姜黄素单羰基类似物(MACs)C66 和 B2BrBC 对链脲佐菌素处理的大鼠胰腺 RIN-m 细胞中糖尿病相关基因表达的影响--定量 RT-PCR 阵列数据

IF 1 Q3 MULTIDISCIPLINARY SCIENCES
Radoslav Stojchevski , Sara Velichkovikj , Jane Bogdanov , Nikola Hadzi-Petrushev , Mitko Mladenov , Leonid Poretsky , Dimiter Avtanski
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引用次数: 0

摘要

本文介绍了在有或没有链脲佐菌素(STZ)的情况下,用姜黄素的两种单羰基类似物(MACs)C66 和 B2BrBC 处理大鼠胰腺 RIN-m 细胞的 RT2-qPCR 阵列分析所获得的数据集。该阵列量化了与糖尿病发病、发展和恶化相关的 84 个基因的表达。该数据集提供了糖尿病背景下两种特定澳门美高梅国际娱乐平台调节的胰腺细胞基因表达谱的信息。这些数据可作为提出新假设、设计后续实验和确定新治疗靶点的基础。它还可用于进一步研究这些 MACs 治疗效果的分子机制,以及使用其他实验性抗糖尿病化合物进行比较研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of monocarbonyl analogs of curcumin (MACs) C66 and B2BrBC on the expression of diabetes-associated genes in streptozotocin-treated rat pancreatic RIN-m cells—Quantitative RT-PCR array data

This paper presents a dataset obtained from an RT2-qPCR array analysis of rat pancreatic RIN-m cells treated with two monocarbonyl analogs of curcumin (MACs), C66 and B2BrBC in the presence or absence of streptozotocin (STZ). The array quantified the expression of 84 genes associated with the onset, development, and progression of diabetes. This dataset provides information on the gene expression profiles of pancreatic cells modulated by two specific MACs in a diabetic context. The data can serve as a foundation for developing new hypotheses, designing follow-up experiments, and identifying novel targets for treatment. It can be used to investigate further the molecular mechanisms underlying the therapeutic effects of these MACs and in comparative studies using other experimental antidiabetic compounds.

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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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