SARS-CoV-2 Virus Culture, Genomic and Subgenomic RNA Load, and Rapid Antigen Test in Experimentally Infected Syrian Hamsters.

The duration of SARS-CoV-2 genomic RNA shedding is much longer than that of infectious SARS-CoV-2 in most COVID-19 patients. It is very important to determine the relationship between test results and infectivity for efficient isolation, contact tracing, and post-isolation. We characterized the duration of viable SARS-CoV-2, viral genomic and subgenomic RNA (gRNA and sgRNA), and rapid antigen test positivity in nasal washes, oropharyngeal swabs, and feces of experimentally infected Syrian hamsters. The duration of viral genomic RNA shedding is longer than that of viral subgenomic RNA, and far longer than those of rapid antigen test (RAgT) and viral culture positivity. The rapid antigen test results were strongly correlated with the viral culture results. The trend of subgenomic RNA is similar to that of genomic RNA, and furthermore, the subgenomic RNA load is highly correlated with the genomic RNA load. IMPORTANCE Our findings highlight the high correlation between rapid antigen test and virus culture results. The rapid antigen test would be an important supplement to real-time reverse transcription-RCR (RT-PCR) in early COVID-19 screening and in shortening the isolation period of COVID-19 patients. Because the subgenomic RNA load can be predicted from the genomic RNA load, measuring sgRNA does not add more benefit to determining infectivity than a threshold determined for gRNA based on viral culture.

Upregulation of PD-L1 by SPP1 mediates macrophage polarization and facilitates immune escape in lung adenocarcinoma.

Tumor-associated macrophages (TAMs) polarization represents a key regulatory process of tumor progression. However, the underlying mechanisms are unclear. This study aimed to investigate the relationship between secreted phosphoprotein 1 (SPP1) and TAMs in lung adenocarcinoma cells. THP-1 monocytes were differentiated into macrophages using PMA. PMA-treated THP-1 cells were co-cultured with human A549 cells culture supernatant. SPP1 expression in TAMs isolated from lung adenocarcinoma tissues and PMA-treated THP-1 cells were measured. Macrophage polarization was identified by flow cytometric analysis. Cell migration and apoptosis were assessed by Transwell migration assays and flow cytometric analysis, respectively. SPP1 is highly expressed in tumor tissues and TAMs isolated from patients with an advanced TNM stage, and also in PMA-treated THP-1 cells. Co-culture with A549 cells strongly induced SPP-1 expression as well as M2 polarization of THP-1 cells, but it had little effect on short hairpin SPP1 (shSPP1)-transfected THP-1 cells. Interestingly, programmed death ligand 1 (PD-L1), a critical regulator of M2 polarization, was downregulated in SPP1 knockdown THP-1 cells. Inhibition of PD-L1 induced a greater decline of the M2 markers IL-10 and Arg-1 but an increase in the M1 markers IL-12 and TNF-α. In addition, SPP1 knockdown in THP-1 cells can mitigate migration but promote apoptosis of A549 cells, and PD-L1 inhibition can further enhance this effect. THP-1 cells co-cultured with A549 cells attenuated CD4+ T-cell activation, whereas SPP1 inhibition restored T-cell activation. These results highlight the importance of SPP1 in mediating macrophage polarization and lung cancer evasion, suggesting a potential therapeutic target for lung cancer.

Functional study of a salt-inducible TaSR gene in Triticum aestivum.

