Screening of molecular markers associated with hornless traits in Qira black sheep by Wen Zhou, Lijun Zhu, Yuwei Peng, Xiaopeng Li, Xuechen Zhang, Zhipeng Han, Jingping Li, Xinyu Bai, Qifeng Gao, Ruizhi Yang, Tao Jiang, Shudong Liu

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Expression of long non-coding RNA in patients with non-IgA mesangial proliferative glomerulonephritis by CONG Shan, SUI Wei-guo, ZOU Gui-mian, XUE Wen, LI Huan, YAN Qiang, CHEN Jie-jing, LUO Ya-dan, CHEN Huai-zhou

“…Objective To study differential expression profile of mRNA and long non-coding RNA(IncRNA) through microarray analysis between non-IgA mesangial proliferative glomerulonephritis(MsPGN) patients and the controls,and then explore the potential role of IncRNA in the pathogenesis of non-IgA MsPGN.Methods Through simple random sampling,4 patients with non-IgA MsPGN and 2 controls were selected as disease group and control group,respectively.Renal cortical tissues from two groups were collected.Total RNA was extracted,quantified and prepared to ds-cDNA through reverse transcription ds-cDNA was labeled with NimbleGen one-color DNA labeling kit and used for array hybridization.All experimental data were processed through GO analysis,Pathway analysis and the gene loci correlation analysis of mRNA and IncRNA.Some IncRNAs that were closely related to non-IgA MsPGN were screened out.Finally,part of the array results was detected by PCR to verify the reliability of array test Results By fold change filtering,4317 differentially expressed mRNAs and 3502 differentially expressed IncRNAs were screened out.Five IncRNAs were found to play potential roles in the pathogenesis of non-IgA MsPGN:AF1180924(close to coding gene FGG),AK092233(close to coding gene COL18A1),AK130579(close to coding gene CREBBP),AK023598(close to coding gene LEPR),and AK055915(close to coding gene CDC42EP3).These results provided an important basis for revealing the pathogenesis of non-IgA MsPGN.Conclusions Some IncRNAs can potentially regulate related genes and plays an important role in the pathogenesis and development of non-IgA MsPGN.…”
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A practical distribution pattern of α-SMA-positive carcinoma associated fibroblasts indicates poor prognosis of patients with pancreatic ductal adenocarcinoma by Bo Li, Meilong Shi, Yang Wang, Penghao Li, Xiaoyi Yin, Guoxiao Zhang, Xiaochao Kang, Huan Wang, Suizhi Gao, Kailian Zheng, Xiaohan Shi, Xiongfei Xu, Yukun Zhou, Hui Jiang, Wei Jing, Shiwei Guo, Gang Jin

“…We utilized a tissue microarray to assess the spatial intensity of α-SMA expression within the tumor microenvironment. …”
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Possible cross‑talk between atrial fibrillation and obstructive sleep apnea revealed by transcriptomic analysis by Xinmiao Chen, Yi Li, Yao Liu

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Exposing Optimal Feature Sets for Enhancing Machine Learning Performance by Hiba Mohammed Al-Marwai, Ghaleb H. Al-Gaphari, Mohammed Mohammed Zayed

“…The majority of high dimensional gene expression data contain a significant amount of redundant genes, posing challenges for machine learning algorithms due to their high dimensionality. Feature selection has shown to be a successful method for improving classification algorithms performance by addressing two primary objectives: reducing the number of features and improving classification accuracy. …”
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Successful strategy of comprehensive pre-implantation genetic testing for Duchenne muscular dystrophy and chromosome balance using karyomapping and fluorescent PCR by Suchada Mongkolchaipak, Sirivipa Piyamongkol, Chutithep Teekaput, Rungthiwa Sirapat, Wanwisa Suriya, Tawiwan Pantasri, Theera Tongsong, Wirawit Piyamongkol

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A multi-omic meta-analysis reveals novel mechanisms of insecticide resistance in malaria vectors by Sanjay C. Nagi, Victoria A. Ingham

“…This study, employing a cross-species approach, integrates RNA-Sequencing, whole-genome sequencing, and microarray data to elucidate drivers of insecticide resistance in Anopheles gambiae complex and An. funestus. …”
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Analysis of Gene Expression in Human Dermal Fibroblasts Treated with Senescence-Modulating COX Inhibitors by Jeong A. Han, Jong-Il Kim

“…In contrast, celecoxib, another COX-2–selective inhibitor, and aspirin, a non-selective COX inhibitor, accelerated the senescence and aging. …”
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Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data. by Enrico Glaab, Jaume Bacardit, Jonathan M Garibaldi, Natalio Krasnogor

“…A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. …”
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The impact of the nucleosome code on protein-coding sequence evolution in yeast. by Tobias Warnecke, Nizar N Batada, Laurence D Hurst

“…We identify nucleosome positioning as a likely candidate to set up such a DNA-level selective regime and use high-resolution microarray data in yeast to compare the evolution of coding sequence bound to or free from nucleosomes. …”
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Evaluation of gene expression classification studies: factors associated with classification performance. by Putri W Novianti, Kit C B Roes, Marinus J C Eijkemans

