Global burden and trends of ectopic pregnancy: An observational trend study from 1990 to 2019.

BACKGROUND: Ectopic pregnancy (EP) is one of the leading causes of death in women in early pregnancy, and the mortality of EP have gradually decreased over time in developed countries such as the United Kingdom and the United States. However, epidemiological information on EP has been lacking in recent years, so we analyzed EP data over a thirty-year period from 1990-2019 with the help of Global Burden of Disease study (GBD) data to fill this gap. METHODS: According to the EP data in GBD for the three decades from 1990 to 2019, we used estimated annual percentage changes (EAPC) to assess the trend of age-standardized incidence rate (ASIR), age-standardized death rate (ASDR) and age-standardized disability adjusted life years (AS-DALYs) trends in EP and to explore the correlation between socio-demographic index (SDI) stratification, age stratification and EP. RESULTS: Global ASIR, ASDR, AS-DALYs for EP in 2019 are 170.33/100,000 persons (95% UI: 133.18 to 218.49), 0.16/100,000 persons (95% UI, 0.14 to 0.19) and 9.69/100,000 persons (95% UI, 8.27 to 11.31), respectively. At the overall level, ASDR is significantly negatively correlated with SDI values (R = -0.699, p < 0.001). Besides that, ASDR and AS-DALYs have basically the same pattern. In addition, iron deficiency is one of the risk factors for EP. CONCLUSIONS: In the past three decades, the morbidity, mortality and disease burden of EP have gradually decreased. It is noteworthy that some economically disadvantaged areas are still experiencing an increase in all indicators, therefore, it is more important to strengthen the protection of women from ethnic minorities and low-income groups.

Spatial-temporal dynamics, ecological risk assessment, source identification and interactions with internal nutrients release of heavy metals in surface sediments from a large Chinese shallow lake.

The surface sediment concentrations of heavy metals (Cu, Zn, Pb, Cd, Cr, Hg, and As), major metals (Fe and Mn), and the nutrient concentrations in the interstitial water of Lake Houguan, a large eutrophic shallow lake, were surveyed for three years. The results showed that Cu, Zn, and Fe were significantly higher in the east lake parts, and Cd in November was significantly higher than April. 19% of Hg and all of As were larger than the probable effect concentrations (PECs) according to the consensus-based sediment quality guidelines (SQGs), and the geo-accumulation index (Igeo) indicated As, Hg, and Cd were slightly polluted to severely polluted. The RI value (average 704.2) of the potential ecological risk index (PERI) suggested that heavy metals posed very high ecological risks with most of the contributions induced by Cd and Hg. The consequence of hierarchical clustering analysis (HCA) and principle component analysis (PCA) identified Cd, As, and Pb might originate from urbanization, industrial pollution, and agricultural activity; Hg might be from atmospheric deposition and anthropogenic sources above; Cu, Zn, Cr, Fe, and Mn might be from both natural and anthropogenic sources. The Spearman correlation analysis indicated Pb and As were significantly positively correlated with total nitrogen, while Cd significantly negatively correlated with sulfate; As was significantly correlated with ammonia, sulfate, and nitrate in the interstitial water. These results suggested eutrophication might affect sedimental heavy metals by increasing organic matter or influencing the redox potentials in the sediment.

Assessment of ADCY9 polymorphisms and colorectal cancer risk in the Chinese Han population.

BACKGROUND: Recently, ADCY9 has been found to be highly expressed in colon cancer, and high ADCY9 expressionis a poor prognostic factor of colon cancer. However, no study has reported on the relationship between single nucleotide polymorphisms (SNPs) of ADCY9 and colorectal cancer risk in the Chinese Han population. METHODS: To evaluate the association between four ADCY9 SNPs and colorectal cancer risk, we performed a case-control study including 511 colorectal cancer patients and 511 healthy controls. SNPs were genotyped using the Agena MassARRAY platform (Agena Bioscience, San Diego, CA, USA). The distributions of alleles and genotypes frequencies between the case and control groups were compared using chi-squared. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by logistic regression adjusted for age and gender to assess the association between SNPs and colorectal cancer risk. RESULTS: The overall analysis found that rs2230742 was associated with an increased risk of colorectal cancer (AA versus GG: OR = 3.54, 95% CI = 1.16-10.86, p = 0.027; recessive model: OR = 3.55, 95% CI = 1.16-10.85, p = 0.027). Stratification analysis showed that rs2230742 was associated with an increased rectal cancer risk; rs11076810 was associated with a reduced colorectal cancer risk for age > 59 years. No association was observed between other two SNPs and colorectal cancer risk. CONCLUSIONS: Our findings suggest that ADCY9 polymorphisms (rs2230742 and rs11076810) have an effect on colorectal cancer risk in the Chinese Han population. Future association and functional studies are required to confirm our findings and explore the mechanism of ADCY2 in colorectal cancer.

