{{get_dataset_fail}}




{{section.text}} {{section.text}} {{section.text}} {{section.text}} {{dataset.name}}


There are an estimated 21million diabetics in the United States and 150 million diabetics worldwide. The World Health Organization anticipates that these numbers will double in the next 20 years. Metabolic syndrome is a well recognized set of symptoms that increases a patient’s risk of developing diabetes. Insulin resistance is a factor in both metabolic syndrome and Type 2 diabetes. It is characterized by decreased insulin stimulated glucose uptake in peripheral tissues, decreased adiponectin levels, increased adipocyte FFA and cytokine production, and increased insulin and hepatic glucose output. Prevention or reversal of insulin resistance should serve as an important strategy in addressing the growing health concerns posed by the Diabetes epidemic. While increased adiposity is associated with insulin resistance, the role of the cell types present within adipose (adipocytes, pre-adipocytes, endothelial cells, macrophages, fibroblasts, leukocytes and smooth muscle cells) in insulin resistance is unclear. In an effort to begin dissection of this question, we examined the transcriptional response of the buoyant and non-buoyant fractions isolated from insulin sensitive or TNF induced insulin resistant hMSC derived adipocytes before and after treatment with insulin. hMSC derived adipocytes were treated with 0 or 10ng/ml TNF for 24 hours to induce insulin resistance and subsequently serum starved for 5 h followed by treatment with 0 or 20nM insulin for 2 hours. At the end of the incubation period, cells were harvested as is (mixed) or subjected to fractionation to separate the adipocytes (buoyant fraction) and the stromal cells (non-buoyant fraction). These isolated cells were resuspended in RLT buffer to prepare lysates for total RNA isolation using the Qiagen RNeasy kit. Total RNA was submitted to GeneLogic for cRNA preparation using the Affymetrix GenChip IVT kit and hybridization to Affymetrix U133 Plus 2.0 arrays.

ABSTRACT: {{section.text}} {{section.text}} {{section.text}} {{section.text}} {{abstract_sections[abstract_sections.length-1].tobeReduced=='true'?"... [more]":""}} [less]

SAMPLE PROTOCOL: {{section.text}} {{section.text}} {{section.text}} {{section.text}} {{sample_protocol_sections[sample_protocol_sections.length-1].tobeReduced=='true'?"... [more]":""}} [less]

DATA PROTOCOL: {{section.text}} {{section.text}} {{section.text}} {{section.text}} {{data_protocol_sections[data_protocol_sections.length-1].tobeReduced=='true'?"... [more]":""}} [less]

REANALYSIS of: {{reanalysis_item.accession}}

REANALYZED by: {{reanalyzed_item.accession}}

OTHER RELATED OMICS DATASETS IN: {{reanalysis_item.accession}}

INSTRUMENT(S): {{instrument+';'}}

ORGANISM(S): {{organism.name + ';'}}

TISSUE(S): {{tissue+';'}}

DISEASE(S): {{disease+';'}}

SUBMITTER: {{dataset['submitter']}}

PROVIDER: {{acc}} | {{repositories[domain]}} | {{dataset['publicationDate']}}

{{publication_info[publication_index_info[dataset.publicationIds[current_publication]]].title}}

{{author.fullname.substr(0,author.fullname.length-2)}} ,

{{publication_info[publication_index_info[dataset.publicationIds[current_publication]]].citation}}


Sorry, this publication's infomation has not been loaded in the Indexer, please go directly to PUBMED or Altmetric.

ABSTRACT: {{publication_info[publication_index_info[dataset.publicationIds[current_publication]]].pub_abstract[0]}}
{{publication_info[publication_index_info[dataset.publicationIds[current_publication]]].pub_abstract[1]}} [less]

ABSTRACT: {{publication_info[publication_index_info[dataset.publicationIds[current_publication]]].pub_abstract[0]|limitTo:500}} {{publication_info[publication_index_info[dataset.publicationIds[current_publication]]].pub_abstract[0].length>500?"... [more]":""}}

Publication: {{current_publication +1}}/{{dataset.publicationIds.length}}

{{dataset.publicationIds[current_publication].publicationDate}}


Only show the datasets with similarity scores above:{{threshold}}

Threshold:
    {{threshold}}
     

The biological similarity score is calculated based on the number of molecules (Proteins, Metabolites, Genes) common between two different projects.

Similar Datasets

  • Organism: {{organism["name"]}} Not available
    {{relatedDataset['publicationDate'].substr(0,4)+"-"+relatedDataset['publicationDate'].substr(4,2)+"-"+relatedDataset['publicationDate'].substr(6,2)}}| {{relatedDataset.id}} | {{repositories[relatedDataset.source]}}