Machine learning as new promising technique for selection of significant features in obese women with type 2 diabetes.

Background The global trend of obesity and diabetes is considerable. Recently, the early diagnosis and accurate prediction of type 2 diabetes mellitus (T2DM) patients have been planned to be estimated according to precise and reliable methods, artificial networks and machine learning (ML). Materials and methods In this study, an experimental data set of relevant features (adipocytokines and anthropometric levels) obtained from obese women (diabetic and non-diabetic) was analyzed. Machine learning was used to select significant features [by the separability-correlation measure (SCM) algorithm] for classification of women with the best accuracy and the results were evaluated using an artificial neural network (ANN). Results According to the experimental data analysis, a significant difference (p < 0.05) was found between fasting blood sugar (FBS), hemoglobin A1c (HbA1c) and visfatin level in two groups. Moreover, significant correlations were determined between HbA1c and FBS, homeostatic model assessment (HOMA) and insulin, total cholesterol (TC) level and body mass index (BMI) in non-diabetic women and insulin and HOMA, FBS and HbA1c, insulin and HOMA, systolic blood pressure (SBP) and diastolic blood pressure (DBP), BMI and TC and HbA1c and TC in the diabetic group. Furthermore, there were significant positive correlations between adipocytokines except for the resistin and leptin levels for both groups. The excellent (FBS and HbA1c), good (HOMA) and fair (visfatin, adiponectin and insulin) discriminators of diabetic women were determined based on specificities and sensitivities level. The more selected features in the ML method were FBS, apelin, visfatin, TC, HbA1c and adiponectin. Conclusions Thus, the subset of features involving FBS, apelin, visfatin and HbA1c are significant features and make the best discrimination between groups. In this study, based on statistical and ML results, the useful biomarkers for discrimination of diabetic women were FBS, HbA1c, HOMA, insulin, visfatin, adiponectin and apelin. Eventually, we designed useful software for identification of T2DM and the healthy population to be utilized in clinical diagnosis.

Induced pluripotent stem cell-derived extracellular vesicles: A novel approach for cell-free regenerative medicine.

In recent years, induced pluripotent stem cells (iPSCs) have been considered as a promising approach in the field of regenerative medicine. iPSCs can be generated from patients' somatic cells and possess the potential to differentiate, under proper conditions, into any cell type. However, the clinical application of iPS cells is restricted because of their tumorigenic potential. Recent studies have indicated that stem cells exert their therapeutic benefit via a paracrine mechanism, and extracellular vesicles have been demonstrated that play a critical role in this paracrine mechanism. Due to lower immunogenicity, easier management, and presenting no risk of tumor formation, in recent years, researchers turned attention to exosomes as potential alternatives to whole-cell therapy. Application of exosomes derived from iPSCs and their derived precursor provides a promising approach for personalized regenerative medicine. This study reviews the physiological functions of extracellular vesicles and discusses their potential therapeutic benefit in regenerative medicine.

Molecular Targeting of Notch Signaling Pathway by DAPT in Human Ovarian Cancer: Possible Anti Metastatic Effects

Background: Ovarian cancer is one of the most important gynecological malignancies, causing significant mortality. Recently, there has been extensive attention to the involvement of signaling cascades in its initiation/progression. In this study, we focused on the possible role of Notch signal transduction in proliferation and metalloproteinase 2 and 9 function in human ovarian cancer OVCAR-3 cells. Methods: MTT proliferation assays were used to evaluate effects of a DAPT inhibitor on cell proliferation. For measurement of Hes-1 mRNA levels, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was applied following 48 h incubation with the inhibitor. In addition, metalloproteinase (MMPs) activity was assessed by zymography. Results: Inhibition of Notch signaling resulted in a significant reduction in OVCAR-3 cell proliferation. Additionally, DAPT treatment of cells significantly decreased Hes-1 mRNA levels (p < 0.05) as well as activity of MMP-2 and -9 (p < 0.05). Conclusion: Our results suggested that suppression of Notch signaling by a specific inhibitor can effectively decrease proliferation and the potential for metastasis of OVCAR-3 cells via a reduction in the activity of metalloproteinases 2 and 9. Thus, pharmacological targeting of the Notch signaling pathway could be a promising future treatment for ovarian cancer.

