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OBJECTIVE: Glioma is a central nervous system tumor arising from glial cells. Despite significant advances in diagnosis and treatment, most patients with high-grade gliomas have a poor prognosis. Many studies have shown that long noncoding RNAs (lncRNAs) may play important roles in the development, progression and treatment of many tumors, including gliomas. Molecularly targeted therapy may be a new direction for the adjuvant treatment of glioma. Therefore, we hope that by studying differentially expressed lncRNAs (DElncRNAs) in glioma, we can discover lncRNAs that can serve as biomarkers for glioma and provide better therapeutic modalities for glioma patients. METHODS: First, the expression of lncRNAs in 5 normal brain (NB) tissues and 10 glioma tissues was examined by RNA sequencing (RNA-seq). Next, we performed Kaplan-Meier analysis of data from The Cancer Genome Atlas (TCGA) database to assess the prognostic value of these variables. Finally, functional analysis of the DElncRNAs was performed by means of Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. RESULTS: RNA sequencing analysis revealed 85 upregulated miRNAs and 71 downregulated lncRNAs in low-grade glioma (LGG) and 50 upregulated lncRNAs and 70 downregulated lncRNAs in glioblastoma (GBM). Among them, AL355974.3 was the most upregulated lncRNA. LINC00632 was the most downregulated lncRNA. Second, LGG patients with higher AL355974.3 expression had worse overall survival according to Kaplan-Meier analysis of the TCGA database. Finally, bioinformatics analysis revealed that the target genes of these DElncRNAs were enriched in various biological processes and signaling pathways, such as cell metabolic and developmental processes. CONCLUSION: Our findings provide evidence that AL355974.3 may be a new biomarker for glioma.
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BACKGROUND AND AIMS: Despite the benefits of artificial intelligence in small-bowel (SB) capsule endoscopy (CE) image reading, information on its application in the stomach and SB CE is lacking. METHODS: In this multicenter, retrospective diagnostic study, gastric imaging data were added to the deep learning-based SmartScan (SS), which has been described previously. A total of 1069 magnetically controlled GI CE examinations (comprising 2,672,542 gastric images) were used in the training phase for recognizing gastric pathologies, producing a new artificial intelligence algorithm named SS Plus. A total of 342 fully automated, magnetically controlled CE examinations were included in the validation phase. The performance of both senior and junior endoscopists with both the SS Plus-assisted reading (SSP-AR) and conventional reading (CR) modes was assessed. RESULTS: SS Plus was designed to recognize 5 types of gastric lesions and 17 types of SB lesions. SS Plus reduced the number of CE images required for review to 873.90 (median, 1000; interquartile range [IQR], 814.50-1000) versus 44,322.73 (median, 42,393; IQR, 31,722.75-54,971.25) for CR. Furthermore, with SSP-AR, endoscopists took 9.54 minutes (median, 8.51; IQR, 6.05-13.13) to complete the CE video reading. In the 342 CE videos, SS Plus identified 411 gastric and 422 SB lesions, whereas 400 gastric and 368 intestinal lesions were detected with CR. Moreover, junior endoscopists remarkably improved their CE image reading ability with SSP-AR. CONCLUSIONS: Our study shows that the newly upgraded deep learning-based algorithm SS Plus can detect GI lesions and help improve the diagnostic performance of junior endoscopists in interpreting CE videos.
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Three p-terphenyl metabolites (1-3), three indole-diterpenoids (4-6), an herbicide sesquiterpene (7), a flavonoid (8), and five other small molecules containing nitrogen (9-13) were isolated from the medicinal insect (Periplaneta americana)-derived endophytic Aspergillus taichungensis SMU01. Their chemical structures were elucidated on the basis of spectroscopic data and quantum chemical computational methods. Biological activity of these isolates in the differentiation of mouse CD4+ T cell subsets was evaluated. Importantly, metabolites 2 targeting JAK-STAT signaling pathway could hold potential benefits in maintaining peripheral immune homeostasis and alleviating the progression of autoimmune diseases.
