Preeclampsia in pregnant women with polycystic ovary syndrome: risk factor analysis based on a retrospective cohort study

Objectives: To compare the clinical characteristics of pregnant women with polycystic ovary syndrome (PCOS) and perinatal outcomes with or without preeclampsia (PE) and to factors that are potentially associated with the onset of PE. Material and methods: This was a retrospective study of pregnant women diagnosed with PCOS from January 2017 to December 2021. Eligible patients were divided into two groups based on the presence or absence of preeclampsia: a PE group and a non-PE group. Demographics, clinical characteristics, maternal and perinatal outcomes, and potential factors linked to disease recurrence were analyzed. Results: In total, 616 patients were enrolled and respectively classified into the PE group (n = 51) and the non-PE group (n = 565). The incidence of PE in pregnant women with PCOS was 8.28%; this was significantly higher than that in non-PCOS pregnant women (3.22%, p < 0.001). Logistic regression analysis of the predictive factors for PE in women with PCOS revealed that the combination of maternal hyperandrogenism, a pre-pregnancy BMI ≥ 24 kg/m2, and a family history of cardiovascular disease (CVD) and assisted reproductive techniques (ART) exhibited the steepest receiver-operating characteristic (ROC) curve value at 0.797 [95% confidence interval (CI): 0.733–0.862]. Conclusions: Patients with PCOS have a higher incidence of PE. We identified a series of significant and independent factors associated with PE in PCOS: maternal hyperandrogenism, a pre-pregnancy BMI ≥ 24 kg/m2, and a family history of CVD and ART

An Ensemble Successive Project Algorithm for Liquor Detection Using Near Infrared Sensor

Spectral analysis technique based on near infrared (NIR) sensor is a powerful tool for complex information processing and high precision recognition, and it has been widely applied to quality analysis and online inspection of agricultural products. This paper proposes a new method to address the instability of small sample sizes in the successive projections algorithm (SPA) as well as the lack of association between selected variables and the analyte. The proposed method is an evaluated bootstrap ensemble SPA method (EBSPA) based on a variable evaluation index (EI) for variable selection, and is applied to the quantitative prediction of alcohol concentrations in liquor using NIR sensor. In the experiment, the proposed EBSPA with three kinds of modeling methods are established to test their performance. In addition, the proposed EBSPA combined with partial least square is compared with other state-of-the-art variable selection methods. The results show that the proposed method can solve the defects of SPA and it has the best generalization performance and stability. Furthermore, the physical meaning of the selected variables from the near infrared sensor data is clear, which can effectively reduce the variables and improve their prediction accuracy

An Optimization of the Analytical Method for Determining the Flexural Toppling Failure Plane

According to the results of the physical model tests, the failure plane of an anaclinal layered rock slope was a linear-type plane at an angle above the plane normal to the discontinuities, and the failure mode of rock strata was bending tension. However, the shear failure occurred near the slope toe, the effects of the cohesion of the discontinuities on the stability of the slope, and the contribution of tangential force to cross-section axial force were neglected in such studies. Moreover, none of the experts had developed a rigorously theoretical method for determining the angle between the failure plane and the plane normal to the discontinuities. This paper was initiated for the purpose of solving the problems described above. With the cantilever beam model and a step-by-step analytical method, an optimization of the analytical method for determining the flexural toppling failure plane based on the limit equilibrium theory was developed and the corresponding formulations were derived. Based on the present computational framework, comparisons with other studies were carried out by taking a slate slope in South Anhui in China and a rock slope facing the Tehran-Chalus Road near the Amir-Kabir Dam Lake in Iran. Furthermore, the sensitivity analyses of the parameters used in the calculation process of the failure angle of the slate slope in South Anhui in China were performed. The results demonstrated that the failure plane and the safety factor of the stability obtained with the presented method were credible, which verified the proposed method. The dip angle of the slope, the dip angle of the rock stratum, and the friction angle of the discontinuities were the controlling factors for the overall failure of the slate slope in South Anhui in China

Alcohol Expectancies among Adolescents in Inner Mongolia

Female students were more likely to report global negative expectancies, while male students were more likely to report stronger positive social perception expectancies. The 11th and 12th graders expected more negative effects from drinking including global negative effects and negative personal effects than did the 10th graders. Nondrinkers and occasional drinkers reported greater expectancies of negative personal effects and negative perceptions of drinking than regular-drinkers. In contrast, regular drinkers more often reported expectancies of positive social perception, tension reduction and pleasure. social courtesy, social facilitation. and beneficial drinking. The results suggest that alcohol expectancies among Chinese adolescents in Inner Mongolia vary as a function of gender, grade, and drinking behavior

