Investigation of the safety and protective efficacy of an attenuated and marker M. bovis-BoHV-1 combined vaccine in bovines.

Bovine respiratory disease (BRD) is one of the most common diseases in the cattle industry worldwide; it is caused by multiple bacterial or viral coinfections, of which Mycoplasma bovis (M. bovis) and bovine herpesvirus type 1 (BoHV-1) are the most notable pathogens. Although live vaccines have demonstrated better efficacy against BRD induced by both pathogens, there are no combined live and marker vaccines. Therefore, we developed an attenuated and marker M. bovis-BoHV-1 combined vaccine based on the M. bovis HB150 and BoHV-1 gG-/tk- strain previously constructed in our lab and evaluated in rabbits. This study aimed to further evaluate its safety and protective efficacy in cattle using different antigen ratios. After immunization, all vaccinated cattle had a normal rectal temperature and mental status without respiratory symptoms. CD4+, CD8+, and CD19+ cells significantly increased in immunized cattle and induced higher humoral and cellular immune responses, and the expression of key cytokines such as IL-4, IL-12, TNF-α, and IFN-γ can be promoted after vaccination. The 1.0 × 108 CFU of M. bovis HB150 and 1.0 × 106 TCID50 BoHV-1 gG-/tk- combined strain elicited the most antibodies while significantly increasing IgG and cellular immunity after challenge. In conclusion, the M. bovis HB150 and BoHV-1 gG-/tk- combined strain was clinically safe and protective in calves; the mix of 1.0 × 108 CFU of M. bovis HB150 and 1.0 × 106 TCID50 BoHV-1 gG-/tk- strain was most promising due to its low amount of shedding and highest humoral and cellular immune responses compared with others. This study introduces an M. bovis-BoHV-1 combined vaccine for application in the cattle industry.

Antimicrobial polysaccharide hydrogels embedded with methyl-ß-cyclodextrin/thyme oil inclusion complexes for exceptional mechanical performance and chilled chicken breast preservation.

While hydrogels have potential for food packaging, limited research on hydrogels with excellent mechanical performance and antibacterial activity for preserving chicken breasts. Herein, we created antibacterial hydrogels by embedding methyl-ß-cyclodextrin/thyme oil inclusion complexes (MCD/TO-ICs) into a polyvinyl alcohol matrix containing dendrobium polysaccharides and guar gum in varying ratios using freeze-thaw cycling method. The resulting hydrogels exhibited a more compact structure than those without MCD/TO-ICs, enhancing thermal stability and increasing glass transition temperature due to additional intermolecular interactions between polymer chains that inhibited chain movement. XRD analysis showed no significant changes in crystalline phase, enabling formation of a 3D network through abundant hydrogen bonding. Moreover, the hydrogel demonstrated exceptional durability, with a toughness of 350 ± 25 kJ/m3 and adequate tearing resistance of 340 ± 30 J/m2, capable of lifting 3 kg weight, 1200 times greater than the hydrogel itself. Additionally, the hydrogels displayed excellent antimicrobial activity and antioxidant properties. Importantly, the hydrogels effectively maintained TVB-N levels and microbial counts within acceptable ranges, preserving sensory properties and extending the shelf life of chilled chicken breasts by four days. This study highlights the potential of MCD/TO-IC-incorporated polysaccharide hydrogels as safe and effective active packaging solutions for preserving chilled chicken in food industry.

Ammonia-responsive chitosan/polymethacrylamide double network hydrogels with high-stretchability, fatigue resistance and anti-freezing for real-time chicken breast spoilage monitoring.

Hydrogels are a promising option for detecting food spoilage in humid conditions, but current indicators are prone to mechanical flaws, posing a concern for packaging systems that require strong mechanical properties. Herein, a double network hydrogel was prepared by polymerizing methacrylamide in a chitosan system with aluminum chloride and glycerol. The resulting hydrogel demonstrated high stretchability (strain >1500 %), notch insensitivity, excellent fatigue resistance, and exceptional anti-freezing capabilities even at -21 °C. When incorporating bromothymol blue (BB) or methyl red (MR), or mixtures of these dyes into the hydrogels as indicators, they exhibited sensitive colorimetric responses to pH and NH3 levels at different temperatures. Hydrogels immobilizing BB to MR ratios of 1:1 and 1:2 displayed clearer and more sensitive color responses when packed into chicken breast, with a sensitivity level of 1.5 ppm of total volatile basic nitrogen (TVB-N). This color response correlated positively with the accumulation of TVB-N on the packaging during storage at both 25 °C and 4 °C, providing sensitive indications of chicken breast deterioration. Overall, the developed hydrogels and indicators demonstrate enhanced performance characteristics, including excellent mechanical strength and highly NH3-sensitive color responses, making significant contributions to the food spoilage detection and intelligent packaging systems field.

