Trend of Cardio-Metabolic Risk Factors in Polycystic Ovary Syndrome: A Population-Based Prospective Cohort Study

Objective

To see the changes of cardio-metabolic risk factors overtime in polycystic ovary syndrome vs. control women.

Methods

This study was conducted on 637 participants (85 PCOS and 552 control reproductive aged, 18–45 years) of Tehran Lipid and Glucose Study (TLGS), an ongoing population-based cohort study with 12 years of follow-up. The cardiovascular risk factors of these groups were assessed in three-year intervals using standard questionnaires, history taking, anthropometric measures, and metabolic/endocrine evaluation. Generalized estimating equation was used to analyze the data.

Results

Overall mean of insulin (3.55, CI: 0.66–6.45), HOMA-IR (0.63, CI: 0.08–1.18), and HOMA-β (45.90, CI: 0.86–90.93) were significantly higher in PCOS than in healthy women after adjustment for age, BMI, and baseline levels. However, the negative interaction (follow-up years × PCOS status) of PCOS and normal women converged overtime. Comparing third follow-up with first, insulin and HOMA-IR decreased 10.6% and 5%, respectively in PCOS women; and increased 6.7% and 14.6%, respectively in controls (P<0.05). The results did not show any significant result for other cardio-metabolic variables including WC, lipid profile, FPG, 2-h PG, SBP, and DBP.

Conclusion

While the insulin level and insulin resistance rate were higher in reproductive aged PCOS than in healthy women, the difference of these risk factors decreased overtime. Thus, the metabolic consequences of PCOS women in later life may be lower than those initially anticipated.     

Evaluation of the effects of weak and moderate static magnetic fields on the characteristics of human low density lipoprotein in vitro

It has been suggested that exposure to electromagnetic fields may be a risk factor for cardiovascular disease in humans. Low density lipoprotein (LDL) modifications such as peroxidation and aggregation have been implicated in the pathogenesis of atherosclerosis. The present study investigated the effects of weak (0.125–0.5 mT) and moderate (1–4 mT) static magnetic fields (SMFs) on LDL oxidation, aggregation and zeta potential in vitro. Our results demonstrated that magnetic flux densities of 0.25 and 0.5 mT decreased, and magnetic flux densities of 3 and 4 mT increased the zeta potential and LDL oxidation in comparison with the control samples. All doses of SMFs increased the LDL aggregation in a time‐ and dose‐dependent manner. It is concluded that SMFs can alter the susceptibility of LDL to oxidation and this alteration is dependent on the applied magnetic flux density. The SMF, in addition to its role in the production and stabilization of free radicals and promotion of lipid peroxidation, may influence the metabolism of lipoproteins and their interaction with other molecules such as apolipoproteins, enzymes and receptors through the alteration of the LDL zeta potential and its particles tendency to aggregation. Bioelectromagnetics 34:397–404, 2013. © 2012 Wiley Periodicals, Inc.     

The effect of different salts (heavy metals) on the mycelium growth of Nematophagus fungi

Environmental pollution in addition to direct damage on plant growth, with the destruction of biological control agents, causes indirect damage to plants. The aim of this research was to study the effects of different concentrations (0, 500, 1000, 1500 and 2000 ppm) of heavy metals including Ag, Co, Cu, Fe, Hg, Mn, Pb and Zn on the mycelial growth and to assess the fungicidal or fungistatic effects of these salts on five Nematophagus fungi including Trichoderma harzianum (T8), Trichoderma virens (T21), Trichoderma hamatum (T9), Pochonia chlamydosporia var. chlamydosporia and Arthrobotrys oligospora. The results show that Ag, Co, Cu, Fe and Hg could stop the mycelium growth of all fungi, but Mn, Pb and Zn cannot inhibit the growth of these fungi completely. Among the first group, Hg and Cu stopped the growth of fungi even in 500 ppm. Among these metals that inhibit the growth of fungi, Cu has fungistatic effect and others have fungicide effect. The experiment was conducted in vitro condition, using potato dextrose agar (PDA) under complete randomised design with four replications. The data of mycelium growth were recorded at seven days after inoculation at 25 ± 2°C.     