A Systems Biology Approach Provides Deeper Insights into Differentially Expressed Genes in Taxane-Anthracycline Chemoresistant and Non-Resistant Breast Cancers

Objective: To date, numerous studies have been conducted to search for reasons for chemoresistance and differences in survival rates of patients receiving chemotherapy. We have sought to identify differentially expressed genes (DEGs) between predicted chemotherapy resistance and sensitive phenotypes by a network as well as gene enrichment approach. Methods: Functional modules were explored with network analysis of DEGs in predicted neoadjuvant taxane-anthracycline resistance versus sensitive cases in the GSE25066 dataset, including 508 samples. A linear model was created by limma package in R to establish DEGs. Results: A gene set related to phagocytic vesicle membrane was found to be up-regulated in chemoresistance samples. Also, we found GO_CYTOKINE_ACTIVITY and GO_GROWTH_FACTOR BINDING to be up-regulated gene sets with the chemoresistance phenotype. Growth factors and cytokines are two groups of agents that induce the immune system to recruit APCs and promote tolerogenic phagocytosis. Some hub nodes like S100A8 were found to be important in the chemoresistant tumor cell network with associated high rank genes in GSEA. Conclusions: Functional gene sets and hub nodes could be considered as potential treatment targets. Moreover, by screening and enrichment analysis of a chemoresistance network, ligands and chemical agents have been found that could modify significant gene sets like the phagocytic vesicle membrane functional gene set as a key to chemoresistance. They could also impact on down- or up-regulated hub nodes.

MicroRNAs and adipocytokines: Promising biomarkers for pharmacological targets in diabetes mellitus and its complications.

Nowadays, diabetes mellitus (DM) along with its complications is considered as a fundamental problem in both developing and industrial countries, and is causing millions of people to suffer worldwide. Currently, diabetes mellitus is diagnosed traditionally or classically in the world by measuring fasting blood glucose and conducting oral glucose tolerance test. New alternatives are required for the treatment of diabetes mellitus, especially type 2 diabetes mellitus (T2DM), at its early levels due to the ineffective control of its development in patients. In recent years, by further identifying of molecular agents such as microRNAs (miRNAs), studies have focused on miRNAs in diabetes as well as in other diseases. These small non-coding RNA molecules have a significant role in the regulation of insulin gene expression and also, obesity problems. White adipose tissue, as an important tissue in obese subjects, is directly related to type 2 diabetes and its complications via synthesis of adipokines. Prevention and treatment of obesity should be noted since childhood. Our aim in this review is to briefly provide a new glance at types of potential biomarkers, which can be used as pharmacological targets for prevention and treatment of prediabetic subjects, and patients with T2DM.

A new insight on reciprocal relationship between microRNA expression and epigenetic modifications in human lung cancer.

Lung cancer stands among the leading causes of cancer-related death in the world. Although the molecular network implicated in lung cancer development is extensively revealed, the mortality rate is only slightly improved. MicroRNAs are small, endogenous single-stranded evolutionary conserved non-coding RNAs which involve in a wide variety of biological processes including cell growth, proliferation, metabolism, and differentiation. MicroRNAs, as novel biomarkers, have multiple functions in normal lung tissue development, and aberrant expression profiles of certain microRNAs could induce lung tumorigenesis. Similar to that of protein-coding genes, microRNA expression and function are regulated by multiple factors as well as the epigenetic network including DNA methylation and histone modification mechanisms. Furthermore, microRNAs can themselves regulate key enzymes which drive epigenetic modifications and have a pivotal effect on the cell biology. In this review, we will look into the regulatory loop linkage between microRNA expression and epigenetic modifications, and then, we will discuss the effects of epigenetics on the miRNome, as well as the role of epi-microRNAs in controlling the epigenome in human lung cancer. Better knowledge of reciprocal connection between microRNAs and epigenome will help to develop novel microRNA-orientated diagnostic, prognostic and therapeutic strategies related to human lung cancer in future.

