Association of solute carrier family 30 A8 zinc transporter gene variations with gestational diabetes mellitus risk in a Chinese population

BackgroundThe solute carrier family 30 A8 zinc transporter (SLC30A8) plays a crucial role in insulin secretion. This study aimed to investigate the impact of SLC30A8 gene polymorphisms on gestational diabetes mellitus (GDM).MethodsThe research objective was to select 500 patients with GDM and 502 control subjects. Rs13266634 and rs2466293 were genotyped using the SNPscan™ genotyping assay. Statistical tests, such as the chi-square test, t-test, logistic regression, ANOVA, and meta-analysis, were conducted to determine the differences in genotypes, alleles, and their associations with GDM risk.ResultsStatistically significant differences were observed in age, pregestational BMI, SBP, DBP, and parity between individuals with GDM and healthy subjects (P < 0.05). After adjusting for these factors, rs2466293 remained significantly associated with an increased risk of GDM in overall subjects (GG+AG vs. AA: OR = 1.310; 95% CI: 1.005-1.707; P = 0.046, GG vs. AA: OR = 1.523; 95% CI: 1.010-2.298; P = 0.045 and G vs. A: OR = 1.249; 95% CI: 1.029-1.516; P = 0.024). Rs13266634 was still found to be significantly associated with a decreased risk of GDM in individuals aged ≥ 30 years (TT vs. CT+CC: OR = 0.615; 95% CI: 0.392-0.966; P = 0.035, TT vs. CC: OR = 0.503; 95% CI: 0.294-0.861; P = 0.012 and T vs. C: OR =0.723; 95% CI: 0.557-0.937; P = 0.014). Additionally, the haplotype CG was found to be associated with a higher risk of GDM (P < 0.05). Furthermore, pregnant women with the CC or CT genotype of rs13266634 exhibited significantly higher mean blood glucose levels than those with the TT genotype (P < 0.05). Our findings were further validated by the results of a meta-analysis.ConclusionThe SLC30A8 rs2466293 polymorphism was found to be associated with an increased risk of GDM, while rs13266634 was associated with a decreased risk of GDM in individuals aged ≥ 30 years. These findings provide a theoretical basis for GDM testing

The SARS-Unique Domain (SUD) of SARS Coronavirus Contains Two Macrodomains That Bind G-Quadruplexes

Since the outbreak of severe acute respiratory syndrome (SARS) in 2003, the three-dimensional structures of several of the replicase/transcriptase components of SARS coronavirus (SARS-CoV), the non-structural proteins (Nsps), have been determined. However, within the large Nsp3 (1922 amino-acid residues), the structure and function of the so-called SARS-unique domain (SUD) have remained elusive. SUD occurs only in SARS-CoV and the highly related viruses found in certain bats, but is absent from all other coronaviruses. Therefore, it has been speculated that it may be involved in the extreme pathogenicity of SARS-CoV, compared to other coronaviruses, most of which cause only mild infections in humans. In order to help elucidate the function of the SUD, we have determined crystal structures of fragment 389–652 (“SUDcore”) of Nsp3, which comprises 264 of the 338 residues of the domain. Both the monoclinic and triclinic crystal forms (2.2 and 2.8 Å resolution, respectively) revealed that SUDcore forms a homodimer. Each monomer consists of two subdomains, SUD-N and SUD-M, with a macrodomain fold similar to the SARS-CoV X-domain. However, in contrast to the latter, SUD fails to bind ADP-ribose, as determined by zone-interference gel electrophoresis. Instead, the entire SUDcore as well as its individual subdomains interact with oligonucleotides known to form G-quadruplexes. This includes oligodeoxy- as well as oligoribonucleotides. Mutations of selected lysine residues on the surface of the SUD-N subdomain lead to reduction of G-quadruplex binding, whereas mutations in the SUD-M subdomain abolish it. As there is no evidence for Nsp3 entering the nucleus of the host cell, the SARS-CoV genomic RNA or host-cell mRNA containing long G-stretches may be targets of SUD. The SARS-CoV genome is devoid of G-stretches longer than 5–6 nucleotides, but more extended G-stretches are found in the 3′-nontranslated regions of mRNAs coding for certain host-cell proteins involved in apoptosis or signal transduction, and have been shown to bind to SUD in vitro. Therefore, SUD may be involved in controlling the host cell's response to the viral infection. Possible interference with poly(ADP-ribose) polymerase-like domains is also discussed

