Lung adenocarcinoma (LUAD) is a common malignant tumor with a poor prognosis. Recent studies have found that angiopoietin-like 4 (ANGPTL4) is abnormally expressed in many tumors, so it can serve as a potential prognostic marker and therapeutic target. However, its prognostic value in LUAD remains unclear. We downloaded RNA sequence data for LUAD from The Cancer Genome Atlas (TCGA) database, methylation data from the University of California Santa Cruz genome database, and clinical information. R software (version 4.1.1) was applied to analyze the ANGPTL4 expression in LUAD and nontumor samples, and the correlation with clinical characteristics to assess its prognostic and diagnostic value. In addition, we analyzed the relationship between the ANGPTL4 expression and methylation levels. Tumor IMmune Estimation Resource (TIMER) tool was taken for immune infiltration analysis, and two Gene Expression Omnibus (GEO) datasets were combined for meta-analysis. Finally, differentially expressed genes (DEGs) related to ANGPTL4 were analyzed to clarify its function. As shown in our results, ANGPTL4 was upregulated in LUAD and was an independent risk factor for the diagnosis and prognosis of LUAD. The general methylation level and eight ANGPTL4 methylation sites were significantly negatively correlated with the ANGPTL4 expression. Furthermore, we found that B cell infiltration was negatively correlated with ANGPTL4 expression and was an independent risk factor. Meta-analysis showed that the high expression of ANGPTL4 was closely associated with a poor prognosis. 153 DEGs, including the matrix metalloproteinase family, the chemokines subfamily, and the collagen family, were correlated with ANGPTL4. In this study, we found that ANGPTL4 was significantly elevated in LUAD and was closely associated with the development and poor prognosis of LUAD, suggesting that ANGPTL4 may be a prognostic biomarker and a potential therapeutic target for LUAD.
Xinguan No. 3 has been recommended for the treatment of coronavirus disease 2019 (COVID-19); however, its potential mechanisms are unclear. This study aims to explore the mechanisms of Xinguan No. 3 against COVID-19 through network pharmacology and molecular docking. We first searched the ingredients of Xinguan No. 3 in three databases (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, Traditional Chinese Medicines Integrated Database, and The Encyclopedia of Traditional Chinese Medicine). The active components and their potential targets were predicted through the SwissTargetPrediction website. The targets of COVID-19 can be found on the GeneCards website. Protein interaction analysis, screening of key targets, functional enrichment of key target genes, and signaling pathway analysis were performed through Search Tool for the Retrieval of Interacting Genes databases, Metascape databases, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases. Finally, the affinity of the key active components with the core targets was verified by molecular docking. The results showed that five core targets had been screened, including MAPK1, NF-κB1, RELA, AKT1, and MAPK14. Gene ontology enrichment analysis revealed that the key targets were associated with inflammatory responses and responses to external stimuli. KEGG enrichment analysis indicated that the main pathways were influenza A, hepatitis B, Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, and TNF signaling pathway. Therefore, Xinguan No. 3 might play a role in treating COVID-19 through anti-inflammatory, immune responses, and regulatory responses to external stimuli.
Background: Interest in the application of deep learning (DL) in critical care medicine (CCM) is growing rapidly. However, comprehensive bibliometric research that analyze and measure the global literature is still lacking. Objective: The present study aimed to systematically evaluate the research hotspots and trends of DL in CCM worldwide based on the output of publications, cooperative relationships of research, citations, and the co-occurrence of keywords. Methods: A total of 1708 articles in all were obtained from Web of Science. Bibliometric analysis was performed by Bibliometrix package in R software (4.2.2), Microsoft Excel 2019, VOSviewer (1.6.18), and CiteSpace (5.8.R3).
Results:The annual publications increased steeply in the past five years, accounting for 95.67% (1634/1708) of all the included literature. China and USA contributed to approximately 71.66% (1244/1708) of all publications. Seven of the top ten most productive organizations rank in the top 100 universities globally. Hot spots in research on the application of DL in CCM have focused on classifying disease phenotypes, predicting early signs of clinical deterioration, and forecasting disease progression, prognosis, and death. Convolutional neural networks, long and short-term memory networks, recurrent neural networks, transformer models, and attention mechanisms were all commonly used DL technologies.
Conclusion:Hot spots in research on the application of DL in CCM have focused on classifying disease phenotypes, predicting early signs of clinical deterioration, and forecasting disease progression, prognosis, and death. Extensive collaborative research to improve the maturity and robustness of the model remains necessary to make DL-based model applications sufficiently compelling for conventional CCM practice.
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