Nicotinamide adenine dinucleotide treatment confers resistance to neonatal ischemia and hypoxia: effects on neurobehavioral phenotypes.

JOURNAL/nrgr/04.03/01300535-202412000-00031/figure1/v/2024-04-08T165401Z/r/image-tiff Neonatal hypoxic-ischemic brain injury is the main cause of hypoxic-ischemic encephalopathy and cerebral palsy. Currently, there are few effective clinical treatments for neonatal hypoxic-ischemic brain injury. Here, we investigated the neuroprotective and molecular mechanisms of exogenous nicotinamide adenine dinucleotide, which can protect against hypoxic injury in adulthood, in a mouse model of neonatal hypoxic-ischemic brain injury. In this study, nicotinamide adenine dinucleotide (5 mg/kg) was intraperitoneally administered 30 minutes before surgery and every 24 hours thereafter. The results showed that nicotinamide adenine dinucleotide treatment improved body weight, brain structure, adenosine triphosphate levels, oxidative damage, neurobehavioral test outcomes, and seizure threshold in experimental mice. Tandem mass tag proteomics revealed that numerous proteins were altered after nicotinamide adenine dinucleotide treatment in hypoxic-ischemic brain injury mice. Parallel reaction monitoring and western blotting confirmed changes in the expression levels of proteins including serine (or cysteine) peptidase inhibitor, clade A, member 3N, fibronectin 1, 5'-nucleotidase, cytosolic IA, microtubule associated protein 2, and complexin 2. Proteomics analyses showed that nicotinamide adenine dinucleotide ameliorated hypoxic-ischemic injury through inflammation-related signaling pathways (e.g., nuclear factor-kappa B, mitogen-activated protein kinase, and phosphatidylinositol 3 kinase/protein kinase B). These findings suggest that nicotinamide adenine dinucleotide treatment can improve neurobehavioral phenotypes in hypoxic-ischemic brain injury mice through inflammation-related pathways.

Prevalence, Molecular Characteristics and Virulence Identification of Bovine Parainfluenza Virus Type 3 in China.

Bovine parainfluenza virus type 3 (BPIV-3) is one of the major pathogens of the bovine respiratory disease complex (BRDC). BPIV-3 surveillance in China has been quite limited. In this study, we used PCR to test 302 cattle in China, and found that the positive rate was 4.64% and the herd-level positive rate was 13.16%. Six BPIV-3C strains were isolated and confirmed by electron microscopy, and their titers were determined. Three were sequenced by next-generation sequencing (NGS). Phylogenetic analyses showed that all isolates were most closely related to strain NX49 from Ningxia; the genetic diversity of genotype C strains was lower than strains of genotypes A and B; the HN, P, and N genes were more suitable for genotyping and evolutionary analyses of BPIV-3. Protein variation analyses showed that all isolates had mutations at amino acid sites in the proteins HN, M, F, and L. Genetic recombination analyses provided evidence for homologous recombination of BPIV-3 of bovine origin. The virulence experiment indicated that strain Hubei-03 had the highest pathogenicity and could be used as a vaccine candidate. These findings apply an important basis for the precise control of BPIV-3 in China.

Complete genome sequence of a novel alternavirus isolated from the phytopathogenic fungus Colletotrichum fioriniae.

A novel double-strand RNA (dsRNA) mycovirus, named "Colletotrichum fioriniae alternavirus1" (CfAV1), was isolated from the strain CX7 of Colletotrichum fioriniae, the causal agent of walnut anthracnose. The complete genome of CfAV1 is composed of three dsRNA segments: dsRNA1 (3528 bp), dsRNA2 (2485 bp), and dsRNA3 (2481 bp). The RNA-dependent RNA polymerase (RdRp) is encoded by dsRNA1, while both dsRNA2 and dsRNA3 encode hypothetical proteins. Based on multiple sequence alignments and phylogenetic analysis, CfAV1 is identified as a new member of the family Alternaviridae. This is the first report of an alternavirus that infects the phytopathogenic fungus C. fioriniae.

