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global pattern recognition software lonza gpr 2.0  (Lonza)


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    Lonza global pattern recognition software lonza gpr 2.0
    Global Pattern Recognition Software Lonza Gpr 2.0, supplied by Lonza, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/global+pattern+recognition+software/global+pattern+recognition+analytical+software/pmc03325141-110-7-11
    Average 90 stars, based on 1 article reviews
    global pattern recognition software lonza gpr 2.0 - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Innate immune response and programmed cell death following carrier-mediated delivery of unmodified mRNA to respiratory cells.
    Article Snippet: a Laboratory of Gene Therapy, Department of Nutrition, Genetics and Ethology, Faculty of Veterinary Medicine, Ghent University, Heidestraat 19, B-9820 Merelbeke, Belgium b Laboratory of General Biochemistry and Physical Pharmacy, Ghent Research Group on Nanomedicine, Faculty of Pharmaceutical Sciences, Ghent University, Harelbekestraat 72, B-9000 Ghent, Belgium c Laboratory for Animal Genetics, Department of Nutrition, Genetics and Ethology, Faculty of Veterinary Medicine, Ghent University, Heidestraat 19, B-9820 Merelbeke, Belgium

    Article Title: Therapeutic Evaluation of Ex Vivo-Generated Versus Natural Regulatory T-cells in a Mouse Model of Chronic Gut Inflammation
    Article Snippet: We also quantified surface expression of the a4 integrin (CD49d) on iTregs and nTregs to ascertain whether the absence of Real-time PCR was performed using the Treg-targeted 96-gene qPCR StellARray (Lonza), where primer-coated 96-well plates were incubated with the cDNA in 3 biological replicates and analyzed with the Global Pattern Recognition (GPR) analysis tool v.2.0 (provided by Lonza). www.ibdjournal.org | 2289 Copyright © 2013 Crohn’s & Colitis Foundation of America, Inc.

    Article Title: β 2 -adrenergic agonists modulate TNF-α induced astrocytic inflammatory gene expression and brain inflammatory cell populations
    Article Snippet: The qPCR-array data were analyzed using Global Pattern RecognitionTM software (GPR) (Lonza, Basel, Switzerland).

    Article Title: Therapeutic Evaluation of Ex Vivo-Generated Versus Natural Regulatory T-cells in a Mouse Model of Chronic Gut Inflammation
    Article Snippet: Real-time PCR data from iTregs and nTregs was analyzed with the Global Pattern Recognition analysis tool v.2.0 (provided by Lonza).

    Article Title: Feeder & basic fibroblast growth factor-free culture of human embryonic stem cells: Role of conditioned medium from immortalized human feeders
    Article Snippet: Array data analysis and statistical significance were calculated using the Global Pattern RecognitionTM (GPR) software (Lonza, USA).

    Gene Expression:

    Article Title: EphA2 Activation Promotes the Endothelial Cell Inflammatory Response: a potential role in atherosclerosis
    Article Snippet: B , Co-localization of EphA2 (green) and ephrinA1 (red) in the carotid ... EphrinA1-induced human aortic endothelial cell gene expression To determine how EphA2 signaling affects endothelial cell gene expression, HAECs were stimulated with recombinant ephrinA1 for 3 hours and changes in mRNA expression were analyzed using a commercial qRT-PCR array for genes involved in endothelial cell physiology (Endothelial Cell Biology array; Lonza StellARray®). .. Changes in gene expression were analyzed by Global Pattern Recognition software (Lonza GPR 2.0) identifying 8 genes significantly regulated in response to ephrinA1 ( , p < 0.05 highlighted in yellow). ..

    Software:

    Article Title: EphA2 Activation Promotes the Endothelial Cell Inflammatory Response: a potential role in atherosclerosis
    Article Snippet: B , Co-localization of EphA2 (green) and ephrinA1 (red) in the carotid ... EphrinA1-induced human aortic endothelial cell gene expression To determine how EphA2 signaling affects endothelial cell gene expression, HAECs were stimulated with recombinant ephrinA1 for 3 hours and changes in mRNA expression were analyzed using a commercial qRT-PCR array for genes involved in endothelial cell physiology (Endothelial Cell Biology array; Lonza StellARray®). .. Changes in gene expression were analyzed by Global Pattern Recognition software (Lonza GPR 2.0) identifying 8 genes significantly regulated in response to ephrinA1 ( , p < 0.05 highlighted in yellow). ..



