Preparation of polyclonal antibodies to chicken P62 protein and its application in nephropathogenic infectious bronchitis virus-infected chickens.

p62, also known as SQSTM1, has been shown to be closely related to the coronavirus. However, it remains unclear on the relationship between p62 and NIBV infection. Moreover, there are no available antibodies against the chicken p62 protein. Thus, this study aimed to prepare p62 polyclonal antibody and investigate the correlation between the p62 protein and NIBV infection. Here, PET-32a-p62 prokaryotic fusion expression vector was constructed for prokaryotic protein expression, and then p62 polyclonal antibody was prepared by immunizing rabbits. Lastly, these antibodies were then utilized in Western blotting (WB), immunohistochemistry (IHC), and immunofluorescence (IF) assays. The results showed that we successfully prepared chicken p62 polyclonal antibody. Meanwhile, WB and IF demonstrated that the expression of p62 showed a trend of first increase and then decrease after NIBV infection. IHC showed that the expression of p62 in the spleen, lung, kidney, bursa of Fabricius and trachea of chickens infected with NIBV in 11 dpi was significantly higher than that of normal chickens. Taken together, this study successfully prepared a polyclonal antibody for chicken p62 protein and confirmed its application and expression in chickens, as well as the expression of p62 in tissues after NIBV infection.

Baicalin mitigates nephropathogenic infectious bronchitis virus infection-induced spleen injury via modulation of mitophagy and macrophage polarization in Hy-Line chick.

Nephropathogenic infectious bronchitis virus (NIBV) infections continue to pose a significant hazard in the poultry industry. Baicalin is a natural flavonoid that has been reported to have antiviral activity, but its function in NIBV infection largely remains unclear. In this study, the antiviral mechanism of baicalin in the spleen of NIBV-infected chicks was mainly elucidated in mitophagy and macrophage polarization. 28-day-old Hy-Line brown chicks were randomly divided into four groups: the group of chicks was treated intranasally (in) with normal saline (0.2 mL) and subsequently divided into two groups: the Con group (basic diet), the Con+BA group (basic diet+10 mg/kg Baicalin); another group of chicks was intranasally infected with SX9 (10-5/0.2 mL) and subsequently divided into two groups: the Dis group (basic diet), the Dis+BA group (basic diet+10 mg/kg Baicalin). Spleen tissues were collected at 3, 7, and 11 days post infection (dpi). NIBV copy number was strikingly decreased in the spleens under BA treatment with infectious time. Histopathological examination showed enlarged and hemorrhagic white pulp and no clearly defined boundary between white pulp and red pulp in the Dis group, which could be improved by BA treatment. Meanwhile, the loss of cristae structure and vacuolization in mitochondria caused by NIBV infection was repaired in the Dis+BA group by ultrastructure observation. In addition, BA treatment inhibited the induction of mitophagy by NIBV infection. BA treatment also promoted innate immunity by enhancing type I IFN levels. Moreover, BA treatment up-regulated M1-related cytokines (iNOS, TNF-α, IL-1ß, IL-6) and inhibited M2-related cytokines (ARG2, IL-4, IL-10, Pparg) at the mRNA and protein levels. However, the results from the splenic tissues at 11 dpi are opposite results from 3 and 7 dpi. Immunofluorescence analysis for M1 macrophage marker iNOS and M2 macrophage marker CD163 further validated this result. Collectively, BA inhibited mitophagy and triggered IFN activation, and M1 polarization, which contributed to the inhibition of NIBV infection.

Preparation of polyclonal antibodies to the chicken Beclin1 protein and its application in the detection of nephropathogenic infectious bronchitis virus.

Beclin1, also known as ATG6, has been shown to be closely related to coronavirus, however, the link between Beclin1 and nephropathogenic infectious bronchitis virus (NIBV) has been poorly investigated and there are no available antibodies specifically targeting the chicken Beclin1 protein. The study aimed to prepare and assay a polyclonal antibody to Beclin1, enabling a deeper understanding of the mechanism of action of Beclin1 in NIBV. In this study, we amplified the chicken Beclin1 target gene and constructed a recombinant plasmid using prokaryotic expression techniques, then obtained the recombinant target protein by induced expression. Finally, the serum is obtained by immunizing rabbits with the purified and concentrated protein. The results show that the antiserum potency of the ELISA assay was >1:204800. By western blotting and immunofluorescence, the antibodies we prepared specifically recognized the chicken Beclin1 protein, which is mainly found in the nucleus of trachea, lung, kidney, spleen and fabricant cells. NIBV infection significantly decreased the expression of Beclin1 in the trachea, but increased in others. We have successfully prepared specific rabbit anti-chicken Beclin1 polyclonal antibodies, and detected changes in tissues of diseased chickens infected with NIBV, laying the foundation for further studies on the role of Beclin1 in avian diseases.

PTBGRP: predicting phage-bacteria interactions with graph representation learning on microbial heterogeneous information network.

Identifying the potential bacteriophages (phage) candidate to treat bacterial infections plays an essential role in the research of human pathogens. Computational approaches are recognized as a valid way to predict bacteria and target phages. However, most of the current methods only utilize lower-order biological information without considering the higher-order connectivity patterns, which helps to improve the predictive accuracy. Therefore, we developed a novel microbial heterogeneous interaction network (MHIN)-based model called PTBGRP to predict new phages for bacterial hosts. Specifically, PTBGRP first constructs an MHIN by integrating phage-bacteria interaction (PBI) and six bacteria-bacteria interaction networks with their biological attributes. Then, different representation learning methods are deployed to extract higher-level biological features and lower-level topological features from MHIN. Finally, PTBGRP employs a deep neural network as the classifier to predict unknown PBI pairs based on the fused biological information. Experiment results demonstrated that PTBGRP achieves the best performance on the corresponding ESKAPE pathogens and PBI dataset when compared with state-of-art methods. In addition, case studies of Klebsiella pneumoniae and Staphylococcus aureus further indicate that the consideration of rich heterogeneous information enables PTBGRP to accurately predict PBI from a more comprehensive perspective. The webserver of the PTBGRP predictor is freely available at http://120.77.11.78/PTBGRP/.