A DNA tetrahedron dimer for dual membrane protein logic recognition and interaction inhibition.

Here, we report a DNA tetrahedron dimer for dual membrane protein logic recognition and interaction inhibition. The DNA tetrahedron dimer not only detects dual proteins that are both overexpressed on target cells in "AND" logic, but also inhibits protein interaction by steric hindrance to suppress cell proliferation, offering new insights for cancer cell diagnosis and treatment.

Utilizing graph convolutional networks for identification of mild cognitive impairment from single modal fMRI data: a multiconnection pattern combination approach.

Mild cognitive impairment plays a crucial role in predicting the early progression of Alzheimer's disease, and it can be used as an important indicator of the disease progression. Currently, numerous studies have focused on utilizing the functional brain network as a novel biomarker for mild cognitive impairment diagnosis. In this context, we employed a graph convolutional neural network to automatically extract functional brain network features, eliminating the need for manual feature extraction, to improve the mild cognitive impairment diagnosis performance. However, previous graph convolutional neural network approaches have primarily concentrated on single modes of brain connectivity, leading to a failure to leverage the potential complementary information offered by diverse connectivity patterns and limiting their efficacy. To address this limitation, we introduce a novel method called the graph convolutional neural network with multimodel connectivity, which integrates multimode connectivity for the identification of mild cognitive impairment using fMRI data and evaluates the graph convolutional neural network with multimodel connectivity approach through a mild cognitive impairment diagnostic task on the Alzheimer's Disease Neuroimaging Initiative dataset. Overall, our experimental results show the superiority of the proposed graph convolutional neural network with multimodel connectivity approach, achieving an accuracy rate of 92.2% and an area under the Receiver Operating Characteristic (ROC) curve of 0.988.

Multipattern graph convolutional network-based autism spectrum disorder identification.

The early diagnosis of autism spectrum disorder (ASD) has been extensively facilitated through the utilization of resting-state fMRI (rs-fMRI). With rs-fMRI, the functional brain network (FBN) has gained much attention in diagnosing ASD. As a promising strategy, graph convolutional networks (GCN) provide an attractive approach to simultaneously extract FBN features and facilitate ASD identification, thus replacing the manual feature extraction from FBN. Previous GCN studies primarily emphasized the exploration of topological simultaneously connection weights of the estimated FBNs while only focusing on the single connection pattern. However, this approach fails to exploit the potential complementary information offered by different connection patterns of FBNs, thereby inherently limiting the performance. To enhance the diagnostic performance, we propose a multipattern graph convolution network (MPGCN) that integrates multiple connection patterns to improve the accuracy of ASD diagnosis. As an initial endeavor, we endeavored to integrate information from multiple connection patterns by incorporating multiple graph convolution modules. The effectiveness of the MPGCN approach is evaluated by analyzing rs-fMRI scans from a cohort of 92 subjects sourced from the publicly accessible Autism Brain Imaging Data Exchange database. Notably, the experiment demonstrates that our model achieves an accuracy of 91.1% and an area under ROC curve score of 0.9742. The implementation codes are available at https://github.com/immutableJackz/MPGCN.

Grass carp (Ctenopharyngodon idella) deacetylase SIRT1 targets p53 to suppress apoptosis in a KAT8 dependent or independent manner.