Improved tensile strength of carbon nanotube reinforced aluminum composites processed by powder metallurgy

Carbon nanotube (CNT) reinforced aluminum (Al) composites were synthesized using the powder metallurgy (P/M) technique, combined with hot extrusion and hot rolling. 0-2.0wt.% of CNTs were added as reinforcements. The effect of CNTs on the mechanical properties of Al was investigated and a significant enhancement in tensile strength was obtained compared with the pure matrix. The improved strength was analyzed based on (i) Orowan strengthening, (ii) thermal mismatch between CNTs and matrix, and (iii) load partition effect due to the CNTs

Influence of the Steel Fiber Content on the Flexural Fatigue Behavior of Recycled Aggregate Concrete

Steel fiber recycled aggregate concrete (SFRAC) is mainly used in roads, bridges, and railways that are subjected to bear wheel load. This paper presents a comparative experimental study on the flexural fatigue behavior of the SFRAC, the natural aggregate concrete (NAC), and the recycled aggregate concrete (RAC). The results show that, with the use of 1.0% volume fraction steel fiber, the flexural strength of SFRAC exceeds the flexural strength of NAC (around 0.3%), and the fatigue lives of RAC have been found to be lower by 19.9% and 53.4% compared to SFRAC at stress levels S = 0.9 and S = 0.7. The fatigue strain of SFRAC follows the three-stage law, and the fatigue strain of SFRAC develops more slowly than that of RAC at the same stress level. Two-parameter Weibull distribution is fitted to the test data to generate fatigue models at different survival probabilities, and fatigue life can be accurately predicted using the developed model. Therefore, it is feasible to replace the natural concrete with the recycled aggregate concrete with appropriate steel fiber content in some aspects, which is of great significance to green development

A Two-Stage Framework for Directed Hypergraph Link Prediction

Hypergraphs, as a special type of graph, can be leveraged to better model relationships among multiple entities. In this article, we focus on the task of hyperlink prediction in directed hypergraphs, which finds a wide spectrum of applications in knowledge graphs, chem-informatics, bio-informatics, etc. Existing methods handling the task overlook the order constraints of the hyperlink’s direction and fail to exploit features of all entities covered by a hyperlink. To make up for the deficiency, we present a performant pipelined model, i.e., a two-stage framework for directed hyperlink prediction method (TF-DHP), which equally considers the entity’s contribution to the form of hyperlinks, and emphasizes not only the fixed order between two parts but also the randomness inside each part. The TF-DHP incorporates two tailored modules: a Tucker decomposition-based module for hyperlink prediction, and a BiLSTM-based module for direction inference. Extensive experiments on benchmarks—WikiPeople, JF17K, and ReVerb15K—demonstrate the effectiveness and universality of our TF-DHP model, leading to state-of-the-art performance

Hyperbolic Directed Hypergraph-Based Reasoning for Multi-Hop KBQA

The target of the multi-hop knowledge base question-answering task is to find answers of some factoid questions by reasoning across multiple knowledge triples in the knowledge base. Most of the existing methods for multi-hop knowledge base question answering based on a general knowledge graph ignore the semantic relationship between each hop. However, modeling the knowledge base as a directed hypergraph has the problems of sparse incidence matrices and asymmetric Laplacian matrices. To make up for the deficiency, we propose a directed hypergraph convolutional network modeled on hyperbolic space, which can better deal with the sparse structure, and effectively adapt to the problem of an asymmetric incidence matrix of directed hypergraphs modeled on a knowledge base. We propose an interpretable KBQA model based on the hyperbolic directed hypergraph convolutional neural network named HDH-GCN which can update relation semantic information hop-by-hop and pays attention to different relations at different hops. The model can improve the accuracy of the multi-hop knowledge base question-answering task, and has application value in text question answering, human–computer interactions and other fields. Extensive experiments on benchmarks—PQL, MetaQA—demonstrate the effectiveness and universality of our HDH-GCN model, leading to state-of-the-art performance