The gene expression chip of a salt-tolerant wheat mutant under salt stress was used to clone a salt-induced gene with unknown functions. This gene was designated as TaSR (Triticum aestivum salt-response gene) and submitted to GenBank under accession number EF580107. Quantitative polymerase chain reaction (PCR) analysis showed that gene expression was induced by salt stress. Arabidopsis and rice (Oryza sativa) plants expressing TaSR presented higher salt tolerance than the controls, whereas AtSR mutant and RNA interference rice plants were more sensitive to salt. Under salt stress, TaSR reduced Na(+) concentration and improved cellular K(+) and Ca(2+) concentrations; this gene was also localized on the cell membrane. ß-Glucuronidase (GUS) staining and GUS fluorescence quantitative determination were conducted through fragmentation cloning of the TaSR promoter. Salt stress-responsive elements were detected at 588-1074 bp upstream of the start codon. GUS quantitative tests of the full-length promoter in different tissues indicated that promoter activity was highest in the leaf under salt stress. Bimolecular fluorescence complementation and yeast two-hybrid screening further showed the correlation of TaSR with TaPRK and TaKPP. In vitro phosphorylation of TaSR and TaPRK2697 showed that TaPRK2697 did not phosphorylate TaSR. This study revealed that the novel TaSR may be used to improve plant tolerance to salt stress.

Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs.

Heterogeneous graph neural networks play a crucial role in discovering discriminative node embeddings and relations from multi-relational networks. One of the key challenges in heterogeneous graph learning lies in designing learnable meta-paths, which significantly impact the quality of learned embeddings. In this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically explores meta-paths that involve multi-hop neighbors by aggregating multi-order adjacency matrices. The proposed model first constructs different orders of adjacency matrices from manually designed node connections. Next, AMOGCN fuses these various orders of adjacency matrices to create an intact multi-order adjacency matrix. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we employ a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node information propagation with multi-layer graph neural networks. Substantial experiments reveal that AMOGCN achieves superior semi-supervised classification performance compared with state-of-the-art competitors.

The unexpected effect of the compound microbial agent NP-M2 on microbial community dynamics in a nonylphenol-contaminated soil: the self-stability of soil ecosystem.

Background: Nonylphenol (NP) is widely recognized as a crucial environmental endocrine-disrupting chemical and persistent toxic substance. The remediation of NP-contaminated sites primarily relies on biological degradation. Compound microbial products, as opposed to pure strains, possess a greater variety of metabolic pathways and can thrive in a wider range of environmental conditions. This characteristic is believed to facilitate the synergistic degradation of pollutants. Limited research has been conducted to thoroughly examine the potential compatibility of compound microbial agents with indigenous microflora, their ability to function effectively in practical environments, their capacity to enhance the dissipation of NP, and their potential to improve soil physicochemical and biological characteristics. Methods: In order to efficiently eliminate NP in contaminated soil in an eco-friendly manner, a simulation study was conducted to investigate the impact of bioaugmentation using the functional compound microbial agent NP-M2 at varying concentrations (50 and 200 mg/L) on the dynamics of the soil microbial community. The treatments were set as follows: sterilized soil with 50 mg/kg NP (CK50) or 200 mg/kg NP (CK200); non-sterilized soil with 50 mg/kg NP (TU50) or 200 mg/kg NP (TU200); non-sterilized soil with the compound microbial agent NP-M2 at 50 mg/kg NP (J50) or 200 mg/kg NP (J200). Full-length 16S rRNA analysis was performed using the PacBio Sequel II platform. Results: Both the indigenous microbes (TU50 and TU200 treatments) and the application of NP-M2 (J50 and J200 treatments) exhibited rapid NP removal, with removal rates ranging from 93% to 99%. The application of NP-M2 further accelerated the degradation rate of NP for a subtle lag period. Although the different treatments had minimal impacts on the soil bacterial α-diversity, they significantly altered the ß-diversity and composition of the bacterial community. The dominant phyla were Proteobacteria (35.54%-44.14%), Acidobacteria (13.55%-17.07%), Planctomycetes (10.78%-11.42%), Bacteroidetes (5.60%-10.74%), and Actinobacteria (6.44%-8.68%). The core species were Luteitalea_pratensis, Pyrinomonas_methylaliphatogenes, Fimbriiglobus_ruber, Longimicrobium_terrae, and Massilia_sp003590855. The bacterial community structure and taxon distribution in polluted soils were significantly influenced by the activities of soil catalase, sucrase, and polyphenol oxidase, which were identified as the major environmental factors. Notably, the concentration of NP and, to a lesser extent, the compound microbial agent NP-M2 were found to cause major shifts in the bacterial community. This study highlights the importance of conducting bioremediation experiments in conjunction with microbiome assessment to better understand the impact of bioaugmentation/biostimulation on the potential functions of complex microbial communities present in contaminated soils, which is essential for bioremediation success.

AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing.

Graph convolutional network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-based models suffer from the notorious over-smoothing issue, owing to which shallow networks are extensively adopted. This may be problematic for complex graph datasets because a deeper GCN should be beneficial to propagating information across remote neighbors. Recent works have devoted effort to addressing over-smoothing problems, including establishing residual connection structure or fusing predictions from multilayer models. Because of the indistinguishable embeddings from deep layers, it is reasonable to generate more reliable predictions before conducting the combination of outputs from various layers. In light of this, we propose an alternating graph-regularized neural network (AGNN) composed of graph convolutional layer (GCL) and graph embedding layer (GEL). GEL is derived from the graph-regularized optimization containing Laplacian embedding term, which can alleviate the over-smoothing problem by periodic projection from the low-order feature space onto the high-order space. With more distinguishable features of distinct layers, an improved Adaboost strategy is utilized to aggregate outputs from each layer, which explores integrated embeddings of multi-hop neighbors. The proposed model is evaluated via a large number of experiments including performance comparison with some multilayer or multi-order graph neural networks, which reveals the superior performance improvement of AGNN compared with the state-of-the-art models.

Joint learning of feature and topology for multi-view graph convolutional network.

Graph convolutional network has been extensively employed in semi-supervised classification tasks. Although some studies have attempted to leverage graph convolutional networks to explore multi-view data, they mostly consider the fusion of feature and topology individually, leading to the underutilization of the consistency and complementarity of multi-view data. In this paper, we propose an end-to-end joint fusion framework that aims to simultaneously conduct a consistent feature integration and an adaptive topology adjustment. Specifically, to capture the feature consistency, we construct a deep matrix decomposition module, which maps data from different views onto a feature space obtaining a consistent feature representation. Moreover, we design a more flexible graph convolution that allows to adaptively learn a more robust topology. A dynamic topology can greatly reduce the influence of unreliable information, which acquires a more adaptive representation. As a result, our method jointly designs an effective feature fusion module and a topology adjustment module, and lets these two modules mutually enhance each other. It takes full advantage of the consistency and complementarity to better capture the more intrinsic information. The experimental results indicate that our method surpasses state-of-the-art semi-supervised classification methods.

Learnable Graph Convolutional Network With Semisupervised Graph Information Bottleneck.

Graph convolutional network (GCN) has gained widespread attention in semisupervised classification tasks. Recent studies show that GCN-based methods have achieved decent performance in numerous fields. However, most of the existing methods generally adopted a fixed graph that cannot dynamically capture both local and global relationships. This is because the hidden and important relationships may not be directed exhibited in the fixed structure, causing the degraded performance of semisupervised classification tasks. Moreover, the missing and noisy data yielded by the fixed graph may result in wrong connections, thereby disturbing the representation learning process. To cope with these issues, this article proposes a learnable GCN-based framework, aiming to obtain the optimal graph structures by jointly integrating graph learning and feature propagation in a unified network. Besides, to capture the optimal graph representations, this article designs dual-GCN-based meta-channels to simultaneously explore local and global relations during the training process. To minimize the interference of the noisy data, a semisupervised graph information bottleneck (SGIB) is introduced to conduct the graph structural learning (GSL) for acquiring the minimal sufficient representations. Concretely, SGIB aims to maximize the mutual information of both the same and different meta-channels by designing the constraints between them, thereby improving the node classification performance in the downstream tasks. Extensive experimental results on real-world datasets demonstrate the robustness of the proposed model, which outperforms state-of-the-art methods with fixed-structure graphs.