Sirtuin1 (SIRT1) is known as a deacetylase to control various physiological processes. In mammals, SIRT1 inhibits apoptotic process, but the detailed mechanism is not very clear. Here, our study revealed that grass carp (Ctenopharyngodon idella) SIRT1 (CiSIRT1, MN125614.1) inhibits apoptosis through targeting p53 in a KAT8-dependent or a KAT8-independent manner. In CIK cells, CiSIRT1 over-expression results in significant decrease of some apoptotic gene expressions, including Bax/Bcl2, caspase3 and caspase9, whereas CiKAT8 or Cip53 facilitates the induction of apoptosis. Because CiSIRT1 separately interacted with CiKAT8 and Cip53, we speculated that CiSIRT1 blocked apoptosis may be by virtue of KAT8-p53 axis or directly by p53. In a KAT8-dependent manner, CiSIRT1 interacted with CiKAT8, then reduced the acetylation of CiKAT8 and subsequently promoted its degradation. Then, CiKAT8 acetylated p53 and induced p53-mediated apoptosis. MYST domain of CiKAT8 was critical in this pathway. In a KAT8-independent manner, CiSIRT1 also inhibited p53-induced apoptosis by directly deacetylating p53 and promoting the degradation of p53. Generally, these findings uncovered two pathways in which CiSIRT1 decreases the acetylation of p53 via a KAT8-dependent or a KAT8-independent manner.

Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome.

Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease.

Accurately predicting the risk of unfavorable outcomes after endovascular coil therapy in patients with aneurysmal subarachnoid hemorrhage: an interpretable machine learning model.

BACKGROUND: Despite endovascular coiling as a valid modality in treatment of aneurysmal subarachnoid hemorrhage (aSAH), there is a risk of poor prognosis. However, the clinical utility of previously proposed early prediction tools remains limited. We aimed to develop a clinically generalizable machine learning (ML) models for accurately predicting unfavorable outcomes in aSAH patients after endovascular coiling. METHODS: Functional outcomes at 6 months after endovascular coiling were assessed via the modified Rankin Scale (mRS) and unfavorable outcomes were defined as mRS 3-6. Five ML algorithms (logistic regression, random forest, support vector machine, deep neural network, and extreme gradient boosting) were used for model development. The area under precision-recall curve (AUPRC) and receiver operating characteristic curve (AUROC) was used as main indices of model evaluation. SHapley Additive exPlanations (SHAP) method was applied to interpret the best-performing ML model. RESULTS: A total of 371 patients were eventually included into this study, and 85.4% of them had favorable outcomes. Among the five models, the DNN model had a better performance with AUPRC of 0.645 (AUROC of 0.905). Postoperative GCS score, size of aneurysm, and age were the top three powerful predictors. The further analysis of five random cases presented the good interpretability of the DNN model. CONCLUSION: Interpretable clinical prediction models based on different ML algorithms have been successfully constructed and validated, which would serve as reliable tools in optimizing the treatment decision-making of aSAH. Our DNN model had better performance to predict the unfavorable outcomes at 6 months in aSAH patients compared with Yan's nomogram model.

Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data.

Functional connectome has revealed remarkable potential in the diagnosis of neurological disorders, e.g. autism spectrum disorder. However, existing studies have primarily focused on a single connectivity pattern, such as full correlation, partial correlation, or causality. Such an approach fails in discovering the potential complementary topology information of FCNs at different connection patterns, resulting in lower diagnostic performance. Consequently, toward an accurate autism spectrum disorder diagnosis, a straightforward ambition is to combine the multiple connectivity patterns for the diagnosis of neurological disorders. To this end, we conduct functional magnetic resonance imaging data to construct multiple brain networks with different connectivity patterns and employ kernel combination techniques to fuse information from different brain connectivity patterns for autism diagnosis. To verify the effectiveness of our approach, we assess the performance of the proposed method on the Autism Brain Imaging Data Exchange dataset for diagnosing autism spectrum disorder. The experimental findings demonstrate that our method achieves precise autism spectrum disorder diagnosis with exceptional accuracy (91.30%), sensitivity (91.48%), and specificity (91.11%).

Machine Learning-Enabled Intelligent Gesture Recognition and Communication System Using Printed Strain Sensors.

Gesture contains abundant and complicated information in daily life; as a consequence, gesture recognition attracts a wide range of application prospects and academic values as an important way of achieving human-machine interactions (HMIs). Here, we report an intelligent system consisting of a smart glove made by printed CNT-graphene/PDMS strain sensors. The smart glove shows excellent fitness, comfort, and lightness for human hands. Inspired by machine learning strategies, several objects and gestures can be well classified and implemented by a customized artificial neural network. Several data sets of different sign language gestures and object-grabbing gestures were established, and the result shows that the intelligent system can achieve an average accuracy of 97% and up to 99.4% for a number of gesture groups. Moreover, a robot hand is connected to this system, which is able to react to the motion of human hands with certain gestures where simple sign communication is achieved. These features provide a feasible practical application scheme for gesture recognition in HMIs.