A G-quadruplex-binding macrodomain within the >SARS-unique domain> is essential for the activity of the SARS-coronavirus replication-transcription complex

The multi-domain non-structural protein 3 of SARS-coronavirus is a component of the viral replication/transcription complex (RTC). Among other domains, it contains three sequentially arranged macrodomains: the X domain and subdomains SUD-N as well as SUD-M within the >SARS-unique domain>. The X domain was proposed to be an ADP-ribose-1>-phosphatase or a poly(ADP-ribose)-binding protein, whereas SUD-NM binds oligo(G)-nucleotides capable of forming G-quadruplexes. Here, we describe the application of a reverse genetic approach to assess the importance of these macrodomains for the activity of the SARS-CoV RTC. To this end, Renilla luciferase-encoding SARS-CoV replicons with selectively deleted macrodomains were constructed and their ability to modulate the RTC activity was examined. While the SUD-N and the X domains were found to be dispensable, the SUD-M domain was crucial for viral genome replication/transcription. Moreover, alanine replacement of charged amino-acid residues of the SUD-M domain, which are likely involved in G-quadruplex-binding, caused abrogation of RTC activity.This work was supported by the German Center for Infection Research (DZIF), the European Commission (through its projects SILVER (Contract no. 260644) and EMPERIE (Contract no. 223498)), the Ministry of Science and Innovation of Spain (BIO2010-16705), the U.S. National Institutes of Health (Grant nos. 2P01AI060699-06A1 and CRIP-HHSN266200700010C), and the Sino-German Center for the Promotion of Research, Beijing, China (GZ 236 (202/9))

DataSheet1_A novel near-infrared fluorescent probe for rapid sensing of HClO in living cells and zebrafish.docx

Reactive oxygen species (ROS) are significant active species in living organisms, and their coordination maintains the function of organelles to resist the invasion of foreign substances. Hypochlorous acid (HClO) is not only an eventful signaling species but also a kind of ROS, which plays an irreplaceable role in the immune system. However, its abnormal levels can cause cell damage or even apoptosis, which in turn leads to the onset of a series of diseases such as inflammation, neurological diseases, and even cancer. Based on this, we designed a near-infrared fluorescent probe with a large Stokes shift for ultrafast response to HClO. Furthermore, the probe exhibits excellent sensitivity and selectivity toward HClO over other species. The probe was successfully applied to visualize endogenous and exogenous HClO in living cells and in zebrafish. This unique study is the key to providing a trustworthy tool for imaging based on the in vitro and in vivo imaging of endogenous HClO, which possesses great potential for the use in future studies of HClO-related biology and pathology.</p

An Adaptive Hybrid Algorithm for Global Network Alignment

It is challenging to obtain reliable and optimal mapping between networks for alignment algorithms when both nodal and topological structures are taken into consideration due to the underlying NP-hard problem. Here, we introduce an adaptive hybrid algorithm that combines the classical Hungarian algorithm and the Greedy algorithm (HGA) for the global alignment of biomolecular networks. With this hybrid algorithm, every pair of nodes with one in each network is first aligned based on node information (e.g., their sequence attributes) and then followed by an adaptive and convergent iteration procedure for aligning the topological connections in the networks. For four well-studied protein interaction networks, i.e., C.elegans, yeast, D.melanogaster and human, applications of HGA lead to improved alignments in acceptable running time. The mapping between yeast and human PINs obtained by the new algorithm has the largest value of common Gene Ontology (GO) terms compared to those obtained by other existing algorithms, while it still has lower Mean normalized entropy (MNE) and good performances on several other measures. Overall, the adaptive HGA is effective and capable of providing good mappings between aligned networks in which the biological properties of both the nodes and the connections are important

Structure-based discovery of antivirals targeting the proteases of RNA viruses

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