Electrical and optical properties of fluid iron from compressed to expanded regime

Using quantum molecular dynamics simulations, we show that the electrical and optical properties of fluid iron change drastically from compressed to expanded regime. The simulation results reproduce the main trends of the electrical resistivity along isochores and are found to be in good agreement with experimental data. The transition of expanded fluid iron into a nonmetallic state takes place close to the density at which the constant volume derivative of the electrical resistivity on internal energy becomes negative. The study of the optical conductivity, absorption coefficient, and Rosseland mean opacity shows that, quantum molecular dynamics combined with the Kubo-Greenwood formulation provides a powerful tool to calculate and benchmark the electrical and optical properties of iron from expanded fluid to warm dense region

MeDBA: the Metalloenzyme Data Bank and Analysis platform

Metalloenzymes are attractive research targets in fields of chemistry, biology, and medicine. Given that metalloenzymes can manifest conservation of metal-coordination and ligand binding modes, the excavation and expansion of metalloenzyme-specific knowledge is of interest in bridging metalloenzyme-related fields. Building on our previous metalloenzyme-ligand association database, MeLAD, we have expanded the scope of metalloenzyme-specific knowledge and services, by forming a versatile platform, termed the Metalloenzyme Data Bank and Analysis (MeDBA). The MeDBA provides: (i) manual curation of metalloenzymes into different categories, that this M-I, M-II and M-III; (ii) comprehensive information on metalloenzyme activities, expression profiles, family and disease links; (iii) structural information on metalloenzymes, in particular metal binding modes; (iv) metalloenzyme substrates and bioactive molecules acting on metalloenzymes; (v) excavated metal-binding pharmacophores and (vi) analysis tools for structure/metal active site comparison and metalloenzyme profiling. The MeDBA is freely available at https://medba.ddtmlab.org

Next-Generation Sequencing Data-Based Association Testing of a Group of Genetic Markers for Complex Responses Using a Generalized Linear Model Framework

To study the relationship between genetic variants and phenotypes, association testing is adopted; however, most association studies are conducted by genotype-based testing. Testing methods based on next-generation sequencing (NGS) data without genotype calling demonstrate an advantage over testing methods based on genotypes in the scenarios when genotype estimation is not accurate. Our objective was to develop NGS data-based methods for association studies to fill the gap in the literature. Single-variant testing methods based on NGS data have been proposed, including our previously proposed single-variant NGS data-based testing method, i.e., UNC combo method. The NGS data-based group testing method has been proposed by us using a linear model framework which can handle continuous responses. In this paper, we extend our linear model-based framework to a generalized linear model-based framework so that the methods can handle other types of responses especially binary responses which is a common problem in association studies. To evaluate the performance of various estimators and compare them we performed simulation studies. We found that all methods have Type I errors controlled, and our NGS data-based methods have better performance than genotype-based methods for other types of responses, including binary responses (logistics regression) and count responses (Poisson regression), especially when sequencing depth is low. We have extended our previous linear model (LM) framework to a generalized linear model (GLM) framework and derived NGS data-based methods for a group of genetic variables. Compared with our previously proposed LM-based methods, the new GLM-based methods can handle more complex responses (for example, binary responses and count responses) in addition to continuous responses. Our methods have filled the literature gap and shown advantage over their corresponding genotype-based methods in the literature

HIF-1α Contributes to Hypoxia-induced Invasion and Metastasis of Esophageal Carcinoma via Inhibiting E-cadherin and Promoting MMP-2 Expression

Hypoxia-inducible factor-1α (HIF-1α) has been found to enhance tumor invasion and metastasis, but no study has reported its action in esophageal carcinoma. The goal of this study was to explore the probable mechanism of HIF-1α in the invasion and metastasis of esophageal carcinoma Eca109 cells in vitro and in vivo. mRNA and protein expression of HIF-1α, E-cadherin and matrix metalloproteinase-2 (MMP-2) under hypoxia were detected by RT-PCR and Western blotting. The effects of silencing HIF-1α on E-cadherin, MMP-2 mRNA and protein expression under hypoxia or normoxia were detected by RT-PCR and Western blotting, respectively. The invasive ability of Eca109 cells was tested using a transwell chambers. We established an Eca109-implanted tumor model and observed tumor growth and lymph node metastasis. The expression of HIF-1α, E-cadherin and MMP-2 in xenograft tumors was detected by Western blotting. After exposure to hypoxia, HIF-1α protein was up-regulated, both mRNA and protein levels of E-cadherin were down-regulated and MMP-2 was up-regulated, while HIF-1α mRNA showed no significant change. SiRNA could block HIF-1α effectively, increase E-cadherin expression and inhibit MMP-2 expression. The number of invading cells decreased after HIF-1α was silenced. Meanwhile, the tumor volume was much smaller, and the metastatic rate of lymph nodes and the positive rate were lower in vivo. Our observations suggest that HIF-1α inhibition might be an effective strategy to weaken invasion and metastasis in the esophageal carcinoma Eca109 cell line

4-(1H-Tetra­zol-5-yl)benzoic acid monohydrate

The asymmetric unit of the title compound, C8H6N4O2·H2O, consists of one 4-(1H-tetra­zol-5-yl)benzoic acid mol­ecule and one water mol­ecule. Hydrogen-bonding and π–π stacking (centroid–centroid distance between tetra­zole and benzene rings = 3.78 Å) inter­actions link the mol­ecules into a three-dimensional network