Effects of different laying periods on airborne bacterial diversity and antibiotic resistance genes in layer hen houses.

Poultry farms are a complex environment for close contact between humans and animals. Accumulating evidence has indicated that pathogens and drug resistance genes in chicken houses may pose a serious threat to public health and economic concerns. However, insufficient knowledge of the indoor aerosol microbiome and resistome profiles of layer hen houses hampers the understanding of their health effects. Environmental surveillance of antibiotic resistance may contribute to a better understanding and management of the human exposure risk of bioaerosols under the environmental conditions of chicken houses. In addition, the chicken house has a long operation cycle, and the bacterial diversity and antibiotic resistance genes of aerosols in different periods may be different. In this study, air samples were collected from 18 chicken houses on three farms, including the early laying period (EL), peak laying period (PL), and late laying period (LL). 16S rRNA gene sequencing and metagenomics were used to study the composition of the bacteria and resistome in aerosols of layer hen houses and the results showed that they varied with laying period. The highest alpha diversity of bacteria was observed in PL bioaerosols. The dominant bacterial phyla included Firmicutes, Bacteroidetes and Proteobacteria. Three potential pathogenic bacterial genera (Bacteroides, Corynebacterium and Fusobacterium) were found. The most abundant ARG type was aminoglycosides in all laying periods. In total, 22 possible ARG host genera were detected. ARG subtypes and abundance were both higher in LL. Network analysis also showed higher co-occurrence patterns between the bacteria and resistome in bioaerosols. The laying period plays an important role in the bacterial community and resistome in layer house aerosols.

A Point-of-Care Testing Device Utilizing Graphene-Enhanced Fiber Optic SPR Sensor for Real-Time Detection of Infectious Pathogens.

Timely detection of highly infectious pathogens is essential for preventing and controlling public health risks. However, most traditional testing instruments require multiple tedious steps and ultimately testing in hospitals and third-party laboratories. The sample transfer process significantly prolongs the time to obtain test results. To tackle this aspect, a portable fiber optic surface plasmon resonance (FO-SPR) device was developed for the real-time detection of infectious pathogens. The portable device innovatively integrated a compact FO-SPR sensing component, a signal acquisition and processing system, and an embedded power supply unit. A gold-plated fiber is used as the FO-SPR sensing probe. Compared with traditional SPR sensing systems, the device is smaller size, lighter weight, and higher convenience. To enhance the detection capacity of pathogens, a monolayer graphene was coated on the sensing region of the FO-SPR sensing probe. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was used to evaluate the performance of the portable device. The device can accurately detect the SARS-CoV-2 spike S1 protein in phosphate-buffered saline (PBS) and artificial saliva within just 20 min, and the device successfully detected cultured SARS-CoV-2 virus. Furthermore, the FO-SPR probe has long-term stability, remaining stable for up to 8 days. It could distinguish between the SARS-CoV-2 spike protein and the MERS-CoV spike protein. Hence, this FO-SPR device provides reliable, rapid, and portable access to test results. It provides a promising point-of-care testing (POCT) tool for on-site screening of infectious pathogens.

Learning Deep Sparse Regularizers With Applications to Multi-View Clustering and Semi-Supervised Classification.

Sparsity-constrained optimization problems are common in machine learning, such as sparse coding, low-rank minimization and compressive sensing. However, most of previous studies focused on constructing various hand-crafted sparse regularizers, while little work was devoted to learning adaptive sparse regularizers from given input data for specific tasks. In this paper, we propose a deep sparse regularizer learning model that learns data-driven sparse regularizers adaptively. Via the proximal gradient algorithm, we find that the sparse regularizer learning is equivalent to learning a parameterized activation function. This encourages us to learn sparse regularizers in the deep learning framework. Therefore, we build a neural network composed of multiple blocks, each being differentiable and reusable. All blocks contain learnable piecewise linear activation functions which correspond to the sparse regularizer to be learned. Furthermore, the proposed model is trained with back propagation, and all parameters in this model are learned end-to-end. We apply our framework to multi-view clustering and semi-supervised classification tasks to learn a latent compact representation. Experimental results demonstrate the superiority of the proposed framework over state-of-the-art multi-view learning models.

