt to the human host.

Compelling Evidence Suggesting the Codon Usage of SARS-CoV-2 Adapts to Human After the Split From RaTG13.

SARS-CoV-2 needs to efficiently make use of the resources from hosts in order to survive and propagate. Among the multiple layers of regulatory network, mRNA translation is the rate-limiting step in gene expression. Synonymous codon usage usually conforms with tRNA concentration to allow fast decoding during translation. It is acknowledged that SARS-CoV-2 has adapted to the codon usage of human lungs so that the virus could rapidly proliferate in the lung environment. While this notion seems to nicely explain the adaptation of SARS-CoV-2 to lungs, it is unable to tell why other viruses do not have this advantage. In this study, we retrieve the GTEx RNA-seq data for 30 tissues (belonging to over 17 000 individuals). We calculate the RSCU (relative synonymous codon usage) weighted by gene expression in each human sample, and investigate the correlation of RSCU between the human tissues and SARS-CoV-2 or RaTG13 (the closest coronavirus to SARS-CoV-2). Lung has the highest correlation of RSCU to SARS-CoV-2 among all tissues, suggesting that the lung environment is generally suitable for SARS-CoV-2. Interestingly, for most tissues, SARS-CoV-2 has higher correlations with the human samples compared with the RaTG13-human correlation. This difference is most significant for lungs. In conclusion, the codon usage of SARS-CoV-2 has adapted to human lungs to allow fast decoding and translation. This adaptation probably took place after SARS-CoV-2 split from RaTG13 because RaTG13 is less perfectly correlated with human. This finding depicts the trajectory of adaptive evolution from ancestral sequence to SARS-CoV-2, and also well explains why SARS-CoV-2 rather than other viruses could perfectly adapt to human lung environment.

Fast evolution of SARS-CoV-2 driven by deamination systems in hosts.

As an RNA virus, the fast evolution of SARS-CoV-2 is driven by the extensive RNA deamination by the host cells.

SNPs or RNA modifications? Concerns on mutation-based evolutionary studies of SARS-CoV-2.

SARS-CoV-2 is still rampaging throughout the world while the many evolutionary studies on it are simultaneously springing up. Researchers have simply utilized the public RNA-seq data to find out the so-called SNPs in the virus genome. The evolutionary analyses were largely based on these mutations. Here, we claim that we reliably detected A-to-G RNA modifications in the RNA-seq data of SARS-CoV-2 with high signal to noise ratios, presumably caused by the host's deamination enzymes. Intriguingly, since SARS-CoV-2 is an RNA virus, it is technically impossible to distinguish SNPs and RNA modifications from the RNA-seq data alone without solid evidence, making it difficult to tell the evolutionary patterns behind the mutation spectrum. Researchers should clarify their biological significance before they automatically regard the mutations as SNPs or RNA modifications. This is not a problem for DNA organisms but should be seriously considered when we are investigating the RNA viruses.

Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation.

PURPOSE: Accurate segmentation of lung and infection in COVID-19 computed tomography (CT) scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID-19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. METHODS: To promote the development of data-efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID-19 cases, which contain current active research areas, for example, few-shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. RESULTS: Based on the state-of-the-art network, we provide more than 40 pretrained baseline models, which not only serve as out-of-the-box segmentation tools but also save computational time for researchers who are interested in COVID-19 lung and infection segmentation. We achieve average dice similarity coefficient (DSC) scores of 97.3%, 97.7%, and 67.3% and average normalized surface dice (NSD) scores of 90.6%, 91.4%, and 70.0% for left lung, right lung, and infection, respectively. CONCLUSIONS: To the best of our knowledge, this work presents the first data-efficient learning benchmark for medical image segmentation, and the largest number of pretrained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID-19 CT segmentation with limited data.