Extrusion-blown oxidized starch/poly(butylene adipate-co-terephthalate) biodegradable active films with adequate material properties and antimicrobial activities for chilled pork preservation.

Food safety concerns from spoilage and non-degradable packaging risk human health. Progress made in biodegradable plastic films, but limited study on biomass composite films with favorable morphological, mechanical, and inherent antibacterial properties for fresh meat preservation. Herein, we present a versatile packaging film created through the extrusion blowing process, combining oxidized starch (OST) with poly(butylene adipate-co-terephthalate) (PBAT). SEM analysis revealed even distribution of spherical OST particles on film's surface. FTIR spectra revealed new intermolecular hydrogen bonds between OST and PBAT. While combining OST slightly reduced tensile properties, all composite films met the required strength of 16.5 ± 1.39 MPa. Notably, films with 40 % OST showed over 98 % antibacterial rate against Staphylococcus aureus within 2 h. pH wasn't the main cause of bacterial growth inhibition; OST hindered growth by interfering with nutrient absorption and metabolism due to its carboxyl groups. Additionally, OST disrupted bacterial membrane integrity and cytoplasmic membrane potential. Remarkably, the OST/PBAT film excellently preserved chilled fresh pork, maintaining TVB-N level at 12.6 mg/100 g on day 6, microbial count at 105 CFU/g within 6-10 days, and sensory properties for 8 days. It extended pork's shelf life by two days compared to polyethylene film, suggesting an alternative to a synthetic material.

Wavelet transform-based frequency self-adaptive model for functional brain network.

The accurate estimation of functional brain networks is essential for comprehending the intricate relationships between different brain regions. Conventional methods such as Pearson Correlation and Sparse Representation often fail to uncover concealed information within diverse frequency bands. To address this limitation, we introduce a novel frequency-adaptive model based on wavelet transform, enabling selective capture of highly correlated frequency band sequences. Our approach involves decomposing the original time-domain signal from resting-state functional magnetic resonance imaging into distinct frequency domains, thus constructing an adjacency matrix that offers enhanced separation of features across brain regions. Comparative analysis demonstrates the superior performance of our proposed model over conventional techniques, showcasing improved clarity and distinctiveness. Notably, we achieved the highest accuracy rate of 89.01% using Sparse Representation based on Wavelet Transform, outperforming Pearson Correlation based on Wavelet Transform with an accuracy of 81.32%. Importantly, our method optimizes raw data without significantly altering feature topology, rendering it adaptable to various functional brain network estimation approaches. Overall, this innovation holds the potential to advance the understanding of brain function and furnish more accurate samples for future research and clinical applications.

Characterization of a novel hypovirus isolated from the phytopathogenic fungus Colletotrichum camelliae.

Some members of genus Colletotrichum are important plant pathogens. Here, we report a novel positive single-stranded RNA virus, Colletotrichum camelliae hypovirus 1 (CcHV1), from strain GXNN11-2 of Colletotrichum camelliae. The complete genome of CcHV1 is 9907 nucleotides (nt) in length and contains a single large open reading frame (ORF) from nt 352 to 9006. This ORF encodes a polyprotein with four conserved domains, namely UDP-glycosyltransferase, RNA-dependent RNA polymerase (RdRp), peptidase, and DEAD-like helicase. The CcHV1 polyprotein shares the highest similarity with Fusarium concentricum hypovirus 1. Phylogenetic analysis indicated that CcHV1 clustered with members of the genus Betahypovirus within the family Hypoviridae. This is the first report of a hypovirus in a member of the genus Colletotrichum.

An "ON-OFF" fluorescent sensor based on a novel zinc-based flower-like structured metal-organic framework for sequential detection of deferasirox and tigecycline.