The Distribution Characteristics of Aerosol Bacteria in Different Types of Pig Houses.

With the development of modern pig raising technology, the increasing density of animals in pig houses leads to the accumulation of microbial aerosols in pig houses. It is an important prerequisite to grasp the characteristics of bacteria in aerosols in different pig houses to solve the problems of air pollution and disease prevention and control in different pig houses. This work investigated the effects of growth stages on bacterial aerosol concentrations and bacterial communities in pig houses. Three traditional types of closed pig houses were studied: farrowing (FAR) houses, weaning (WEA) houses, and fattening (FAT) houses. The Andersen six-stage sampler and high-volume air sampler were used to assess the concentrations and size distribution of airborne bacteria, and 16S rRNA gene sequencing was used to identify the bacterial communities. We found that the airborne bacterial concentration, community richness, and diversity index increased with pig age. We found that Acinetobacter, Erysipelothrix, Streptococcus, Moraxella, and Aerococcus in the microbial aerosols of pig houses have the potential risk of causing disease. These differences lead us to believe that disinfection strategies for pig houses should involve a situational focus on environmental aerosol composition on a case-by-case basis.

Oregano Oil Combined with Macleaya Cordata Oral Solution Improves the Growth Performance and Immune Response of Broilers.

The abuse of AGPs in animal husbandry has led to severe problems such as drug resistance and ecological, and environmental destruction, which seriously threaten human health and public health security. In recent years, extracts of oregano oil and macleaya cordata have become a hot spot in the research and application of AGP substitutes for their safety and high efficiency. This study is the first to report the effect of oregano oil combined with macleaya cordata oral solution on broiler growth performance. A total of 960 one-day-old broiler chickens were randomly divided into four treatment groups (240 chickens per group). Each treatment group was divided into six replicate groups (40 birds per replicate group). There were four groups in this study: the solvent control group, the oregano essential oil combined with macleaya cordata extract oral solution group (OS group), the oregano essential oil oral solution group (OEO group), and the macleaya cordata extract oral solution group (MCE group). Two chickens from each replicate group were collected and mixed into a composite sample. Six composite samples were obtained for each treatment group. The results showed that the oregano oil combined with macleaya cordata oral solution significantly improved the growth performance of broiler chickens. At the same time, serum biochemical indices, serum antioxidant indices, serum immune indices, serum cytokines, and intestinal morphology were significantly improved by the OS group. This study shows that oregano oil combined with macleaya cordata oral solution has substantial potential to be an alternative to AGPs for broilers.

SARS-CoV-2-related pangolin coronavirus exhibits similar infection characteristics to SARS-CoV-2 and direct contact transmissibility in hamsters.

To date, intermediate hosts of SARS-CoV-2 remain obscure and controversial. Several studies have shown that SARS-CoV-2-related pangolin coronavirus (Pangolin-CoV) has a high sequence similarity to SARS-CoV-2 and might be the initial source of SARS-CoV-2; however, the biological characteristics of Pangolin-CoV are still largely unknown. In this study, we evaluated the pathogenicity and transmissibility of Pangolin-CoV in Syrian golden hamsters Mesocricetus auratus (Linnaeus, 1758) and compared it with SARS-CoV-2. Pangolin-CoV could effectively infect hamsters, showed similar tissue tropism to SARS-CoV-2 and replicated efficiently in the respiratory system and brain. The infected hamsters had no weight loss but had obvious viral shedding and lung pathological injury. Notably, Pangolin-CoV could transmit between hamsters by direct contact but not via aerosols, and the infected hamsters could exhale infectious viral aerosols (>1 µm). These results highlight the importance of continuous monitoring of coronaviruses in pangolins owing to the potential threat of Pangolin-CoV to human health.

