IL-6-174 G/C and -572 C/G Polymorphisms and Risk of Alzheimer’s Disease

Associations between interleukin 6 (IL-6) polymorphisms and Alzheimer’s disease (AD) remain controversial and ambiguous. The aim of this meta-analysis is to explore more precise estimations for the relationship between IL-6-174 G/C and -572 C/G polymorphisms and risk for AD. Electronic searches for all publications in databases PubMed and EMBASE were conducted on the associations between IL-6 polymorphisms and risk for AD until January 2012. Odds ratio (OR) and 95% confidence intervals (CIs) were calculated using fixed and random effects models. Twenty-seven studies were included with a total of 19,135 individuals, involving 6,632 AD patients and 12,503 controls. For IL-6-174 G/C polymorphism, the combined results showed significant differences in recessive model (CC vs. CG+GG: OR = 0.65, 95%CI = 0.52–0.82). As regards IL-6-572 C/G polymorphism, significant associations were shown in dominant model (CG+GG vs. CC: OR  = 0.73, 95% CI = 0.62–0.86) and in additive model (GG vs. CC, OR  = 0.66, 95% CI = 0.46–0.96). In conclusion, genotype CC of IL-6-174 G/C and genotype GG plus GC of IL-6-572 C/G could decrease the risk of AD

Vitamin D pathway-related gene polymorphisms and their association with metabolic diseases: a literature review

Purpose: Given that the relationship between vitamin D status and metabolic diseases such as obesity and type 2 diabetes (T2D) remains unclear, this review will focus on the genetic associations, which are less prone to confounding, between vitamin D-related single nucleotide polymorphisms (SNPs) and metabolic diseases. Methods: A literature search of relevant articles was performed on PubMed up to December 2019. Those articles that had examined the association of vitamin D-related SNPs with obesity and/or T2D were included. Two reviewers independently evaluated the eligibility for the inclusion criteria and extracted the data. In total, 73 articles were included in this review. Results: There is a lack of research focussing on the association of vitamin D synthesis-related genes with obesity and T2D; however, the limited available research, although inconsistent, is suggestive of a protective effect on T2D risk. While there are several studies that investigated the vitamin D metabolism-related SNPs, the research focussing on vitamin D activation, catabolism and transport genes is limited. Studies on CYP27B1, CYP24A1 and GC genes demonstrated a lack of association with obesity and T2D in Europeans; however, significant associations with T2D were found in South Asians. VDR gene SNPs have been extensively researched; in particular, the focus has been mainly on BsmI (rs1544410), TaqI (rs731236), ApaI (rs7975232) and FokI (rs2228570) SNPs. Even though the association between VDR SNPs and metabolic diseases remain inconsistent, some positive associations showing potential effects on obesity and T2D in specific ethnic groups were identified. Conclusion: Overall, this literature review suggests that ethnic-specific genetic associations are involved. Further research utilizing large studies is necessary to better understand these ethnic-specific genetic associations between vitamin D deficiency and metabolic diseases

A simple chaotic neuron model: Stochastic behavior of neural networks

We have briefly reviewed the occurrence of the post-synaptic potentials between neurons, the relationship between EEG and neuron dynamics, as well as methods of signal analysis. We propose a simple stochastic model representing electrical activity, of neuronal systems. The model is constructed using the Monte Carlo simulation technique. The results yielded EEG-like signals with their phase portraits in three-dimensional space. The Lyapunov exponent was positive, indicating chaotic behavior. The correlation of the EEG-like signals was .92, smaller than those reported by others. It was concluded that this neuron model may provide valuable clues about the dynamic behavior of neural systems

Object-based image labeling through learning by example and multi-level segmentation

We propose a method for automatic extraction and labeling of semantically meaningful image objects using "learning by example" and threshold-free multi-level image segmentation. The proposed method scans through images, each of which is pre-segmented into a hierarchical uniformity tree, to seek and label objects that are similar to an example object presented by the user. By representing images with stacks of multi-level segmentation maps, objects can be extracted in the segmentation map level with adequate detail. Experiments have shown that the proposed multi-level image segmentation results in significant reduction in computation complexity for object extraction and labeling (compared to a single fine-level segmentation) by avoiding unnecessary tests of combinations in finer levels. The multi-level segmentation-based approach also achieves better accuracy in detection and labeling of small objects. (C) 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved

α-Lipoic Acid Ameliorates The Changes in Prooxidant-Antioxidant Balance in Liver and Brain Tissues of Propylthiouracil-Induced Hypothyroid Rats.

Objective: There are controversial data about the prooxidant-antioxidant balance in hypothyroidism. We aimed to investigate the effect of alpha-lipoic acid (ALA) on oxidative stress parameters in the liver and brain of propylthiouracil (PTU)-induced hypothyroid rats