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accession-icon GSE32473
Gene expression is differently affected by pimecrolimus and betamethasone in lesional skin of atopic dermatitis.
  • organism-icon Homo sapiens
  • sample-icon 30 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Topical corticosteroids and calcineurin inhibitors are well known treatments of atopic dermatitis (AD), but differ in their efficacy and side effects. A study in AD patients has demonstrated that betamethasone valerate (BM) though clinically more efficient impaired skin barrier repair in contrast to pimecrolimus. Objective: The present study elucidates the mode of action of topical BM and pimecrolimus cream in AD.

Publication Title

Gene expression is differently affected by pimecrolimus and betamethasone in lesional skin of atopic dermatitis.

Sample Metadata Fields

Specimen part

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accession-icon GSE83610
Renal fibrosis mRNA classifier: validation in experimental lithium-induced interstitial fibrosis in the rat kidney
  • organism-icon Rattus norvegicus
  • sample-icon 24 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Gene 1.0 ST Array (ragene10st)

Description

Metzincins and related genes (MARGS) play important roles in ECM remodeling in fibrotic conditions.

Publication Title

Renal Fibrosis mRNA Classifier: Validation in Experimental Lithium-Induced Interstitial Fibrosis in the Rat Kidney.

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE17861
Analyses of heterogeneous renal allograft biopsies reveal conserved rejection signatures and molecular pathways I, partB
  • organism-icon Homo sapiens
  • sample-icon 16 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Specific early diagnosis of renal allograft rejection is gaining importance in the current trend to minimize and individualize immunosuppression. Gene expression analyses could contribute significantly by defining molecular Banff signatures. Several previous studies have applied transcriptomics to distinguish different classes of kidney biopsies. However, the heterogeneity of microarray platforms, clinical samples and data analysis methods complicates the identification of robust signatures for the different types and grades of rejection. To address these issues, a comparative meta-analysis was performed across five different microarray datasets of heterogeneous sample collections from two published clinical datasets and three own datasets including biopsies for clinical indications, protocol biopsies, as well as comparative samples from non-human primates (NHP). This work identified conserved gene expression signatures that can differentiate groups with different histopathological findings in both human and NHP, regardless of the technical platform used. The marker panels comprise genes that clearly support the biological changes known to be involved in allograft rejection. A characteristic dynamic expression change of genes associated with immune and kidney functions was observed across samples with different grades of CAN. In addition, differences between human and NHP rejection were essentially limited to genes reflecting interstitial fibrosis progression. This data set comprises all renal allograft biopsies for clinical indications from patients at Hpital Tenon, Paris (February 2003 until September 2004) and few respective patients from Hpital Bictre, Paris, Hpital Pellegrin, Bordeaux, and Hpital Dupuytren, Limoges, plus control normal kidney samples from Hpital Tenon, Paris, France (first batch).

Publication Title

Analysis of independent microarray datasets of renal biopsies identifies a robust transcript signature of acute allograft rejection.

Sample Metadata Fields

Sex, Age, Subject

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refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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