Differentiating Branch Duct and Mixed IPMN in Endoscopically Collected Pancreatic Cyst Fluid via Cytokine Analysis by Linda S. Lee, Andrew M. Bellizzi, Peter A. Banks, Nisha I. Sainani, Vivek Kadiyala, Shadeah Suleiman, Darwin L. Conwell, Joao A. Paulo

“…Eighty-six of 89 IMPs were detected in at least one of the 10 samples. Fourteen IMPs were detected only in mixed IPMN, while none were only in BD-IPMN. …”
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Identification of CD36 as a contributor in inflammatory response of rheumatoid arthritis and screening of feasible bioactive drugs targeting it by Dan Xuan, Xiaowan Wang, Dandan Feng, Li Wang, Yonghui Xia

“…Methods RA-related microarray datasets (GSE77298 and GSE206848) and inflammatory genes (IRGs) were downloaded from Gene Expression Omnibus database and GeneCards database, respectively. …”
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MiR-223-3p regulates erythropoiesis by targeting TGFBR3/Smad signaling pathway in hemoglobin H-Constant Spring disease by Lan Lan, Xinyu Wang, Simeng Zhang, Dan Zeng, Limin Mo, Qiao Feng, Jun Li, Chunjiang Zhu

“…The present study analyzed miRNAs differential expression profile between patients with HbH-CS disease and healthy subjects by Arraystar Human small RNA Microarray.Methods The differential expression profiles of miRNAs between HbH-CS patients and healthy individuals were analyzed using Arraystar human small RNA microarrays. …”
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Patterns of Human Respiratory Viruses and Lack of MERS-Coronavirus in Patients with Acute Upper Respiratory Tract Infections in Southwestern Province of Saudi Arabia by Ahmed A. Abdulhaq, Vinod Kumar Basode, Anwar M. Hashem, Ahmed S. Alshrari, Nassrin A. Badroon, Ahmed M. Hassan, Tagreed L. Alsubhi, Yahia Solan, Saleh Ejeeli, Esam I. Azhar

“…MERS-CoV was not detected in any of the samples. Human rhinovirus (hRV) was the most detected pathogen (40.9%) followed by non-MERS-CoV human coronaviruses (19.3%), influenza (Flu) viruses (15.9%), and human respiratory syncytial virus (hRSV) (13.6%). …”
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Identification and validation of diagnostic genes IFI44 and IRF9 in insomnia-associated autoimmune uveitis by Chao Wu, Hui Feng, Meng Tian, Baorui Chu, Xianyang Liu, Shuhao Zeng, Yakun Wang, Hong Wang, Shengping Hou, Qingfeng Liang

“…We investigated insomnia-associated genes that may contribute to AU pathogenesis and sought to identify potential biomarkers for insomnia-associated AU.MethodsMicroarray data related to insomnia and AU were downloaded from the Gene Expression Omnibus (GEO) database and analyzed. …”
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Genome-wide analysis of long non-coding RNA expression and function in colorectal cancer by Fangyuan Jing, Huicheng Jin, Yingying Mao, Yingjun Li, Ye Ding, Chunhong Fan, Kun Chen

“…Long non-coding RNA microarray was applied to investigate the global long non-coding RNA expression profile in colorectal cancer. …”
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ARHGAP42 promotes cell migration and invasion involving PI3K/Akt signaling pathway in nasopharyngeal carcinoma by Qian Hu, Xiao Lin, Linxiaoxiao Ding, Yinduo Zeng, Danmei Pang, Nengtai Ouyang, Yanqun Xiang, Herui Yao

“…Western blot and immunohistochemical staining were used for detecting the expression of ARHGAP42 protein in nasopharyngeal carcinoma tissues and cell lines. …”
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Integrated analysis of hub genes and intrinsically disordered regions in triple-negative breast cancer by Azhar Iqbal, Faisal Ali, Sulaiman Ali Alharbi, Muhammad Sajid, Saleh Alfarraj, Momina Hussain, Tehmina Siddique, Rakhshanda Mustaq, Fakhra Shafique, Muhammad Sarfaraz Iqbal

“…Despite several attempts, the molecular pathways of TNBC remain unknown, posing a significant clinical barrier in the search for viable targets. Two microarray datasets were used to identify possible targets for TNBC, GSE38959 and GSE45827, retrieved from the Gene Expression Omnibus (GEO) database. …”
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Early diagnosis of acute myocardial infarction via hub genes identified by integrated weighted gene co-expression network analysis by Kun Huang, Feng Wen, Jingyi Li, Wenhao Niu, Hui Chen, Shilei Wan, Fupeng Yang, Yihong Chen, Chun Liang

“…Diagnostic biomarkers were selected and identified using three machine learning algorithms. …”
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KRT6A, KRT6B, PKP1, and PKP3 as key hub genes in esophageal cancer: A combined bioinformatics and experimental study by Shayan Marhamati, Morvarid Hamrahjoo, Zeinab Seyedkhan, Mahdi Bahmani, Nasrin Ziamajidi, Iraj Khodadadi, Mohadese Pouryani, Roghayeh Abbasalipourkabir

