ABSTRACT
A common denominator of metabolic diseases, including type 2 diabetes Mellitus, dyslipidemia, and atherosclerosis, are elevated oxidative stress and chronic inflammation. These complex, multi-factorial diseases are caused by the detrimental interaction between the individual genetic background and multiple environmental stimuli. The cells, including the endothelial ones, acquire a preactivated phenotype and metabolic memory, exhibiting increased oxidative stress, inflammatory gene expression, endothelial vascular activation, and prothrombotic events, leading to vascular complications. There are different pathways involved in the pathogenesis of metabolic diseases, and increased knowledge suggests a role of the activation of the NF-kB pathway and NLRP3 inflammasome as key mediators of metabolic inflammation. Epigenetic-wide associated studies provide new insight into the role of microRNAs in the phenomenon of metabolic memory and the development consequences of vessel damage. In this review, we will focus on the microRNAs related to the control of anti-oxidative enzymes, as well as microRNAs related to the control of mitochondrial functions and inflammation. The objective is the search for new therapeutic targets to improve the functioning of mitochondria and reduce oxidative stress and inflammation, despite the acquired metabolic memory.
ABSTRACT
The development of type 2 diabetes mellitus (T2DM) vascular complications (VCs) is associated with oxidative stress and chronic inflammation and can result in endothelial dysfunctions. Circulating microRNAs play an important role in epigenetic regulation of the etiology of T2DM. We studied 30 healthy volunteers, 26 T2DM patients with no complications, and 26 T2DM patients with VCs, to look for new biomarkers indicating a risk of developing VCs in T2DM patients. Peripheral blood samples were used to determine redox state, by measuring the endogenous antioxidant defense system (superoxide dismutase, SOD; catalase, CAT; glutathione reductase, GRd; glutathione peroxidase, GPx; and glucose-6-phosphate dehydrogenase, G6DP) and markers of oxidative damage (advanced oxidation protein products, AOPP; lipid peroxidation, LPO). Additionally, inflammatory marker levels (IL-1, IL-6, IL-18, and TNF-α), c-miR-21, and c-miR-126 expression were analyzed. T2DM patients showed the highest oxidative damage with increased GSSG/GSH ratios, LPO, and AOPP levels. In both diabetic groups, we found that diminished SOD activity was accompanied by increased CAT and decreased GRd and G6PD activities. Diabetic patients presented with increased relative expression of c-miR-21 and decreased relative expression of c-miR-126. Overall, c-miR-21, SOD, CAT, and IL-6 had high predictive values for diabetes diagnoses. Finally, our data demonstrated that IL-6 exhibited predictive value for VC development in the studied population. Moreover, c-miR-21 and c-miR-126, along with GPx and AOPP levels, should be considered possible markers for VC development in future studies.
ABSTRACT
Breast cancer (BC) diagnosis is made by a pathologist who analyzes a portion of the breast tissue under the microscope and performs a histological evaluation. This evaluation aims to determine the grade of cellular differentiation and the aggressiveness of the tumor by the Nottingham Grade Classification System (NGS). Nowadays, digital pathology is an innovative tool for pathologists in diagnosis and acquiring new learning. However, a recurring problem in health services is the excessive workload in all medical services. For this reason, it is required to develop computational tools that assist histological evaluation. This work proposes a methodology for the quantitative analysis of BC tissue that follows NGS. The proposed methodology is based on digital image processing techniques through which the BC tissue can be characterized automatically. Moreover, the proposed nuclei characterization was helpful for grade differentiation in carcinoma images of the BC tissue reaching an 0.84 accuracy. In addition, a metric was proposed to assess the likelihood of a structure in the tissue corresponding to a tubule by considering spatial and geometrical characteristics between lumina and its surrounding nuclei, reaching an accuracy of 0.83. Tests were performed from different databases and under various magnification and staining contrast conditions, showing that the methodology is reliable for histological breast tissue analysis.