Immunology and immunotherapy in gastric cancer.

Gastric cancer is the fifth leading cause of cancer-related deaths worldwide. As the diagnosis of early gastric cancer is difficult, most patients are at a late stage of cancer progression when diagnosed. The current therapeutic approaches based on surgical or endoscopic resection and chemotherapy indeed improve patients' outcomes. Immunotherapy based on immune checkpoint inhibitors has opened a new era for cancer treatment, and the immune system of the host is reshaped to combat tumor cells and the strategy differs according to the patient's immune system. Thus, an in-depth understanding of the roles of various immune cells in the progression of gastric cancer is beneficial to application for immunotherapy and the discovery of new therapeutic targets. This review describes the functions of different immune cells in gastric cancer development, mainly focusing on T cells, B cells, macrophages, natural killer cells, dendritic cells, neutrophils as well as chemokines or cytokines secreted by tumor cells. And this review also discusses the latest advances in immune-related therapeutic approaches such as immune checkpoint inhibitors, CAR-T or vaccine, to reveal potential and promising strategies for gastric cancer treatment.

Automated Classification of Intramedullary Spinal Cord Tumors and Inflammatory Demyelinating Lesions Using Deep Learning.

Accurate differentiation of intramedullary spinal cord tumors and inflammatory demyelinating lesions and their subtypes are warranted because of their overlapping characteristics at MRI but with different treatments and prognosis. The authors aimed to develop a pipeline for spinal cord lesion segmentation and classification using two-dimensional MultiResUNet and DenseNet121 networks based on T2-weighted images. A retrospective cohort of 490 patients (118 patients with astrocytoma, 130 with ependymoma, 101 with multiple sclerosis [MS], and 141 with neuromyelitis optica spectrum disorders [NMOSD]) was used for model development, and a prospective cohort of 157 patients (34 patients with astrocytoma, 45 with ependymoma, 33 with MS, and 45 with NMOSD) was used for model testing. In the test cohort, the model achieved Dice scores of 0.77, 0.80, 0.50, and 0.58 for segmentation of astrocytoma, ependymoma, MS, and NMOSD, respectively, against manual labeling. Accuracies of 96% (area under the receiver operating characteristic curve [AUC], 0.99), 82% (AUC, 0.90), and 79% (AUC, 0.85) were achieved for the classifications of tumor versus demyelinating lesion, astrocytoma versus ependymoma, and MS versus NMOSD, respectively. In a subset of radiologically difficult cases, the classifier showed an accuracy of 79%-95% (AUC, 0.78-0.97). The established deep learning pipeline for segmentation and classification of spinal cord lesions can support an accurate radiologic diagnosis. Supplemental material is available for this article. © RSNA, 2022 Keywords: Spinal Cord MRI, Astrocytoma, Ependymoma, Multiple Sclerosis, Neuromyelitis Optica Spectrum Disorder, Deep Learning.

Hierarchical Self-Assembly of Adhesive and Conductive Gels with Anion-Coordinated Triple Helicate Junctions.

The fabrication of anion-coordinated assemblies into functional soft materials remains a major challenge. To this end, four C2 -symmetric anion-binding ligands equipped with ortho-phenylene-bridged bis(urea) and amine or amide ends were designed, which generated A2 L3 triple helical architectures upon self-assembly with phosphate ions. Hierarchical intermolecular hydrogen bonds among the terminal amine/amide groups and urea moieties resulted in the formation of functional gels. The obtained gels were further applied for conductive adhesion between different surfaces, displaying excellent flexibility and selective wettability. The viscoelastic gels constructed from anion-coordinated assemblies described in this work represent the first example of a new class of anion-coordination-driven smart materials.

Prediction of radiosensitivity in primary central nervous system germ cell tumors using dynamic contrast-enhanced magnetic resonance imaging.

OBJECTIVE: To evaluate the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting tumor response to radiotherapy in patients with suspected primary central nervous system (CNS) germ cell tumors (GCTs). METHODS: DCE-MRI parameters of 35 patients with suspected primary CNS GCTs were obtained prior to diagnostic radiation, using the Tofts and Kermode model. Radiosensitivity was determined in tumors diagnosed 2 weeks after radiation by observing changes in tumor size and markers as a response to MRI. Taking radiosensitivity as the gold standard, the cut-off value of DCE-MRI parameters was measured by receiver operating characteristic (ROC) curve. Diagnostic accuracy of DCE-MRI parameters for predicting radiosensitivity was evaluated by ROC curve. RESULTS: A significant elevation in transfer constant (K(trans)) and extravascular extracellular space (Ve) (P=0.000), as well as a significant reduction in rate constant (Kep) (P=0.000) was observed in tumors. K(trans), relative K(trans), and relative Kep of the responsive group were significantly higher than non-responsive groups. No significant difference was found in Kep, Ve, and relative Ve between the two groups. Relative K(trans) showed the best diagnostic value in predicting radiosensitivity with a sensitivity of 100%, specificity of 91.7%, positive predictive value (PPV) of 95.8%, and negative predictive value (NPV) of 100%. CONCLUSIONS: Relative K(trans) appeared promising in predicting tumor response to radiation therapy (RT). It is implied that DCE-MRI pre-treatment is a requisite step in diagnostic procedures and a novel and reliable approach to guide clinical choice of RT.