ZEPENG DONG, CHENYE ZHAO, SHIBO HU, KUI YANG, JUNHUI YU^*, XUEJUN SUN, JIANBAO ZHENG^*

BIOCELL, Vol.47, No.12, pp. 2735-2745, 2023, DOI:10.32604/biocell.2023.043422 - 27 December 2023

Abstract Objectives: Angiogenesis plays a significant role in the occurrence and development of inflammatory bowel disease (IBD). The aim of this study is to explore potential angiogenesis related genes (ARGs) in IBD through bioinformatics analysis and in vivo experiments. Methods: GSE57945, GSE87466, and GSE36807 were obtained from the Gene Expression Omnibus database. GSE57945 was used as the training set, while GSE87466 and GSE36807 were used as the validation set. The key ARGs associated with IBD were identified using the least absolute shrinkage and selection operator (LASSO) and random forest methods. These identified ARGs were then utilized to construct… More >

WEI LIANG^1,2,*, ZONGWEI ZHANG^1,2, KEJU YANG^1,2,3, HONGTU HU^1,2, QIANG LUO^1,2, ANKANG YANG^1,2, LI CHANG⁴, YUANYUAN ZENG⁴

BIOCELL, Vol.47, No.6, pp. 1253-1263, 2023, DOI:10.32604/biocell.2023.027373 - 19 May 2023

Abstract Background: Diabetic nephropathy (DN) is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide. Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN. Kidney biopsy is the gold standard for diagnosing DN; however, its invasive character is its primary limitation. The machine learning approach provides a non-invasive and specific criterion for diagnosing DN, although traditional machine learning algorithms need to be improved to enhance diagnostic performance. Methods: We applied high-throughput RNA sequencing to obtain the genes… More >

WENBIAO CHEN, KEFAN BI, JINGJING JIANG, XUJUN ZHANG, HONGYAN DIAO^*

BIOCELL, Vol.45, No.4, pp. 885-899, 2021, DOI:10.32604/biocell.2021.012938 - 22 April 2021

Abstract The genome characteristics and structural functions of coding proteins correlate with the genetic diversity of the H1N1 virus, which aids in the understanding of its underlying pathogenic mechanism. In this study, analyses of the characteristic of the H1N1 virus infection-related genes, their biological functions, and infection-related reversal drugs were performed. Additionally, we used multi-dimensional bioinformatics analysis to identify the key genes and then used these to construct a diagnostic model for the H1N1 virus infection. There was a total of 169 differently expressed genes in the samples between 21 h before infection and 77 h… More >