Deferasirox (DEF) is essential for patients with thalassemia requiring long-term transfusion therapy. Tigecycline (TIGE) is a first-line drug for the clinical treatment of complex, severe bacterial infections. The two drugs can be coordinated to treat Pseudomonas aeruginosa infections. Easy and efficient techniques for monitoring these two drugs in biological samples are few. Metal-organic framework (Zn-MOF) prepared from zinc nitrate hexahydrate and dithioglycolic acid has a flower structure. Interestingly, Zn-MOF can cause DEF to aggregate on it and induce DEF luminescence. The principle may be that Zn-MOF limits the vibration and rotation of DEF to avoid its nonradiative jump, which triggers aggregation-induced emission (AIE) and exhibits intense fluorescence. Further investigation revealed that TIGE could decompose Zn-MOF, thus alleviating the inhibitory effect of Zn-MOF on DEF and reducing the fluorescence intensity of DEF@Zn-MOF. A DEF/TIGE detection biosensor was created based on the fluorescence "turn-on" effect of Zn-MOF on DEF and the fluorescence "turn-off" effect of TIGE on DEF@Zn-MOF. The proposed technique was subsequently used to identify DEF/TIGE levels in pharmaceuticals and human plasma. The mean values for the percentage of the labeled amount of DEF/TIGE in DEF dispersible tablets/TIGE injection were 104.5 and 104.9%, respectively. The detection limits for the fluorescence detection of DEF and TIGE were 3.6 and 1.2 nM, respectively. This fluorescence assay is the first application of MOF to the simultaneous detection of DEF and TIGE and has the advantages of rapid sensitivity and high selectivity, providing a new strategy for drug detection.

Grass carp (Ctenopharyngodon idella) Mex3B positively regulates innate immunity by promoting the K63-linked ubiquitination of TLR3.

As a member of Mex3 (muscle excess protein-3) family, Mex3B (Mex-3 RNA binding family member B) is crucial in cell proliferation and migration in mammals. In this study, an ortholog of mammalian Mex3B (denominated CiMex3B, MT276802.1) was cloned and identified in grass carp (Ctenopharyngodon idella). CiMex3B is 1578 bp in length and encodes a polypeptide of 525 amino acids. Consistent with its mammalian counterpart, CiMex3B also contains one C-terminal RING domain and two N-terminal conserved tandem KH domains. CiMex3B up-regulates the expressions of IFN1, ISG15, MX2, as well as the expressions of inflammatory cytokines such as IL6, IL8 and TNFα in response to poly(I:C). A screening test for identifying potential targets indicated that CiMex3B is associated with TLR3 and TRIF. CiMex3B co-localizes with TLR3 in the late endosome, mitochondria and endoplasmic reticulum after poly(I:C) stimulation, whereas they are rarely discovered in the lysosomes. CiMex3B serves as a positive regulator in the phosphorylation of IRF3 and induces IFN1 expression. In addition, two truncation mutants of CiMex3B (1-220 and 221-525) were constructed to better understand the molecular mechanism of CiMex3B-mediated ubiquitination of TLR3. In line with wild-type protein, CiMex3B mutant (1-220) was found mainly in the cytoplasm; however, CiMex3B mutant (221-525) resided in the cytoplasm and the nucleus as well, and it was further confirmed that CiMex3B mutant (221-525) still interacts with TLR3. We also observed that CiMex3B promotes the K63-linked ubiquitination of TLR3, while neither of the truncation mutants (1-220 or 221-525) retains this activity. To sum up, this study revealed that CiMex3B potentiates the K63-linked ubiquitination of TLR3, and then elicits the IRF3-mediated antiviral innate immune responses.

Visualized Lateral Flow Assay for Dual Viral RNA Fragment Detection.

In this technical note, we report an easy-to-produce, reverse-transcription-free, and protein-enzyme-free lateral flow assay for detection of viral RNA fragments by taking SARS-CoV-2 ORF1ab and N as target models. Catalytic hairpin assembly is utilized for dual RNA fragment orthogonal reaction to generate copious amounts of opened hairpin duplexes, which bridge DNA-modified gold nanoparticles and capture strands on the strip to induce coloration. The dual RNA fragments are simultaneously visualized during one time of sample flow, and single-base-mismatched nontarget sequences can be differentiated. The test strip can be flexibly adapted to detect evolutional SARS-CoV-2 variants such as Delta and Omicron. It also shows potential in visually detecting long-sequence virus simulants and achieves a sensitivity comparable to that of RT-qPCR by incorporation with upstream sample amplification. The lateral flow assay should offer a convenient and reliable technique for viral nucleic acid detection.