Anti-Influenza Effect and Mechanisms of Lentinan in an ICR Mouse Model.

Influenza virus is a serious threat to global human health and public health security. There is an urgent need to develop new anti-influenza drugs. Lentinan (LNT) has attracted increasing attention in recent years. As potential protective agent, LNT has been shown to have anti-tumor, anti-inflammatory, and antiviral properties. However, there has been no further research into the anti-influenza action of lentinan in vivo, and the mechanism is still not fully understood. In this study, the anti-influenza effect and mechanism of Lentinan were studied in the Institute of Cancer Research (ICR) mouse model. The results showed that Lentinan had a high degree of protection in mice against infection with influenza A virus, delayed the emergence of clinical manifestations, improved the survival rate of mice, significantly prolonged the middle survival days, attenuated the weight loss, and reduced the lung coefficient of mice. It alleviated the pathological damage of mice infected with the influenza virus and improved blood indices. Lentinan treatment considerably inhibited inflammatory cytokine (TNF-α, IL-1ß, IL-4, IL-5, IL-6) levels in the serum and lung and improved IFN-γ cytokine levels, which reduced cytokine storms caused by influenza virus infection. The underlying mechanisms of action involved Lentinan inhibiting the inflammatory response by regulating the TLR4/MyD88 signaling pathway. This study provides a foundation for the clinical application of Lentinan, and provides new insight into the development of novel immunomodulators.

Epidemiological Investigation of Porcine Pseudorabies Virus in Hebei Province, China, 2017-2018.

Pseudorabies (PR) is a serious disease affecting the pig industry in China, and it is very important to understand the epidemiology of pseudorabies virus (PRV). In the present study, 693 clinical samples were collected from Bartha-K61 vaccinated pigs with symptoms of suspected PRV infection between January 2017 and December 2018. All cases were referred for full clinical autopsy with detailed examination of histopathological examination, virus isolation and genetic evolution analysis of the PRV glycoprotein E (gE) gene. In addition, PRV gE antibodies in 3,449 serum samples were detected by the enzyme-linked immunosorbent assay (ELISA). The clinical data revealed that abortion and stillbirth are the most frequent appearances in pregnant sows of those cases. Histopathological examination exhibited a variety of pathological lesions, such as lobar pneumonia, hepatitis, lymphadenitis, nephritis, and typical nonsuppurative encephalitis. A total of 248 cases tested positive for the PRV gE gene. 11 PRV variants were isolated and confirmed by gE gene sequencing and phylogenetic analysis. These strains had 97.1%-100.0% nucleotide homology with the PRV reference strains. Notably, the isolated strains were highly homologous and clustered in the same branch as HSD-1/2019, which caused human acute encephalitis. Serological tests showed that the positive rate of PRV gE antibody in the 3449 serum samples collected from the Hebei Province was 46.27%. In conclusion, PRV variant strains Are high prevalence in the Hebei Province, which not only causes huge economic losses to the breeding industry but also potentially poses a threat to public health.

Aerosol Transmission of the Pandemic SARS-CoV-2 and Influenza A Virus Was Blocked by Negative Ions.

