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  1. 4621

    Expression Levels of Some Antioxidant and Epidermal Growth Factor Receptor Genes in Patients with Early-Stage Non-Small Cell Lung Cancer by Giuseppe De Palma, Paola Mozzoni, Olga Acampa, Eveline Internullo, Paolo Carbognani, Michele Rusca, Matteo Goldoni, Massimo Corradi, Marcello Tiseo, Pietro Apostoli, Antonio Mutti

    Published 2010-01-01
    “…This study was aimed at: (i) investigating the expression profiles of some antioxidant and epidermal growth factor receptor genes in cancerous and unaffected tissues of patients undergoing lung resection for non-small cell lung cancer (NSCLC) (cross-sectional phase), (ii) evaluating if gene expression levels at the time of surgery may be associated to patients' survival (prospective phase). …”
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  2. 4622

    Object-Place Recognition Learning Triggers Rapid Induction of Plasticity-Related Immediate Early Genes and Synaptic Proteins in the Rat Dentate Gyrus by Jonathan Soulé, Zsuzsa Penke, Tambudzai Kanhema, Maria Nordheim Alme, Serge Laroche, Clive R. Bramham

    Published 2008-01-01
    “…Long-term recognition memory requires protein synthesis, but little is known about the coordinate regulation of specific genes. Here, we examined expression of the plasticity-associated immediate early genes (Arc, Zif268, and Narp) in the dentate gyrus following long-term object-place recognition learning in rats. …”
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  3. 4623
  4. 4624

    Validation of Housekeeping Genes to Study Human Gingival Stem Cells and Their In Vitro Osteogenic Differentiation Using Real-Time RT-qPCR by Ihsène Taïhi, Ali Nassif, Tsouria Berbar, Juliane Isaac, Ariane Berdal, Bruno Gogly, Benjamin Philippe Fournier

    Published 2016-01-01
    “…RT-qPCR is a widely used technique to study gene expression and may help us to follow osteoblast differentiation of GSCs. …”
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  8. 4628

    Deciphering the Immune Subtypes and Signature Genes: A Novel Approach Towards Diagnosing and Prognosticating Severe Asthma Through Interpretable Machine Learning by Yue Hu, Yating Lin, Bo Peng, Chunyan Xiang, Wei Tang

    Published 2024-01-01
    “…We employ single-sample gene set enrichment analysis (ssGSEA) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms to identify differentially expressed immune cells and utilize machine learning techniques, including Extreme Gradient Boosting (XGBoost) and random forest, to predict severe asthma outcomes and identify key genes associated with immune cells. …”
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