Assessment and management of nonpoint source pollution based on multicriteria analysis.

Assessing the potential impacts of nonpoint source (NPS) pollution and proposing sound control strategies are significant global challenges. However, few studies have provided insights into the quantitative risk analysis and efficient management of NPS pollution. This study formulated four evaluation criteria to characterize both the generation and migration of diffuse phosphorus. Multicriteria analysis and the technique for order preference by similarity to ideal solution were combined to develop a NPS pollution risk index model for the evaluation of the potential phosphorus loss at the basin scale. The proposed model is a simple and efficient tool that considers most factors that affect diffuse phosphorus. In the GIS environment, the spatial distribution of the risk index of diffuse phosphorus could be mapped and visualized in the Huai River Basin. With the natural breaks classification method, the study area was divided into the following five regions: a potentially polluted region (3.5%), a lightly polluted region (15.4%), a moderately polluted region (40.7%), a highly polluted region (31.5%), and a seriously polluted region (8.9%). Based on land use composition, geographical location, and sources of diffuse phosphorus of these five regions, corresponding prevention measures were introduced, thus facilitating the management of NPS pollution for policy makers.

Assessment of a Novel Standardized Training System for Mandibular Contour Surgeries.

IMPORTANCE: Mandibular contour surgeries (MCS) involving reduction gonioplasty and genioplasty are rewarding for patients with square faces; however, the procedure has inherently difficult clinician learning curves and unpredictable skill acquisitions. To our knowledge, there has been no effective, validated training model that might improve training and surgical outcomes for MCS. OBJECTIVE: To establish and evaluate a standardized intraoral MCS training system. DESIGN, SETTING, AND PARTICIPANTS: Intraoral MCS training models were constructed by 3-dimensional (3D) skull models covered with elastic head cloths. From April 2016 to April 2018, 90 consecutive MCS patients (30 per group) and 15 craniofacial surgery fellow physicians (5 per group) were enrolled in the prospective observational study. They were randomly divided into intervention groups (A and B) and a control group (C). Intervention groups A and B completed 5 training sessions on the intraoral MCS training models before each clinical case. Group A performed both the model training sessions and clinical surgeries with surgical templates. Control group C had no extra training before clinical surgeries. All groups completed clinical surgery under supervision on 6 patients. The duration of follow-up was at least 3 months postoperatively. INTERVENTIONS: Intraoral MCS training models were provided to intervention groups (A and B) before clinical surgeries. Surgical templates were provided to intervention group A both in training sessions and clinical surgeries. MAIN OUTCOMES AND MEASURES: The completion time, surgical accuracy, learning curves, operating confidence, surgical skill, and outcome satisfaction of each procedure were recorded and analyzed with paired t test and 1-way analysis of variance test by blinded observers. RESULTS: All 90 patients (14 men, 76 women; mean [SD] age, 26 [5] years) were satisfied with their postoperative mandible contours. The intervention groups (A and B), especially the group with surgical templates (A) showed improvements in clinical surgery time (mean [SD], group A 147.2 [24.71] min; group B, 184.47 [16.28] min; group C, 219.3 [35.3] min; P = .001), surgical accuracy (mean [SD], group A, 0.68 [0.22] mm; group B, 1.22 [0.38] mm; group C, 1.88 [0.54] mm; P < .001), learning curves, and operators' confidence and surgical skill. CONCLUSIONS AND RELEVANCE: The intraoral MCS training model was effective and practical. The optimal intraoral MCS training system included intraoral MCS training models and surgical templates. The system significantly decreased clinical surgery time, improved surgical accuracy, shortened the learning curve, boosted operators' confidence, and was associated with better acquisition of surgical skills. LEVEL OF EVIDENCE: NA.

A causation-based method developed for an integrated risk assessment of heavy metals in soil.

A comprehensive and fact-based risk assessment of heavy metals in soils is paramount for defining strategies for environmental management. However, the risk assessment approaches of heavy metals in soils are often incomplete, in particular, causation-based pollution source apportionment is absent at present. Here, we developed a causation-based method framework of an integrated risk assessment of soil heavy metals. This method framework involves risk identification, causation-based source apportionment and an environmental sensitivity assessment. Dongtang Township in Guangdong Province, China was used as a case study. We found that air Cd, the background value and metallurgical industries (Danxia and Fankou plants) were identified as the major causes of soil Cd, and air and soil Cd as well as water Cd interacted causally. Danxia and Fankou plants, the mining area and background value were the major causes of soil Pb. The risk level and environmental sensitivity of the Danxia and Fankou plants were assessed. This is the first study to establish a causation-based method framework of an integrated risk assessment of soil heavy metals. This framework promotes systematic integration of risk assessment of soil heavy metals and expands traditional research on pollution source apportionment from a correlation-based approach to crucial insights into causation.