The divergence between SARS-CoV-2 and RaTG13 might be overestimated due to the extensive RNA modification

Aim: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread throughout the world. There is urgent need to understand the phylogeny, divergence and origin of SARS-CoV-2. Materials & methods: A recent study claimed that there was 17% divergence between SARS-CoV-2 and RaTG13 (a SARS-related coronaviruses) on synonymous sites by using sequence alignment. We re-analyzed the sequences of the two coronaviruses with the same methodology. Results: We found that 87% of the synonymous substitutions between the two coronaviruses could be potentially explained by the RNA modification system in hosts, with 65% contributed by deamination on cytidines (C-T mismatches) and 22% contributed by deamination on adenosines (A-G mismatches). Conclusion: Our results demonstrate that the divergence between SARS-CoV-2 and RaTG13 has been overestimated.

Mutation profile of over 4500 SARS-CoV-2 isolations reveals prevalent cytosine-to-uridine deamination on viral RNAs.

Aim: The inference of coronavirus evolution is largely based on mutations in SARS-CoV-2 genome. Misinterpretation of these mutations would mislead people about the evolution of SARS-CoV-2. Materials & methods: With 4521 lines of SARS-CoV-2, we obtained 3169 unique point mutation sites. We counted the numbers and calculated the minor allele frequency (MAF) of each mutation type. Results: Nearly half of the point mutations are C-T mismatches and 20% are A-G mismatches. The MAF of C-T and A-G mismatches is significantly higher than MAF of other mutation types. Conclusion: The excessive C-T mismatches do not resemble the random mutation profile. They are likely to be caused by the cytosine-to-uridine deamination system in hosts.

Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations.

Infection segmentation on chest CT plays an important role in the quantitative analysis of COVID-19. Developing automatic segmentation tools in a short period with limited labelled images has become an urgent need. Pseudo label-based semi-supervised method is a promising way to leverage unlabelled data to improve segmentation performance. Existing methods usually obtain pseudo labels by first training a network with limited labelled images and then inferring unlabelled images. However, these methods may generate obviously inaccurate labels and degrade the subsequent training process. To address these challenges, in this paper, an active contour regularized semi-supervised learning framework was proposed to automatically segment infections with few labelled images. The active contour regularization was realized by the region-scalable fitting (RSF) model which is embedded to the loss function of the network to regularize and refine the pseudo labels of the unlabelled images. We further designed a splitting method to separately optimize the RSF regularization term and the segmentation loss term with iterative convolution-thresholding method and stochastic gradient descent, respectively, which enable fast optimization of each term. Furthermore, we built a statistical atlas to show the infection spatial distribution. Extensive experiments on a small public dataset and a large scale dataset showed that the proposed method outperforms state-of-the-art methods with up to 5% in dice similarity coefficient and normalized surface dice, 10% in relative absolute volume difference and 8 mm in 95% Hausdorff distance. Moreover, we observed that the infections tend to occur at the dorsal subpleural lung and posterior basal segments that are not mentioned in current radiology reports and are meaningful to advance our understanding of COVID-19.

GC usage of SARS-CoV-2 genes might adapt to the environment of human lung expressed genes.

Understanding how SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) efficiently reproduces itself by taking resources from the human host could facilitate the development of drugs against the virus. SARS-CoV-2 translates its own proteins by using the host tRNAs, so that its GC or codon usage should fit that of the host cells. It is necessary to study both the virus and human genomes in the light of evolution and adaptation. The SARS-CoV-2 virus has significantly lower GC content and GC3 as compared to human. However, when we selected a set of human genes that have similar GC properties to SARS-CoV-2, we found that these genes were enriched in particular pathways. Moreover, these human genes have the codon composition perfectly correlated with the SARS-CoV-2, and were extraordinarily highly expressed in human lung tissues, demonstrating that the SARS-CoV-2 genes have similar GC usage as compared to the lung expressed human genes. RSCU (relative synonymous codon usage) and CAI (codon adaptation index) profiles further support the matching between SARS-CoV-2 and lungs. Our study indicates that SARS-CoV-2 might have adapted to the human lung environment by observing the high correlation between GC usage of SARS-CoV-2 and human lung genes, which suggests the GC content of SARS-CoV-2 is optimized to take advantage of human lung tissues.