Stojchevski Radoslav, Velichkovikj Sara, Bogdanov Jane, Hadzi-Petrushev Nikola, Mladenov Mitko, Poretsky Leonid, Avtanski Dimiter
Friedman Diabetes Institute, Lenox Hill Hospital, Northwell Health, New York, NY, USA.
Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
Data Brief. 2024 Sep 16;57:110952. doi: 10.1016/j.dib.2024.110952. eCollection 2024 Dec.
This paper presents a dataset obtained from an RT-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.
本文展示了一个数据集，该数据集来自于对大鼠胰腺RIN - m细胞进行逆转录定量聚合酶链反应（RT - qPCR）阵列分析的结果。这些细胞在有或没有链脲佐菌素（STZ）存在的情况下，用两种姜黄素单羰基类似物（MACs），即C66和B2BrBC进行处理。该阵列定量了84个与糖尿病的发生、发展和进展相关的基因的表达。这个数据集提供了在糖尿病背景下，由两种特定MACs调节的胰腺细胞基因表达谱的信息。这些数据可作为开发新假设、设计后续实验以及确定新治疗靶点的基础。它可用于进一步研究这些MACs治疗效果背后的分子机制，以及用于使用其他实验性抗糖尿病化合物的比较研究。
