Impact of hypertensive disorders of pregnancy on maternal and neonatal outcomes of twin gestation: a systematic review and meta-analysis

BackgroundThe impact of hypertensive disorders of pregnancy (HDP) on outcomes of twin gestations is not clear. We aimed to collate data via this meta-analysis to examine how HDP alters maternal and neonatal outcomes of twin gestations.MethodsStudies comparing pregnancy outcomes of twin gestations based on HDP and published on the databases of PubMed, CENTRAL, Scopus, Web of Science, and Embase between 1 January 2000 to 20 March 2023 were eligible for inclusion.ResultsTwelve studies were included. A cumulative of 355,129 twin gestations were analyzed in the current meta-analysis. The pooled analysis found that the presence of HDP increases the risk of preterm birth (OR: 1.86 95% CI: 1.36, 2.55 I2 = 99%) and cesarean section in twin gestations (OR: 1.36 95% CI: 1.20, 1.54 I2 = 89%). Meta-analysis showed a significantly increased risk of low birth weight (OR: 1.30 95% CI: 1.10, 1.55 I2 = 97%), small for gestational age (OR: 1.30 95% CI: 1.09, 1.55 I2 = 96%) and neonatal intensive care unit admissions (OR: 1.77 95% CI: 1.43, 2.20 I2 = 76%) with HDP in twin gestations. There was no difference in the incidence of 5-min Apgar scores <7 (OR: 1.07 95% CI: 0.87, 1.38 I2 = 79%) but a lower risk of neonatal death (OR: 0.39 95% CI: 0.25, 0.61 I2 = 62%) with HDP.ConclusionHDP increases the risk of preterm birth, cesarean sections, low birth weight, SGA, and NICU admission in twin gestations. Contrastingly, the risk of neonatal death is reduced with HDP. Further studies are needed to corroborate the current results.Systematic Review RegistrationPROSPERO (CRD42023407725)

Effects of biochar amendment and organic fertilizer on microbial communities in the rhizosphere soil of wheat in Yellow River Delta saline-alkaline soil

The biochar and organic fertilizer amendment have been used as an effective practice to increase soil fertility. Nevertheless, the mechanisms of microbial community response to organic fertilizer and biochar application on saline-alkali soil have not been clarified. This study investigated the effects at different concentrations of organic fertilizer and biochar on the microbial community of wheat rhizosphere soil under field experiment in the Yellow River Delta (China, YRD), using high-throughput sequencing technology. Biochar and organic fertilizer significantly influenced in most soil parameters (p < 0.05), apart from soil moisture content (M), pH, total nitrogen (TN) and soil total phosphorus (TP). Proteobacteria and Actinobacteriota were found in the rhizosphere soil as the main bacterial phyla, and the main fungal phyla were Ascomycota and Mortierellomycota. The soil bacterial and fungal communities under organic fertilizer were distinct from CK. Furthermore, redundancy analysis (RDA) directed that changes in bacterial communities were related to soil properties like pH, available phosphorus (AP), and total organic carbon (TOC), while pH, AP and TP, were crucial contributors in regulating fungal distribution. The correlation between soil parameters and bacteria or fungi varied with the application of biochar and organic fertilizers, and the interaction between the bacteria and fungi in organic fertilizer treatments formed more connections compared with biochar treatments. Our results indicated that biochar was superior to organic fertilizer under the contents set up in this study, and soil parameters increased with biochar and organic fertilizer application rate. The diversity and structure of soil bacteria and fungi differed with the application of biochar and organic fertilizer. The research provides a reference to rational application of organic fertilizer and biochar improvement in saline-alkali soil

In vitro expression and analysis of the 826 human G protein-coupled receptors

ABSTRACT G protein-coupled receptors (GPCRs) are involved in all human physiological systems where they are responsible for transducing extracellular signals into cells. GPCRs signal in response to a diverse array of stimuli including light, hormones, and lipids, where these signals affect downstream cascades to impact both health and disease states. Yet, despite their importance as therapeutic targets, detailed molecular structures of only 30 GPCRs have been determined to date. A key challenge to their structure determination is adequate protein expression. Here we report the quantification of protein expression in an insect cell expression system for all 826 human GPCRs using two different fusion constructs. Expression characteristics are analyzed in aggregate and among each of the five distinct subfamilies. These data can be used to identify trends related to GPCR expression between different fusion constructs and between different GPCR families, and to prioritize lead candidates for future structure determination feasibility

BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network

In recent years, it has been increasingly clear that long noncoding RNAs (lncRNAs) play critical roles in many biological processes associated with human diseases. Inferring potential lncRNA-disease associations is essential to reveal the secrets behind diseases, develop novel drugs, and optimize personalized treatments. However, biological experiments to validate lncRNA-disease associations are very time-consuming and costly. Thus, it is critical to develop effective computational models. In this study, we have proposed a method called BPLLDA to predict lncRNA-disease associations based on paths of fixed lengths in a heterogeneous lncRNA-disease association network. Specifically, BPLLDA first constructs a heterogeneous lncRNA-disease network by integrating the lncRNA-disease association network, the lncRNA functional similarity network, and the disease semantic similarity network. It then infers the probability of an lncRNA-disease association based on paths connecting them and their lengths in the network. Compared to existing methods, BPLLDA has a few advantages, including not demanding negative samples and the ability to predict associations related to novel lncRNAs or novel diseases. BPLLDA was applied to a canonical lncRNA-disease association database called LncRNADisease, together with two popular methods LRLSLDA and GrwLDA. The leave-one-out cross-validation areas under the receiver operating characteristic curve of BPLLDA are 0.87117, 0.82403, and 0.78528, respectively, for predicting overall associations, associations related to novel lncRNAs, and associations related to novel diseases, higher than those of the two compared methods. In addition, cervical cancer, glioma, and non-small-cell lung cancer were selected as case studies, for which the predicted top five lncRNA-disease associations were verified by recently published literature. In summary, BPLLDA exhibits good performances in predicting novel lncRNA-disease associations and associations related to novel lncRNAs and diseases. It may contribute to the understanding of lncRNA-associated diseases like certain cancers

Water Quality Data Measurement and Analysis System Equipped in Underwater Navigation Robot

Abstract: This paper introduces a water quality data measurement and analysis system equipped in underwater robot. The system consists of sonar navigation module, inertial navigation module equipped in underwater robot, data processing module and sensor detection module. Sonar navigation module and inertial navigation module equipped in underwater robot are separately used to detect current speed, azimuth and depth in different latitudes and longitudes. And then we strike differences of the measured data from two modules above, the difference will be feedback to the inertial navigation module after the filtering process to obtain a more precise location information. Sensor detection module uses the water quality sensors equipped in underwater robot to measure water quality parameters of the current location. Underwater robot data processing module matches the water quality data of the current position to its tracking latitude and longitude, forming water quality of the latitude and longitude distribution data. Underwater robot float to water surface, transmit water quality data to the shore-based facility micro-processor before moving on to the next testing spot. Shore-based facility has its own computer, our self-developed Density Map software is embedded in it. Water quality data from different testing point can be visually displayed. The feasibility of the system design and the rationality of the water quality parameter measurement results were verified by experiments and on-site tests. Copyright © 2013 IFSA