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    ( a – d ) CD11c dnR and wild type mice were injected ( i.p. ) twice a day with BrdU for 3 days. On day 3, mice were sacrificed and frequency of cycling cells was determined in the bone marrow. ( a – b ) FACS plots show the distribution of BrdU staining among total mNK cells ( a ) and gated mNK cells at stages D, E, and F ( b ). ( c ) Graph shows the frequency of cycling cells in pNK, iNK, and mNK cells from CD11c dnR (black circle) versus wild type (white circle) mice. ( d ) Graph shows the frequency of cycling cells in mNK cells at stages D, E, and F from CD11c dnR (black circle) versus wild type (white circle) mice. Data in a , d are representative of three independent experiments with n = 2 mice per experiment and results in c , d show all 6 individual mice. ( e – f ) mNK cells at stages D, E, and F were sorted from the bone marrow. mRNA was isolated and cDNA was subjected to pathway-specific qPCR for analysis of cell cycle genes ( e ) or SYBR Green qPCR for analysis of transcription factors T-bet, GATA-3, and IRF-2 ( f ). Data were analyzed using <t>Global</t> <t>Pattern</t> <t>Recognition</t> <t>analytical</t> <t>software</t> ( e ) or 2-33C3 method ( f ) and results were expressed as fold of change in CD11c dnR versus wild type samples. Data in e , f are representative of three independent cell sorting with samples pooled from n = 12 CD11c dnR and 25 WT mice.
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    ( a – d ) CD11c dnR and wild type mice were injected ( i.p. ) twice a day with BrdU for 3 days. On day 3, mice were sacrificed and frequency of cycling cells was determined in the bone marrow. ( a – b ) FACS plots show the distribution of BrdU staining among total mNK cells ( a ) and gated mNK cells at stages D, E, and F ( b ). ( c ) Graph shows the frequency of cycling cells in pNK, iNK, and mNK cells from CD11c dnR (black circle) versus wild type (white circle) mice. ( d ) Graph shows the frequency of cycling cells in mNK cells at stages D, E, and F from CD11c dnR (black circle) versus wild type (white circle) mice. Data in a , d are representative of three independent experiments with n = 2 mice per experiment and results in c , d show all 6 individual mice. ( e – f ) mNK cells at stages D, E, and F were sorted from the bone marrow. mRNA was isolated and cDNA was subjected to pathway-specific qPCR for analysis of cell cycle genes ( e ) or SYBR Green qPCR for analysis of transcription factors T-bet, GATA-3, and IRF-2 ( f ). Data were analyzed using <t>Global</t> <t>Pattern</t> <t>Recognition</t> <t>analytical</t> <t>software</t> ( e ) or 2-33C3 method ( f ) and results were expressed as fold of change in CD11c dnR versus wild type samples. Data in e , f are representative of three independent cell sorting with samples pooled from n = 12 CD11c dnR and 25 WT mice.
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    ( a – d ) CD11c dnR and wild type mice were injected ( i.p. ) twice a day with BrdU for 3 days. On day 3, mice were sacrificed and frequency of cycling cells was determined in the bone marrow. ( a – b ) FACS plots show the distribution of BrdU staining among total mNK cells ( a ) and gated mNK cells at stages D, E, and F ( b ). ( c ) Graph shows the frequency of cycling cells in pNK, iNK, and mNK cells from CD11c dnR (black circle) versus wild type (white circle) mice. ( d ) Graph shows the frequency of cycling cells in mNK cells at stages D, E, and F from CD11c dnR (black circle) versus wild type (white circle) mice. Data in a , d are representative of three independent experiments with n = 2 mice per experiment and results in c , d show all 6 individual mice. ( e – f ) mNK cells at stages D, E, and F were sorted from the bone marrow. mRNA was isolated and cDNA was subjected to pathway-specific qPCR for analysis of cell cycle genes ( e ) or SYBR Green qPCR for analysis of transcription factors T-bet, GATA-3, and IRF-2 ( f ). Data were analyzed using Global Pattern Recognition analytical software ( e ) or 2-33C3 method ( f ) and results were expressed as fold of change in CD11c dnR versus wild type samples. Data in e , f are representative of three independent cell sorting with samples pooled from n = 12 CD11c dnR and 25 WT mice.

    Journal: Nature immunology

    Article Title: TGF-β is responsible for NK cell immaturity during ontogeny and increased susceptibility to infection during mouse infancy

    doi: 10.1038/ni.2388

    Figure Lengend Snippet: ( a – d ) CD11c dnR and wild type mice were injected ( i.p. ) twice a day with BrdU for 3 days. On day 3, mice were sacrificed and frequency of cycling cells was determined in the bone marrow. ( a – b ) FACS plots show the distribution of BrdU staining among total mNK cells ( a ) and gated mNK cells at stages D, E, and F ( b ). ( c ) Graph shows the frequency of cycling cells in pNK, iNK, and mNK cells from CD11c dnR (black circle) versus wild type (white circle) mice. ( d ) Graph shows the frequency of cycling cells in mNK cells at stages D, E, and F from CD11c dnR (black circle) versus wild type (white circle) mice. Data in a , d are representative of three independent experiments with n = 2 mice per experiment and results in c , d show all 6 individual mice. ( e – f ) mNK cells at stages D, E, and F were sorted from the bone marrow. mRNA was isolated and cDNA was subjected to pathway-specific qPCR for analysis of cell cycle genes ( e ) or SYBR Green qPCR for analysis of transcription factors T-bet, GATA-3, and IRF-2 ( f ). Data were analyzed using Global Pattern Recognition analytical software ( e ) or 2-33C3 method ( f ) and results were expressed as fold of change in CD11c dnR versus wild type samples. Data in e , f are representative of three independent cell sorting with samples pooled from n = 12 CD11c dnR and 25 WT mice.

    Article Snippet: For cell cycle genes, we used a customized cell cycle qPCR array according to the manufacturer’s instructions (Lonza), and data were analyzed using Global Pattern Recognition analytical software (Lonza).

    Techniques: Injection, BrdU Staining, Isolation, SYBR Green Assay, Software, FACS