Distinct Immune Escape Mechanisms of Bovine Cronavirus Nucleocapsid by Suppressing beta Interferon Production via Retinoic Acid-inducible Gene I-like Receptor Pathway (preprint)

The present study aimed to explore if bovine coronavirus nucleocapsid (BCoV N) impacts beta interferon (IFN-β) production in the host cells and to reveal further molecular mechanism of BCoV pathopoiesis. Human embryonic kidney (HEK) 293T cells were transientlly transfected with pCMV-Myc-BCoV-N recombinant plasmids, then infected with the vesicular stomatitis virus (VSV). Expression levels of IFN-β mRNA were detected using qPCR. The results determinated that pCMV-Myc-BCoV-N recombinant plasmids of 1347bp was successfully constructed and transcribed into HEK 293T cells. Western-blotting assay indicated that BCoV-N recombinant plasmids had excellent antigenicity. BCoV-N recombinant proteins inhibited dose-dependently IFN-β production mediated by Vesicular stomatitis virus (VSV) (P<0.01). Furthermore, MDA5, MAVS, TBK1 and IRF3 could promote transcription levels of IFN-β mRNA. But, BCoV-N proteins demoted IFN-β levels induced by MDA5, MAVS, TBK1 and IRF3. Expression levels of MDA5, MAVS, TBK1 and IRF3 mRNAs were reduced in retinoic acid-inducible gene I-like receptor (RLR) pathway. In conclusion, BCoV-N reduced IFN-β levels in RLR pathway of HEK 293T cells. BCoV-N protein inhibited IFN-β production and activation of RLRs signal pathway. Our findings demonstrated a new mechanism evolved by BCoV to inhibit type I IFN production and provided a solid scientific basis for revealing the pathogenesis of BCoV, which is beneficial for developing novel strategy of the diagnose and therapy of BCoV disease.

Predictive value of heparin-binding protein, interleukin-6, and C-reactive protein in severe COVID-19, organ failure, and prognosis: a retrospective study (preprint)

Background Systemic inflammation is closely related to the progress of COVID-19.This study aimed to explore the role of combined detection of heparin-binding protein (HBP), interleukin-6 (IL6), and C-reactive protein (CRP) on the severity and clinical outcomes of COVID-19. Methods Our hospital conducted a retrospective analysis of 214 patients with COVID-19 from 1 December 2022 to 28 February 2023. Patients were separated into non-severe and severe categories. Based on whether there was organ failure during hospitalization, patients were further split into the non-organ failure group and the organ failure group. Records on demographics, baseline, and clinical features, as well as the levels of HBP, IL6, and CRP on admission, were collected. Results HBP, IL6, and CRP levels were positively correlated with total bilirubin, lactate dehydrogenase, serum creatinine, and D-dimer but negatively correlated with albumin. HBP, IL6, and CRP levels were remarkably higher in severe, organ failure, and non-survivor groups compared to non-severe, non-organ failure, and survivor groups (all P < 0.001). The optimal cutoff values of HBP, IL6, and CRP for predicting severe COVID-19 were 49.71 ng/mL, 11.24 pg/mL, and 39.67 mg/L, respectively. With a sensitivity and specificity of 85.10% and 95.70% for severe COVID-19, the combined detection of HBP, IL6, and CRP showed the best diagnostic effectiveness. Logistic regression revealed that HBP, IL6, and CRP were independent risk factors for severe COVID-19 and organ failure. Moreover, the risk of death predicted by any two or more of HBP, IL6, and CRP higher than the optimal cutoff value was 3.631 times that of only one of the three indicators higher than the optimal cutoff value (hazard ratio = 3.631, log-rank P = 0.003). Conclusions A combination of HBP, IL6, and CRP has higher diagnostic efficiency of severe COVID-19; combined detection can more accurately and efficiently predict COVID-19 severity, organ failure, and prognosis, which is complementary to previous studies.

CGRNet: Contour-guided graph reasoning network for ambiguous biomedical image segmentation

In this work, we propose to address the existing problem of biomedical image segmentation that often produces results, which fail to capture the exact contours of the target and suffer from ambiguity. Most previous techniques are suboptimal because they often simply concatenate contour information to alleviate this problem, while ignoring the correlation between regions and contours. As a matter of fact, the relationship between cross-domain features is an important clue for ambiguous pixel segmentation in biomedical images. To this end, we contribute a simple yet effective framework called Contour-Guided Graph Reasoning Network (CGRNet) for more accurate segmentation against ambiguity, which is capable of capturing the semantic relations between object regions and contours through graph reasoning. Specifically, we first perform a global graph representation of the low-level and high-level features extracted by the feature extractor, where clusters of pixels with similar features are mapped to each vertex. Further, we explicitly combine contour information as the geometric prior, which can aggregate features of contour pixels to graph vertices and focus on features along the boundaries. Then, the cross-domain features propagate information through the vertices on the graph to efficiently learn and reason about the semantic relations. Finally, the learned refinement graph features are projected back to the original pixel coordinate space for the final pixel-wise segmentation task. Extensive experiments on the three publicly available Kvasir, CVC-612, and COVID19-100 datasets show the effectiveness of our CGRNet with superior performance to existing state-of-the-art methods. Our code is publicly available at: https://github.com/DLWK/CGRNet.