Adaptive neuro-fuzzy inference system: Estimation of soil aggregates stability

In present study, the capabilities of multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) in developing pedotransfer functions (PTFs) for estimating geometric mean diameter (GMD) and mean weight diameter (MWD), from routine soil properties and combination of routine soil properties and fractal dimension of aggregates were evaluated. For this reason 101 samples were collected form the Northwest of Iran and some their properties such as soil texture, pH, cation exchange capacity (CEC), and organic matter (OM), fractal dimension of aggregates between number-diameter (Dn), mass-diameter (Dmt), and bulk density-diameter (Dmy) were determined and used as an input variables for determining of mean weight diameter (MWD) and geometric mean diameter (GMD) by MLR and ANFIS PTFs. Results showed that the application of fractal dimension of aggregates as a predictor in two methods improved the accuracy of PTFs. As well as, results showed that ANFIS have greater potential for determination of the relationships between soil aggregate stability indices and other soil properties in compared with MLR. Therefore using of adaptive neuro-fuzzy inference system (ANFIS) in developing pedotransfer functions is recommended.     

Intraovarian injection of miR-224 as a marker of polycystic ovarian syndrome declines oocyte competency and embryo development

miR-224 is associated with polycystic ovary syndrome (PCOS) that is an epidemic in reproductive age women. Most studies of miR-224 have focused on in vitro analyses, whereas the in vivo effects are not widely understood. In this study, we have conducted in silico analysis and found two potential miR-224 target genes, Ptx3 and Smad4 that have roles in folliculogenesis. Because patients with PCOS have decreased numbers of follicular cells related to cell apoptosis, we also investigated two apoptotic genes, Bax and Bcl2. We used the intraovarian injection method to deliver miR-224 into a mouse model. Histological examination of the ovaries was done by fluorescent microscope. Fertilization, cleavage, and developmental competence rates were counted under a stereomicroscope and compared between the studied groups. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis of miR-224 was conducted to determine the levels of the studied genes in the oocytes, cumulus cells, and blastocysts. The numbers of oocytes and fertilization rate indicated a higher apoptosis index ( p < 0.05) and increased numbers of degenerated embryos with irregular blastomeres and fragmented cytoplasm in the experimental group. RT-PCR results indicated a significant increase in miR-224 levels in the manipulated group. Of the four analyzed genes, Ptx3, Smad4, and Bcl2 had decreased levels in the transfected group, with increased Bax expression ( p < 0.05). This data showed that miR-224 negatively affected ovulation in the mouse model by decreasing Ptx3 and Smad4 expressions. The changes in Bcl2 and Bax expression levels, as apoptosis biomarkers, showed that apoptosis was a secondary outcome of the effect of miR-224.     

Proteomic analysis of the Mexican lime tree response to "Candidatus Phytoplasma aurantifolia" infection

"Candidatus Phytoplasma aurantifolia" is the causative agent of witches' broom disease in the Mexican lime tree (Citrus aurantifolia L.), and is responsible for major tree losses in Southern Iran and Oman. The pathogen is strictly biotrophic, and, therefore, completely dependent on living host cells for its survival. The molecular basis of compatibility and disease development in this system is poorly understood. We applied a proteomics approach to analyse gene expression in Mexican limes infected with "Ca. Phytoplasma aurantifolia". Leaf samples were collected from healthy and infected plants and were analysed using 2-DE coupled with MS. Among 800 leaf proteins that were detected reproducibly in eight biological replicates of healthy and eight biological replicates of infected plants, 55 showed a significant response to the disease. MS resulted in identification of 39 regulated proteins, which included proteins that were involved in oxidative stress defence, photosynthesis, metabolism, and the stress response. Our results provide the first proteomic view of the molecular basis of the infection process and identify genes that could help inhibit the effects of the pathogen.     

Gene expression analysis of intestinal IL-8, IL-17 A and IL-10 in patients with celiac and inflammatory bowel diseases

Molecular Biology Reports - Celiac disease (CeD) and inflammatory bowel disease (IBD) are accompanied by impaired immune responses. To study the immune regulation of these diseases, we evaluated...     

Robust and stable feature selection by integrating ranking methods and wrapper technique in genetic data classification

High dimensional data increase the dimension of space and consequently the computational complexity and result in lower generalization. From these types of classification problems microarray data classification can be mentioned. Microarrays contain genetic and biological data which can be used to diagnose diseases including various types of cancers and tumors. Having intractable dimensions, dimension reduction process is necessary on these data. The main goal of this paper is to provide a method for dimension reduction and classification of genetic data sets. The proposed approach includes different stages. In the first stage, several feature ranking methods are fused for enhancing the robustness and stability of feature selection process. Wrapper method is combined with the proposed hybrid ranking method to embed the interaction between genes. Afterwards, the classification process is applied using support vector machine. Before feeding the data to the SVM classifier the problem of imbalance classes of data in the training phase should be overcame. The experimental results of the proposed approach on five microarray databases show that the robustness metric of the feature selection process is in the interval of [0.70, 0.88]. Also the classification accuracy is in the range of [91%, 96%].