The pandemic of respiratory diseases, such as coronavirus disease 2019 (COVID-19) and influenza, has imposed significant public health and economic burdens on the world. Wearing masks is an effective way to cut off the spread of the respiratory virus. However, due to cultural differences and uncomfortable wearing experiences, not everyone is willing to wear masks; there is an urgent need to find alternatives to masks. In this study, we tested the disinfection effect of a portable ionizer on pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (strain V34) and influenza A virus (strain CA04). Negative ions significantly reduced the concentration of particulate matter in the air above and effectively disinfected viruses stuck to the solid plate at the level of both nucleic acid and virus titer. The disinfection efficiency was >99.8% after 1-h exposure. Moreover, negative ions effectively disinfected aerosolized viruses; the disinfection efficiency was more than 87.77% after purification for 10 min. Furthermore, negative ions had a significant protective effect on susceptible animals exposed to viral aerosols. When the negative ionizer was switched from off to on, the inhalation 50% infective dose (ID50) for golden hamsters challenged with SARS-CoV-2 rose from 9.878 median tissue culture infective dose (TCID50) [95% confidence interval (CI), 6.727-14.013 TCID50] to 43.891 TCID50 (95% CI, 29.31-76.983 TCID50), and the inhalation ID50 for guinea pigs challenged with influenza A virus rose from 6.696 TCID50 (95% CI, 3.251-9.601 TCID50) to 28.284 TCID50 (95% CI, 19.705-40.599 TCID50). In the experiment of transmission between susceptible animals, negative ions 100% inhibited the aerosol transmission of SARS-CoV-2 and influenza A virus. Finally, we tested the safety of negative ion exposure. Balb/c mice exposed to negative ions for 4 weeks showed no abnormalities in body weight, blood routine analysis, and lung pathology. Our study demonstrates that air ions can be used as a safe and effective means of blocking respiratory virus transmission and contribute to pandemic prevention and control.

Pathogenicity and Transmissibility of Clade 2.3.4.4h H5N6 Avian Influenza Viruses in Mammals.

Avian influenza viruses (AIVs) have the potential for cross-species transmission and pandemics. In recent years, clade 2.3.4.4 H5N6 AIVs are prevalent in domestic poultry, posing a threat to the domestic poultry industry and public health. In this study, two strains of H5N6 AIVs were isolated from chickens in Hebei, China, in 2019: A/chicken/Hebei/HB1907/2019(H5N6) and A/chicken/Hebei/HB1905/2019(H5N6). Phylogenetic analysis showed that both viral HA genes clustered in the 2.3.4.4h clade. Receptor binding analysis showed that the HB1905 strain preferentially binds to α-2,3-linked sialic acid (SA) receptors, while the HB1907 strain preferentially binds to α-2,3- and α-2,6-linked sialic acid (SA) receptors. During early infection, the HB1907 strain is highly replicable in MDCK cells, more so than the HB1905 strain. Pathogenicity assays in mice showed that both viruses could replicate in the lungs without prior adaptation, with HB1907 being more highly pathogenic in mice than the HB1905 strain. Significantly, both the HB1905 and HB1907 strains can be transmitted through direct contact among guinea pigs, but the transmission efficiency of the HB1907 strain through contact between guinea pigs is much greater than that of the HB1905 strain. These results strengthen the need for ongoing surveillance and early warning of H5N6 AIVs in poultry.

Rapid detection of influenza A viruses using a real-time reverse transcription recombinase-aided amplification assay.

Introduction: Influenza A viruses (IAVs) are important pathogens of respiratory infections, causing not only seasonal influenza but also influenza pandemics and posing a global threat to public health. IAVs infection spreads rapidly, widely, and across species, causing huge losses, especially zoonotic IAVs infections that are more harmful. Fast and sensitive detection of IAVs is critical for controlling the spread of this disease. Methods: Here, a real-time reverse transcription recombinase-aided amplification (real-time RT-RAA) assay targeting conserved positions in the matrix protein gene (M gene) of IAVs, is successfully established to detect IAVs. The assay can be completed within 20 min at 42°C. Results: The sensitivity of the real-time RT-RAA assay was 142 copies per reaction at 95% probability, which was comparable to the sensitivity of the RT-qPCR assay. The specificity assay showed that the real-time RT-RAA assay was specific to IAVs, and there was no cross-reactivity with other important viruses. In addition, 100%concordance between the real-time RT-RAA and RT-qPCR assays was achieved after testing 120 clinical specimens. Discussion: The results suggested that the real-time RT-RAA assay we developed was a specific, sensitive and reliable diagnostic tool for the rapid detection of IAVs.