An update on sputum MicroRNAs in lung cancer diagnosis.

Lung cancer is one of the leading cause of cancer mortality in the world. It is well known that genetic damages could result in lung tumor genesis. Despite years of research, the survival rate of the patients has not been markedly improved. According to lack of high sensitivity and specificity in diagnostic tests, just about 15-20% of lung cancer cases are discovered prior to progression of the disease. In last decade, sputum biomarkers have been developed for early detection/diagnosis of lung cancer. MicroRNAs are a class of small endogenous noncoding RNAs, which act as post-transcriptional regulators. Some specific miRNAs can have multifunctions in lung development and their aberrant expression could induce lung tumor genesis. The differences in miRNAs between the normal and cancerous lung lead to emerging of a novel type of biomarkers, which can be helpful in screening of high risk individuals, diagnosis of lung cancer as well as its therapy.

Gene silencing effect of SiRNA-magnetic modified with biodegradable copolymer nanoparticles on hTERT gene expression in lung cancer cell line.

BACKGROUND: Telomerase is expressed in most of malignant cells, including lung cancer cells. The success of small interfering RNA (siRNA) in silencing of the telomerase catalytic subunit depends upon carriers ability to efficiently deliver therapeutic agent to cells with minimal toxicity and most biocompatibility. In this study, a potential carrier for efficient delivery was assessed by siRNA encapsulating into Iron MNPs modified with biodegradable polyester nanoparticles consisting of PLGA and PEG. RESULTS: Data analysis shows that the self-assemble diblock copolymers were synthesized, and then the siRNA designed against hTERT catalytic subunit was encapsulated. Also, the rate of telomerase gene expression in equivalent with magnetic copolymers/siRNA was lower than that of free siRNA (P = 0.001). CONCLUSIONS: In conclusion, regarding the enhancing of siRNA stability by magnetic copolymer, the expression of telomerase gene was significantly lower in the cells treated with siRNA-magnetic copolymers than those treated with free siRNA.

Application of gold nanoparticles in biomedical and drug delivery.

Nanoparticles are the simplest form of structures with sizes in the nanometer (nm) range. In principle any collection of atoms bonded together with a structural radius of < 100 nm can be considered nano particles. Nanotechnology offers unique approaches to probe and control a variety of biological and medical processes that occur at nanometer scales, and is expected to have a revolutionary impact on biology and medicine. Among the approaches for exploiting nanotechnology in medicine, nanoparticles offer some unique advantages as sensing, image enhancement, and delivery agents. Several varieties of nanoparticles with biomedical relevance are available including, polymeric nanoparticles, metal nanoparticles, liposomes, micelles, quantum dots, dendrimers, and nanoassemblies. To further the application of nanoparticles in disease diagnosis and therapy, it is important that the systems are biocompatible and capable of being functionalized for recognition of specific target sites in the body after systemic administration. In this review, we have explained some important applications of gold nanoparticles.

Silver nanoparticles: Synthesis methods, bio-applications and properties.

Silver nanoparticles size makes wide range of new applications in various fields of industry. Synthesis of noble metal nanoparticles for applications such as catalysis, electronics, optics, environmental and biotechnology is an area of constant interest. Two main methods for Silver nanoparticles are the physical and chemical methods. The problem with these methods is absorption of toxic substances onto them. Green synthesis approaches overcome this limitation. Silver nanoparticles size makes wide range of new applications in various fields of industry. This article summarizes exclusively scalable techniques and focuses on strengths, respectively, limitations with respect to the biomedical applicability and regulatory requirements concerning silver nanoparticles.