“…In this study, we downloaded the microarray datasets GSE157808 and GSE92396 from the Gene Expression Omnibus (GEO). …”
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Expression and prognostic significance of MLH1 and GPRC5C in resectable hepatocellular carcinoma by Jun Lu, Lei Li, Qing Chen, Bingqi Li, Siqian Ren, Meng Yuan, Meng Meng, Jian Lei, Yuntao Bing, Hangyan Wang, Zhaolai Ma, Dianrong Xiu, Chunhui Yuan

“…However, their expression and prognostic significance in hepatocellular carcinoma (HCC) has not been elucidated, especially for GPRC5C. Methods We detected MLH1 and GPRC5C expression using the tissue microarray-based immunohistochemical staining in tumor and matched adjacent non-tumor liver (ANL) specimens from 230 patients who underwent radical resection for HCC. …”
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Overexpression of cyclin‐dependent kinase 1 in esophageal squamous cell carcinoma and its clinical significance by Han‐Jie Zhang, Gang Chen, Shang‐Wei Chen, Zong‐Wang Fu, Hua‐Fu Zhou, Zhen‐Bo Feng, Jun‐Xian Mo, Chang‐Bo Li, Jun Liu

“…Microarray and external RNA‐seq data from 664 cases of ESCC and 1733 cases of control tissues were used to verify the difference in CDK1 expression between the two groups. …”
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Maternal plasma cell-free DNA nucleosome footprints can reveal changes in gene expression profiles during pregnancy and pre-eclampsia by Kun Li, Zhiwei Guo, Fenxia Li, Shijing Lu, Min Zhang, Xingyu Wei, Chao Sheng, Wenbo Hao, Xuexi Yang

“…These results are consistent with prior microarray and RNA-sequencing data. Conclusions The depth of the cfDNA nucleosome footprint in maternal plasma can be used to reflect changes in the gene expression profile during pregnancy and pre-eclampsia. …”
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Diverse regulated cell death patterns and immune traits in kidney allograft with fibrosis: a prediction of renal allograft failure based on machine learning, single-nucleus RNA seq... by Yuqing Li, Jiandong Zhang, Xuemeng Qiu, Yifei Zhang, Jiyue Wu, Qing Bi, Zejia Sun, Wei Wang

“…A 9-gene signature, termed RCD risk score (RCDI), was constructed using the least absolute shrinkage and selection operator and multivariate Cox regression algorithms. …”
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The potential crosstalk genes and molecular mechanisms between systemic lupus erythematosus and periodontitis by Kai Zhao, Xiaolong Li, Qingmiao Zhu, Mengyu Zhu, Jinge Huang, Ting Zhao

“…Random forest (RF) and Least Absolute Shrinkage and Selection Operator (Lasso) regression were employed to identify key hub genes. …”
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Genome-wide analysis in the search for candidate genes associated with meat productivity traits in meat-and-dairy goats by M. I. Selionova, A.-M. M. Aibazov, A. A. Sermyagin, A. A. Belous, N. A. Zinovieva

“…GWAS analysis using Goat 50K BeadChip high-density DNA microarray determined a genome-wide level of significance for six SNPs located on chromosomes 5, 6, 10 and 16 associated with the live weight of young animals (4 and 8 months old). …”
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Spatial transcriptomic characteristics of gastric cancer in young and the expression and role of TMEM176B in gastric cancer cells by Zhenhua Jiang, Yongfeng Wang, Chi Zhang, Shuangyin Han

“…Methods The surgical specimens of six patients with GCY were included to construct a tissue microarray containing the tumor core region (TCR), cancer-adjacent tissue (CAT), and normal gastric tissue (NGT). …”
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Screening of endoplasmic reticulum stress characteristic genes and immune infiltration manifestations in chronic obstructive pulmonary disease by ZHANG Shuang, LUO Chenyang, HE Zhiyi

“…At the same time, co-expressed mRNA and miRNA were selected to construct the mRNA-miRNA interaction network. …”
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Comprehensive bioinformatics analysis identifies hub genes associated with immune cell infiltration in early-onset schizophrenia by Shasha Wu, Tailian Xue, Yilin Li, Weikang Chen, Yan Ren

“…Methods In this study, we recruited 26 EOS patients and 27 healthy controls (HCs), and microarray data were collected. Crossover genes were identified using weighted gene co-expression network analysis (WGCNA) and differential expression genes (DEGs) analysis. …”
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Noninvasive prediction of fetal growth restriction using maternal plasma cell-free RNA: a case-control study by Yihong Huang, Ruizhi Wang, Lixia Shen, Lingyi Kong, Peisong Chen, Zilian Wang, Zhuyu Li

“…The least absolute shrinkage and selection operator regression was used to select the hub genes from the cell-free RNA DEGs. …”
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