Impact of energy taxation on economy, environmental and public health quality.

This paper argues computable general equilibrium model and assess impact of energy taxation on economy, environmental and public health quality in Tianjin. In order to investigate different energy taxation based on medical cost and labor loss, the computable general equilibrium model integrating with input-output table and social accounting matrix (SAM) was constructed. The medical expense caused by air pollution of Tianjin in 2007 is 396 million yuan and death for 18104 people, which accounted for the total GDP and population 0.754‰ and 1.6‰, respectively. The results show that the enery taxes levy can improve the GDP, but it is only slightly. The energy taxes have adverse impact on energy sector because that the energy cost is increased. The scale of production is reduced, and the capital and labor resources are transferred to low energy consumption low emissions sector. The energy tax levy can reduce air pollutants concentration and improve air environmental quality. The PM10, SO2 and NO2 concentration in the energy taxes 5%-30% was reduced by 0.24%-0.24%, 0.09-0.52% and 0.29%-0.52% respectively. The medical expense has little impact on GDP, but labor loss has a certain effect on GDP. For higher energy taxes rate, the health effects on GDP can reach 0.06%-0.16%. This simultaneous economic and environmental improvement and health effect would thus have positive implications regarding energy taxes of the country.

The metabolic ER stress sensor IRE1α suppresses alternative activation of macrophages and impairs energy expenditure in obesity.

Obesity is associated with metabolic inflammation and endoplasmic reticulum (ER) stress, both of which promote metabolic disease progression. Adipose tissue macrophages (ATMs) are key players orchestrating metabolic inflammation, and ER stress enhances macrophage activation. However, whether ER stress pathways underlie ATM regulation of energy homeostasis remains unclear. Here, we identified inositol-requiring enzyme 1α (IRE1α) as a critical switch governing M1-M2 macrophage polarization and energy balance. Myeloid-specific IRE1α abrogation in Ern1f/f; Lyz2-Cre mice largely reversed high-fat diet (HFD)-induced M1-M2 imbalance in white adipose tissue (WAT) and blocked HFD-induced obesity, insulin resistance, hyperlipidemia and hepatic steatosis. Brown adipose tissue (BAT) activity, WAT browning and energy expenditure were significantly higher in Ern1f/f; Lyz2-Cre mice. Furthermore, IRE1α ablation augmented M2 polarization of macrophages in a cell-autonomous manner. Thus, IRE1α senses protein unfolding and metabolic and immunological states, and consequently guides ATM polarization. The macrophage IRE1α pathway drives obesity and metabolic syndrome through impairing BAT activity and WAT browning.

Genome-wide association study in Chinese Holstein cows reveal two candidate genes for somatic cell score as an indicator for mastitis susceptibility.

BACKGROUNDS: Bovine mastitis is a typical inflammatory disease causing seriously economic loss. Genome-wide association study (GWAS) can be a powerful method to promote marker assistant selection of this kind of complex disease. The present study aimed to analyze and identify single nucleotide polymorphisms (SNPs) and candidate genes that associated with mastitis susceptibility traits in Chinese Holstein. RESULTS: Forty eight SNPs were identified significantly associated with mastitis resistance traits in Chinese Holstein cows, which are mainly located on the BTA 14. A total of 41 significant SNPs were linked to 31 annotated bovine genes. Gene Ontology and pathway enrichment revealed 5 genes involved in 32 pathways, in which, TRAPPC9 and ARHGAP39 genes participate cell differentiation and developmental pathway together. The six common genome-wide significant SNPs are found located within TRAPPC9 and flanking ARHGAP39 genes. CONCLUSIONS: Our data identified the six SNPs significantly associated with SCS EBVs, which suggest that their linked two genes (TRAPPC9 and ARHGAP39) are novel candidate genes of mastitis susceptibility in Holsteins.

[Advances on genome-wide association study for economically important traits in dairy cattle].

Milk performance traits have been considered as the most important production traits. With the development of balance breeding sense, much attention has been paid to the functional traits such as reproduction, type, health, and longevity traits, which is put into breeding programs. Identification of major genes or genetic markers for milk production and functional traits and their applications in breeding programs of dairy cattle are expected to improve the genetic progress of these traits. Occurrence of millions of SNPs in whole genome and high throughout genotyping techniques has made genome-wide association study to be an important strategy to identify genes responsible for economic traits in domestic animals. This paper reviewed the advances on genome-wide association study for milk production traits and functional traits in dairy cattle.