“…The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. …”
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Bioinformatics meets machine learning: identifying circulating biomarkers for vitiligo across blood and tissues by Qiyu Wang, Jingwei Yuan, Jingwei Yuan, Mengdi Zhang, Haiyan Jia, Hongjie Lu, Yan Wu

“…The exact aetiology and pathogenesis of vitiligo remain incompletely understood.MethodsFirst, a microarray dataset of blood samples from multiple patients with vitiligo was collected from GEO database.The limma package was used to analyze the microarray data and identify significant differentially expressed genes (DEGs). …”
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Nanomaterials based biosensors applied for detection of aflatoxin B1 in cereals: a review by Loyce Namanya, Emma Panzi Mukhokosi, Ediriisa Mugampoza

“…However, more studies are needed to address the automatic simultaneous detection of various aflatoxins in real samples and a biosensing system that integrates with microarray technology.…”
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Two stable variants of Burkholderia pseudomallei strain MSHR5848 express broadly divergent in vitro phenotypes associated with their virulence differences. by A A Shea, R C Bernhards, C K Cote, C J Chase, J W Koehler, C P Klimko, J T Ladner, D A Rozak, M J Wolcott, D P Fetterer, S J Kern, G I Koroleva, S P Lovett, G F Palacios, R G Toothman, J A Bozue, P L Worsham, S L Welkos

“…Microscopic and colony morphology differences on six differential media were observed and only the Rough variant metabolized sugars in selective agar. Antimicrobial susceptibilities and lipopolysaccharide (LPS) features were characterized and phenotype microarray profiles revealed distinct metabolic and susceptibility disparities between the variants. …”
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Characterization of transcriptional changes in ERG rearrangement-positive prostate cancer identifies the regulation of metabolic sensors such as neuropeptide Y. by Petra Massoner, Karl G Kugler, Karin Unterberger, Ruprecht Kuner, Laurin A J Mueller, Maria Fälth, Georg Schäfer, Christof Seifarth, Simone Ecker, Irmgard Verdorfer, Armin Graber, Holger Sültmann, Helmut Klocker

“…Functional analyses do not fully explain the selective pressure causing ERG rearrangement during the development of prostate cancer. …”
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Host genome drives the microbiota enrichment of beneficial microbes in shrimp: exploring the hologenome perspective by Fernanda Cornejo-Granados, Luigui Gallardo-Becerra, Sandra Romero-Hidalgo, Alonso A. Lopez-Zavala, Andrés Cota-Huízar, Melany Cervantes-Echeverría, Rogerio R. Sotelo-Mundo, Adrian Ochoa-Leyva

“…Results Using genome-wide SNP microarray analysis, we confirmed that Gen1 and Gen2 represented distinct genetic populations. …”
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Pharmacologic inhibition of CSF-1R suppresses intrinsic tumor cell growth in osteosarcoma with CSF-1R overexpression by Cheng Dai, Bin Shen, Shenyan Liu, Cong Li, Shuqun Yang, Jie Wang, Jie Zhang, Manqi Liu, Zhixuan Zhu, Wan Shi, Qi Zhang, Zhui Chen, Nannan Zhang

“…Immunohistochemistry (IHC) was utilized to analyze human tissue microarray samples of osteosarcoma. We then investigated the anti-tumor effect and the mechanisms of action of pharmacologic inhibition of CSF-1R activity by pimicotinib (ABSK021), a highly potent and selective small molecule inhibitor of CSF-1R, in osteosarcoma models both in vitro and in vivo. …”
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A RNA-Seq Analysis of the Rat Supraoptic Nucleus Transcriptome: Effects of Salt Loading on Gene Expression. by Kory R Johnson, C C T Hindmarch, Yasmmyn D Salinas, YiJun Shi, Michael Greenwood, See Ziau Hoe, David Murphy, Harold Gainer

“…In addition, we compare the SON transcriptomes resolved by RNA-Seq methods with the SON transcriptomes determined by Affymetrix microarray methods in rats under the same osmotic conditions, and find that there are 6,466 genes present in the SON that are represented in both data sets, although 1,040 of the expressed genes were found only in the microarray data, and 2,762 of the expressed genes are selectively found in the RNA-Seq data and not the microarray data. …”
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The transcription factors Snail and Slug activate the transforming growth factor-beta signaling pathway in breast cancer. by Archana Dhasarathy, Dhiral Phadke, Deepak Mav, Ruchir R Shah, Paul A Wade

“…In order to obtain a global view of the impact of Snail and Slug expression, we performed a microarray experiment using the MCF-7 breast cancer cell line, which does not express detectable levels of Snail or Slug. …”
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Prevention of hypovolemic circulatory collapse by IL-6 activated Stat3. by Jeffrey A Alten, Ana Moran, Anna I Tsimelzon, Mary-Ann A Mastrangelo, Susan G Hilsenbeck, Valeria Poli, David J Tweardy

“…Pre-treatment of rats with a selective inhibitor of Stat3, T40214, reduced the IL-6-mediated increase in cardiac Stat3 activity, blocked successful resuscitation by IL-6 and reversed IL-6-mediated protection from cardiac apoptosis. …”
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