Polymorphisms of CYP1A1 I462V and GSTM1 genotypes and lung cancer susceptibility in Mongolian

Aim: To study the genotype of cytochrome P450 1A1(CYP1A1) I462V and glutathions S-transferase M1( GSTM1) and the relationship of the genetic polymorphism of them with the susceptibility of lung cancer in Mongolia of China. 

Methods: Allele-specific PCR and a multiplex PCR were employed to identify the genotypes of I462V of CYP1A1 and GSTM1 in a case-control study of 210 lung cancer patients with bronchoscopy diagnosis and 210 matched controls free of malignancy.

Results: The frequencies of the variant CYP1A1(Val/Val) genotypes and GSTM1(-) in lung cancer groups were higher than that in control groups (15.24% vs 7.4% and 56.67% vs 40.95% ). The individuals who carried with CYP1A1(Val/Val) or GSTM1(-) genotype had a significantly higher risk of lung cancer, the OR is 2.56 and 1.89 respectively. Stratified histologically the relative risk increased to 2.6 - fold when the patients carried with two valine alleles than the ones carried one valine allele in cases of SCC. GSTM1(-) genotype is the risk factor of SCC (OR=2.39) and AC(OR=2.16). The presence of at least one Val allele of CYP1A1 and GSTM1(-), the risk of lung cancer was increased, the OR was 4.15 for one Val allele and GSTM1(-) and 2.67 for two Val alleles and GSTM1 Considering ages and smoking status, the risk of lung cancer increased when the age less than 50 who carried with CYP1A1 valine (one or two) alleles or the age during the 51 to 65 who carried with GSTM1(-) genotype. The light smokers with CYP1A1 valine alleles and GSTM1(-) have a high risk for lung cancer. No association was found between the light and heavy drinkers with the susceptibility of lung cancer and the genetic polymorphisms of CYP1A1 I462V and GSTM1(-). 

Conclusion: The valine allele of CYP1A1 was the risk factors of lung cancer especially for SCC and GSTM1(-) also was the risk factor of lung cancer and especially for SCC and AC of Mongolian, China. Light smoking has a influence each other with genotype of CYP1A1 I462V and GSTM1(-) and susceptibility of lung cancer. No relationship was found between the susceptibility of lung cancer and drinkers with genetic polymorphisms of CYP1A1 I462V and GSTM1(-). The influence of genotypes on the susceptibility of lung cancer may depend on the ages. There may be a synergetic interaction between CYP1A1 valine allele and GSTM1(-) genotypes on the elevated susceptibility of lung cancer. So do those genotypes with light smokers. Key words polymorphism; genotype; lung cancer; cytochrome P450；glutathione S-transferase Abbreviations: SCC, squamous cell carcinoma; AC, adenocarcinoma; SCLC, small cell lung cancer; LCLC, large cell lung cance

Zc(3900)Z_c(3900) as a DDˉ∗D\bar{D}^* molecule from the pole counting rule

A comprehensive study on the nature of the $Z_c(3900)$ resonant structure is carried out in this work. By constructing the pertinent effective Lagrangians and considering the important final-state-interaction effects, we first give a unified description to all the relevant experimental data available, including the $J/\psi\pi$ and $\pi\pi$ invariant mass distributions from the $e^+e^-\to J/\psi\pi\pi$ process, the $h_c\pi$ distribution from $e^+e^-\to h_c\pi\pi$ and also the $D\bar D^{*}$ spectrum in the $e^+e^-\to D\bar D^{*}\pi$ process. After fitting the unknown parameters to the previous data, we search the pole in the complex energy plane and find only one pole in the nearby energy region in different Riemann sheets. Therefore we conclude that $Z_c(3900)$ is of $D\bar D^*$ molecular nature, according to the pole counting rule method~[Nucl.~Phys.~A543, 632 (1992); Phys.~Rev.~D 35,~1633 (1987)]. We emphasize that the conclusion based upon the pole counting method is not trivial, since both the $D\bar D^{*}$ contact interactions and the explicit $Z_c$ exchanges are introduced in our analyses and they lead to the same conclusion.Comment: 21 pages, 9 figures. To match the published version in PRD. Additional discussion on the spectral density function is include

The Basic Helix-Loop-Helix Transcription Factor Family in the Honey Bee, Apis mellifera

The basic helix-loop-helix (bHLH) transcription factors play important roles in a wide range of developmental processes in higher organisms. bHLH family members have been identified in a dozen of organisms including fruit fly, mouse and human. In this study, we identified 51 bHLH sequences in silico in the honey bee, Apis mellifera L. (Hymenoptera: Apidae), genome. Phylogenetic analyses revealed that they belong to 38 bHLH families with 21, 11, 9, 1, 8 and 1 members in high-order groups A, B, C, D, E and F, respectively. Using phylogenetic analyses, all of the 51 bHLH sequences were assigned to their corresponding families. Genes that encode ASCb, NeuroD, Oligo, Delilah, MyoRb, Figa and Mad were not found in the honey bee genome. The present study provides useful background information for future studies using the honey bee as a model system for insect development

A regularized deep matrix factorized model of matrix completion for image restoration

It has been an important approach of using matrix completion to perform image restoration. Most previous works on matrix completion focus on the low-rank property by imposing explicit constraints on the recovered matrix, such as the constraint of the nuclear norm or limiting the dimension of the matrix factorization component. Recently, theoretical works suggest that deep linear neural network has an implicit bias towards low rank on matrix completion. However, low rank is not adequate to reflect the intrinsic characteristics of a natural image. Thus, algorithms with only the constraint of low rank are insufficient to perform image restoration well. In this work, we propose a Regularized Deep Matrix Factorized (RDMF) model for image restoration, which utilizes the implicit bias of the low rank of deep neural networks and the explicit bias of total variation. We demonstrate the effectiveness of our RDMF model with extensive experiments, in which our method surpasses the state of art models in common examples, especially for the restoration from very few observations. Our work sheds light on a more general framework for solving other inverse problems by combining the implicit bias of deep learning with explicit regularization