Bis[2-((4,6-dimethyl­pyrimidin-2-yl){2-[(4,6-dimethyl­pyrimidin-2-yl)sulfan­yl]eth­yl}amino)­eth­yl] disulfide

Bis[2-(4,6-dimethyl­pyrimidin-2-ylsulfan­yl)eth­yl]amine under hydro­thermal conditions has unexpectedly been transformed into the title compound, C32H44N10S4. In the title mol­ecule, the zigzag 3,10-diaza-6,7-disulfanyldodecyl skeleton has two dimethyl­pyrimidinylsulfanyl groups at both ends, and the aza atoms each carry a dimethyl­pyrimidinyl unit. The N atoms in the skeleton show a planar coordination

A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images

Background: Osteoporosis is a common metabolic skeletal disease and usually lacks obvious symptoms. Many individuals are not diagnosed until osteoporotic fractures occur. Bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis detection. However, only a limited percentage of people with osteoporosis risks undergo the DXA test. As a result, it is vital to develop methods to identify individuals at-risk based on methods other than DXA. Results: We proposed a hierarchical model with three layers to detect osteoporosis using clinical data (including demographic characteristics and routine laboratory tests data) and CT images covering lumbar vertebral bodies rather than DXA data via machine learning. 2210 individuals over age 40 were collected retrospectively, among which 246 individuals’ clinical data and CT images are both available. Irrelevant and redundant features were removed via statistical analysis. Consequently, 28 features, including 16 clinical data and 12 texture features demonstrated statistically significant differences (p < 0.05) between osteoporosis and normal groups. Six machine learning algorithms including logistic regression (LR), support vector machine with radial-basis function kernel, artificial neural network, random forests, eXtreme Gradient Boosting and Stacking that combined the above five classifiers were employed as classifiers to assess the performances of the model. Furthermore, to diminish the influence of data partitioning, the dataset was randomly split into training and test set with stratified sampling repeated five times. The results demonstrated that the hierarchical model based on LR showed better performances with an area under the receiver operating characteristic curve of 0.818, 0.838, and 0.962 for three layers, respectively in distinguishing individuals with osteoporosis and normal BMD. Conclusions: The proposed model showed great potential in opportunistic screening for osteoporosis without additional expense. It is hoped that this model could serve to detect osteoporosis as early as possible and thereby prevent serious complications of osteoporosis, such as osteoporosis fractures

Analysis of genetic and nongenetic factors influencing triglycerides-lowering drug effects based on paired observations

Abstract Obesity is a risk factor for heart disease, stroke, diabetes, high blood pressure, and other chronic diseases. Some drugs, including fenofibrate, are used to treat obesity or excessive weight by lowering the level of specific triglycerides. However, different groups have different drug sensitivities and, consequently, there are differences in drug effects. In this study, we assessed both genetic and nongenetic factors that influence drug responses and stratified patients into groups based on differential drug effect and sensitivity. Our methodology of investigating genetic factors and nongenetic factors is applicable to studying differential effects of other drugs, such as statins, and provides an approach to the development of personalized medicine

Mesenchymal stromal cells-derived small extracellular vesicles modulate DC function to suppress Th2 responses via IL-10 in patients with allergic rhinitis

Mesenchymal stromal cells (MSCs) are well known for their immunoregulatory roles on allergic inflammation particularly by acting on T cells, B cells, and dendritic cells (DCs). MSC-derived small extracellular vesicles (MSC-sEV) are increasingly considered as one of the main factors for the effects of MSCs on immune responses. However, the effects of MSC-sEV on DCs in allergic diseases remain unclear. MSC-sEV were prepared from the induced pluripotent stem cells (iPSC)-MSCs by anion-exchange chromatography, and were characterized with the size, morphology, and specific markers. Human monocyte-derived DCs were generated and cultured in the presence of MSC-sEV to differentiate the so-called sEV-immature DCs (sEV-iDCs) and sEV-mature DCs (sEV-mDCs), respectively. The phenotypes and the phagocytic ability of sEV-iDCs were analyzed by flow cytometry. sEV-mDCs were co-cultured with isolated CD4+ T cells or peripheral blood mononuclear cells (PBMCs) from patients with allergic rhinitis. The levels of Th1 and Th2 cytokines produced by T cells were examined by ELISA and intracellular flow staining. And the following mechanisms were further investigated. We demonstrated that MSC-sEV inhibited the differentiation of human monocytes to iDCs with downregulation of the expression of CD40, CD80, CD86, and HLA-DR, but had no effects on mDCs with these markers. However, MSC-sEV treatment enhanced the phagocytic ability of mDCs. More importantly, using anti-IL-10 monoclonal antibody or IL-10Rα blocking antibody, we identified that sEV-mDCs suppressed the Th2 immune response by reducing the production of IL-4, IL-9, and IL-13 via IL-10. Furthermore, sEV-mDCs increased the level of Treg cells. Our study identified that mDCs treated with MSC-sEV inhibited the Th2 responses, providing novel evidence of the potential cell-free therapy acting on DCs in allergic airway diseases. Keywords: Allergic rhinitis; Dendritic cells; Interleukin-10; Mesenchymal stromal cells; Small extracellular vesicles