Bayesian mapping of quantitative trait loci for multiple complex traits with the use of variance components.

Complex traits important for humans are often correlated phenotypically and genetically. Joint mapping of quantitative-trait loci (QTLs) for multiple correlated traits plays an important role in unraveling the genetic architecture of complex traits. Compared with single-trait analysis, joint mapping addresses more questions and has advantages for power of QTL detection and precision of parameter estimation. Some statistical methods have been developed to map QTLs underlying multiple traits, most of which are based on maximum-likelihood methods. We develop here a multivariate version of the Bayes methodology for joint mapping of QTLs, using the Markov chain-Monte Carlo (MCMC) algorithm. We adopt a variance-components method to model complex traits in outbred populations (e.g., humans). The method is robust, can deal with an arbitrary number of alleles with arbitrary patterns of gene actions (such as additive and dominant), and allows for multiple phenotype data of various types in the joint analysis (e.g., multiple continuous traits and mixtures of continuous traits and discrete traits). Under a Bayesian framework, parameters--including the number of QTLs--are estimated on the basis of their marginal posterior samples, which are generated through two samplers, the Gibbs sampler and the reversible-jump MCMC. In addition, we calculate the Bayes factor related to each identified QTL, to test coincident linkage versus pleiotropy. The performance of our method is evaluated in simulations with full-sib families. The results show that our proposed Bayesian joint-mapping method performs well for mapping multiple QTLs in situations of either bivariate continuous traits or mixed data types. Compared with the analysis for each trait separately, Bayesian joint mapping improves statistical power, provides stronger evidence of QTL detection, and increases precision in estimation of parameter and QTL position. We also applied the proposed method to a set of real data and detected a coincident linkage responsible for determining bone mineral density and areal bone size of wrist in humans.

Incorporating single-locus tests into haplotype cladistic analysis in case-control studies.

In case-control studies, genetic associations for complex diseases may be probed either with single-locus tests or with haplotype-based tests. Although there are different views on the relative merits and preferences of the two test strategies, haplotype-based analyses are generally believed to be more powerful to detect genes with modest effects. However, a main drawback of haplotype-based association tests is the large number of distinct haplotypes, which increases the degrees of freedom for corresponding test statistics and thus reduces the statistical power. To decrease the degrees of freedom and enhance the efficiency and power of haplotype analysis, we propose an improved haplotype clustering method that is based on the haplotype cladistic analysis developed by Durrant et al. In our method, we attempt to combine the strengths of single-locus analysis and haplotype-based analysis into one single test framework. Novel in our method is that we develop a more informative haplotype similarity measurement by using p-values obtained from single-locus association tests to construct a measure of weight, which to some extent incorporates the information of disease outcomes. The weights are then used in computation of similarity measures to construct distance metrics between haplotype pairs in haplotype cladistic analysis. To assess our proposed new method, we performed simulation analyses to compare the relative performances of (1) conventional haplotype-based analysis using original haplotype, (2) single-locus allele-based analysis, (3) original haplotype cladistic analysis (CLADHC) by Durrant et al., and (4) our weighted haplotype cladistic analysis method, under different scenarios. Our weighted cladistic analysis method shows an increased statistical power and robustness, compared with the methods of haplotype cladistic analysis, single-locus test, and the traditional haplotype-based analyses. The real data analyses also show that our proposed method has practical significance in the human genetics field.

Study on mapping quantitative trait loci for animal complex binary traits using Bayesian-Markov chain Monte Carlo approach.

It is a challenging issue to map Quantitative Trait Loci (QTL) underlying complex discrete traits, which usually show discontinuous distribution and less information, using conventional statistical methods. Bayesian-Markov chain Monte Carlo (Bayesian-MCMC) approach is the key procedure in mapping QTL for complex binary traits, which provides a complete posterior distribution for QTL parameters using all prior information. As a consequence, Bayesian estimates of all interested variables can be obtained straightforwardly basing on their posterior samples simulated by the MCMC algorithm. In our study, utilities of Bayesian-MCMC are demonstrated using simulated several animal outbred full-sib families with different family structures for a complex binary trait underlied by both a QTL and polygene. Under the Identity-by-Descent-Based variance component random model, three samplers basing on MCMC, including Gibbs sampling, Metropolis algorithm and reversible jump MCMC, were implemented to generate the joint posterior distribution of all unknowns so that the QTL parameters were obtained by Bayesian statistical inferring. The results showed that Bayesian-MCMC approach could work well and robust under different family structures and QTL effects. As family size increases and the number of family decreases, the accuracy of the parameter estimates will be improved. When the true QTL has a small effect, using outbred population experiment design with large family size is the optimal mapping strategy.