You have noticed that while you generally have clear skin, there are some times when you wake up with acne on your face or body. You are not sure where this has come from, so you are wondering if, perhaps, you have acne prone skin.
About Acne Prone Skin
The first way that you can tell if [...]
No one wants to suffer from acne, but it is a nearly inevitable part of growing up, and can last well into adulthood. Unsightly and sometimes painful, people with acne often suffer from lowered self-esteem, thanks to self-consciousness about their acne problem. It?s no wonder that there are plenty of things on the [...]
Background:
Polycystic ovary syndrome (PCOS) is a common endocrine disorder associated with an increased risk of type II diabetes mellitus. The results of previous research about the association of the VNTR polymorphism in 5-prime flanking region of the insulin (INS) gene with PCOS have been inconsistent. The present study was to investigate the association of the INS-VNTR polymorphism with PCOS in a Han Chinese population.
Methods:
The -23/HphI polymorphism as a surrogate marker of the INS-VNTR length polymorphism was genotyped by polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP) in 216 PCOS patients and 192 non-PCOS women as a control group. Allelic and genotypic frequencies were compared between patients and controls, and these results were analyzed in respect to clinical test data.
Results:
No significant differences were observed between the cases and controls groups either in allele (P = 0.996) or genotype (P = 0.802) frequencies of INS-VNTR polymorphism; Regarding anthropometric data and hormone levels, there were no significant differences between INS-VNTR genotypes in the PCOS group, as well as in the non-PCOS group.
Conclusion:
The present study demonstrated for the first time that the INS-VNTR polymorphism is not a key risk factor for sporadic PCOS in the Han Chinese women. Further studies are needed to give a global view of this polymorphism in pathogenesis of PCOS in a large-scale sample, family-based association design or well-defined subgroups of PCOS.
Background:
It is essential to elucidate the relationship between metabolic and genomic diversity to understand the genetic regulatory networks associated with the changing metabolo-phenotype among natural variation and/or populations. Recent innovations in metabolomics technologies allow us to grasp the comprehensive features of the metabolome. Metabolite quantitative trait analysis is a key approach for the identification of genetic loci involved in metabolite variation using segregated populations. Although several attempts have been made to find correlative relationships between genetic and metabolic diversity among natural populations in various organisms, it is still unclear whether it is possible to discover such correlations between each metabolite and the polymorphisms found at each chromosomal location. To assess the correlative relationship between the metabolic and genomic diversity found in rice accessions, we compared the distance matrices for these two “omics” patterns in the rice accessions.
Results:
We selected 18 accessions from the world rice collection based on their population structure. To determine the genomic diversity of the rice genome, we genotyped 128 restriction fragment length polymorphism (RFLP) markers to calculate the genetic distance among the accessions. To identify the variations in the metabolic fingerprint, a soluble extract from the seed grain of each accession was analyzed with one dimensional 1H-nuclear magnetic resonance (NMR). We found no correlation between global metabolic diversity and the phylogenetic relationships among the rice accessions (rs = 0.14) by analyzing the distance matrices (calculated from the pattern of the metabolic fingerprint in the 4.29- to 0.71-ppm 1H chemical shift) and the genetic distance on the basis of the RFLP markers. However, local correlation analysis between the distance matrices (derived from each 0.04-ppm integral region of the 1H chemical shift) against genetic distance matrices (derived from sets of 3 adjacent markers along each chromosome), generated clear correlations ((rs > 0.4, p < 0.001) at 34 RFLP markers.
Conclusions:
This combinatorial approach will be valuable for exploring the correlative relationships between metabolic and genomic diversity. It will facilitate the elucidation of complex regulatory networks and those of evolutionary significance in plant metabolic systems.
Background:
The gamma/delta T cells serve as early immune defense against certain encountered microbes. Only a few gamma/delta T cell-recognized ligands from microbial antigens have been identified so far and the mechanisms by which gamma/delta T cells recognize these ligands remain unknown. Here we explored the mechanism of interaction of human gamma/delta T cells in peripheral blood with Lipid A (LA).
Results:
First, resting gamma/delta T cells (mainly V delta2 T cells) displayed a strong proliferative response to LA-pulsed monocyte-derived dendritic cells (moDC) and LA-pulsed paraformaldehyde-fixed moDC, but not to free LA in a TCR gamma/delta-dependent manner. Second, anti-CD1b or anti-CD1c antibodies could block proliferative response of resting gamma/delta T cells to LA-loaded moDC. Besides, only LA-loaded CD1b/CD1c-transfected C1R lymphoblastoma cells (CD1b-/CD1c-C1R) were able to stimulate the proliferation of human gamma/delta T cells. Third, the expressions of both Toll-like receptor (TLR)2 and TLR4 on surface of LA-activated gamma/delta T cells were upregulated, whereas only anti-TLR4 antibody could partially block their response to LA; Finally LA-loaded moDCs induce gamma/delta T cells to produce Th1 cytokines, such as IFN-gamma.
Conclusions:
Taken together, we found a novel mechanism that human gamma/delta T cells recognize LA in a CD1b- or CD1c-restricted manner in first response against Gram- bacteria, while the interaction between TLR4 on gamma/delta T cells and LA might strengthen the subsequent response of gamma/delta T cells.Reviewers: This article was reviewed by Dr.Youwen He (nominated by Dr.Laurence C Eisenlohr),Dr.Hao Shen,Dr.Michael Lenardo and Dr. Pushpa Pandiyan.