Signaling pathway alterations in COVID-19 of living humans as well as therapeutic targets of the host proteins are not clear. We analyzed 317 urine proteomes, including 86 COVID-19, 55 pneumonia and 176 healthy controls, and identified specific RNA virus detector protein DDX58/RIG-I only in COVID-19 samples. Comparison of the COVID-19 urinary proteomes with controls revealed major pathway alterations in immunity, metabolism and protein localization. Biomarkers that may stratify severe symptoms from moderate ones suggested that macrophage induced inflammation and thrombolysis may play a critical role in worsening the disease. Hyper activation of the TCA cycle is evident and a macrophage enriched enzyme CLYBL is up regulated in COVID-19 patients. As CLYBL converts the immune modulatory TCA cycle metabolite itaconate through the citramalyl-CoA intermediate to acetyl-CoA, an increase in CLYBL may lead to the depletion of itaconate, limiting its anti-inflammatory function. These observations suggest that supplementation of itaconate and inhibition of CLYBL are possible therapeutic options for treating COVID-19, opening an avenue of modulating host defense as a means of combating SARS-CoV-2 viruses. Supporting Information The supporting information is available online at 10.1007/s11427-021-2070-y. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.
Ductular reaction (DR) is usually observed in biliary disorders or various liver disorders, including nonalcoholic fatty liver disease. Few studies have focused on interrupting the DR process in the cholestatic environment. Here, we investigated the impact of reversine on DR in rats that had undergone bile duct ligation (BDL). Cholestatic injury was induced in rats 2 weeks following BDL. DR was assessed with biliary markers by immunohistochemistry. Biliary epithelial cells (BECs) were isolated for the analysis of proliferation and biliary factor gene expression. The effects of reversine on DR and fibrosis were analyzed in vivo via intraperitoneal injection in rats for 2 weeks. Chemically‐induced BEC formation was used to investigate the biliary markers affected by reversine in vitro . DR with increased BEC expansion was identified in cholestatic liver injury, as indicated by CK7, CK19, and EpCAM expression around the portal vein in BDL rats. BDL‐induced DR cells showed the increased expression of genes regulating cell proliferation ( Ki67 , Foxm1 , and Pcna ) and biliary markers ( Krt7 , Krt19 , Epcam , Sox9 , Cftr , and Asbt ). Reversine attenuated cholestatic fibrosis and DR in rats. Reversine affected chemically‐induced BEC formation, with the decreased expression of biliary Krt7 , Cftr , and Ggt1 genes in vitro . BDL‐induced Notch activation was attenuated upon reversine treatment in vivo , in part via the Notch/Sox9 pathway. In conclusion, reversine attenuated cholestatic ductular reaction and fibrosis in rats and reduced the bile duct formation associated with Dlk1/Notch/Sox9 signaling. Reversine may be regarded as a potential drug for cholangiopathies for preventing a ductular reaction.
Background: Radix Scutellariae (RS) has been used to treat influenza for thousands of years in China.However, its mechanisms of action remain unclear. The aim of the present study was to use a network pharmacology and molecular docking-based approach to explore active components and potential molecular mechanisms of RS for influenza A.Methods: Target genes of RS and influenza A were attained by accessing network databases. We then determined the intersection of both genes through bioinformatics using R and Perl language. The proteinprotein interaction (PPI) network was constructed by the STRING website (https://cn.string-db.org). The network analysis was done using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were applied for the above genes. Effective components as core targets were screened out based on the condition that the interaction must come first. These core targets were combined with 3D structures of main RNA coding proteins of influenza A virus. Molecular docking was used to visualize drug-target interaction via AutoDock Vina and PyMOL.Results: Twenty-eight active components and 40 target genes were acquired through the regulatory network of active components of RS and the PPI network. Seventy-one bioinformatics expressions were obtained through GO enrichment analysis (P<0.05). A total of 124 signaling pathways were screened by KEGG enrichment analysis (P<0.05). Acacetin, wogonin, baicalein, oroxylin A, and beta-sitosterol, which are rich in RS, are closely related to hemagglutinin (HA), NeurAminidase (NA), nucleoprotein (NP), polymerase basic protein 1 (PB1), polymerase basic protein 2 (PB2), polymerase acidic (PA), matrix protein 1 (M1), matrix protein 2 (M2), and non-structural protein (NS), which are the main RNA coding proteins of influenza A virus. The binding energies of these 8 proteins were less than -5 kJ/mol, indicating that the ligands had strong affinity with receptor proteins.Conclusions: RS is rich in core target compounds, and its mechanism of action is further expressed. It could have a good therapeutic effect for influenza A through multi-compound and multi-target regulation of these specific protein targets, and targets and pathways related to immunity and inflammation.
<b><i>Introduction:</i></b> Alzheimer’s disease (AD) is the most prevalent type of dementia and can cause abnormal cognitive function and progressive loss of essential life skills. Early screening is thus necessary for the prevention and intervention of AD. Speech dysfunction is an early onset symptom of AD patients. Recent studies have demonstrated the promise of automated acoustic assessment using acoustic or linguistic features extracted from speech. However, most previous studies have relied on manual transcription of text to extract linguistic features, which weakens the efficiency of automated assessment. The present study thus investigates the effectiveness of automatic speech recognition (ASR) in building an end-to-end automated speech analysis model for AD detection. <b><i>Methods:</i></b> We implemented three publicly available ASR engines and compared the classification performance using the ADReSS-IS2020 dataset. Besides, the SHapley Additive exPlanations algorithm was then used to identify critical features that contributed most to model performance. <b><i>Results:</i></b> Three automatic transcription tools obtained mean word error rate texts of 32%, 43%, and 40%, respectively. These automated texts achieved similar or even better results than manual texts in model performance for detecting dementia, achieving classification accuracies of 89.58%, 83.33%, and 81.25%, respectively. <b><i>Conclusion:</i></b> Our best model, using ensemble learning, is comparable to the state-of-the-art manual transcription-based methods, suggesting the possibility of an end-to-end medical assistance system for AD detection with ASR engines. Moreover, the critical linguistic features might provide insight into further studies on the mechanism of AD.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.