A Review on Anti-Dip Bedding Rock Slopes Subjected to Flexural Toppling

Flexural toppling is typically observed in anti-dip bedding rock slopes with a set of steeply dipping parallel joints against the slope face. This failure occurs due to the bending of rock strata, similar to cantilever beams. However, the exact failure surface is usually unknown, which leads to an assumption regarding the location and shape of the failure surface that must be made in the theoretical analysis. This assumption serves as the basis for stability analysis but may lead to errors if it is not appropriate. Therefore, in this study, we provide a detailed and systematic review of the mechanical model, precondition, failure patterns of rock strata, primary controlling factors, morphological characteristics of slope failure surfaces, and theoretical analysis methods of slope stability. We also introduce two practical application cases to better understand the advantages, disadvantages, and application scope of these methods. Additionally, we discuss the existing issues and potential future research developments in this field

Thickness prediction of seismic multi-attributes sand based on association rules and random forests

Seismic attributes analysis technique is an important tool for sand thickness prediction. Due to the varieties of seismic attributes, the best seismic attributes need to be optimized before the seismic attributes analysis technique is applied to reduce the repeatability and redundancy of the attributes. Therefore, we present an improved random forest regression algorithm combined with associate rules for sand thickness prediction (AR-RFR). Although random forest regression algorithm(RFR) is powerful for the problem characterized for nonlinearity and high dimension in reservoir prediction, it cannot solve attribute reduction problems. The associate rules can find the non-linear relationship among the multi-attributes and can reduce some redundant attributes by means of Chi-squared Test. We apply ordinary RFR, AR-RFR and Neural network regression algorithm combined with associate rules(AR-BP) to a synthetic geological model and a real dataset. The results prove that the selection attributes from associate rules is more efficient than that from random forest. Compared to the drilled wells, AR-RFR has higher precision than RFR and AR-BP. And AR-RFR also can improve the lateral distribution of sand bodies. The method proposed is able to choose efficient seismic attributes and improve prediction of sand thickness

Detection of Soil Nitrogen Using Near Infrared Sensors Based on Soil Pretreatment and Algorithms

Soil nitrogen content is one of the important growth nutrient parameters of crops. It is a prerequisite for scientific fertilization to accurately grasp soil nutrient information in precision agriculture. The information about nutrients such as nitrogen in the soil can be obtained quickly by using a near-infrared sensor. The data can be analyzed in the detection process, which is nondestructive and non-polluting. In order to investigate the effect of soil pretreatment on nitrogen content by near infrared sensor, 16 nitrogen concentrations were mixed with soil and the soil samples were divided into three groups with different pretreatment. The first group of soil samples with strict pretreatment were dried, ground, sieved and pressed. The second group of soil samples were dried and ground. The third group of soil samples were simply dried. Three linear different modeling methods are used to analyze the spectrum, including partial least squares (PLS), uninformative variable elimination (UVE), competitive adaptive reweighted algorithm (CARS). The model of nonlinear partial least squares which supports vector machine (LS-SVM) is also used to analyze the soil reflectance spectrum. The results show that the soil samples with strict pretreatment have the best accuracy in predicting nitrogen content by near-infrared sensor, and the pretreatment method is suitable for practical application

High-Precision Automatic Identification of Fentanyl-Related Drugs by Terahertz Spectroscopy with Molecular Dynamics Simulation and Spectral Similarity Mapping

Fentanyl is a potent opioid analgesic with high bioavailability. It is the leading cause of drug addiction and overdose death. To better control the abuse of fentanyl and its derivatives, it is crucial to develop rapid and sensitive detection methods. However, fentanyl-related substrates undergo similar molecular structures resulting in similar properties, which are difficult to be identified by conventional spectroscopic methods. In this work, a method for the automatic identification of 8 fentanyl-related substances with similar spectral characteristics was developed using terahertz (THz) spectroscopy coupled with density functional theory (DFT) and spectral similarity mapping (SSM). To characterize the THz fingerprints of these fentanyl-related samples more accurately, the method of baseline estimation and denoising with sparsity was performed before revealing the unique molecular dynamics of each substance by DFT. The SSM method was proposed to identify these fentanyl analogs based on weighted spectral cosine–cross similarity and fingerprint discrete Fréchet distance, generating a matching list by stepwise searching the entire spectral database. The top matched list returned the identification results of the target fentanyl analogs with accuracies of 94.48~99.33%. Results from this work provide algorithms’ increased reliability, which serves as an artificial intelligence-based tool for high-precision fentanyl analysis in real-world samples

Association between Diethylhexyl Phthalate Exposure with Folliculogenesis and Ovarian Steroidogenesis: A Systematic Review and Meta-Analysis

Background: Environmental endocrine disruptor-diethylhexyl phthalate (DEHP) or its active metabolites-mono(2-ethylhexyl) phthalate (MEHP) has the greatest endocrine disrupting potency. The present systematic review and meta-analysis was to investigate the effects of DEHP/MEHP exposure on the folliculogenesis and ovarian steroidogenesis in female rodents. Methods: A search was conducted using EMBASE, PubMed, Web of Science, and Cochrance Library databases. The meta-analyses were performed using mean difference (MD) and random-effects model. Risk of bias and subgroup analyses were assessed using Revman 5.4.1 and R 4.1.2. Registration number: PROSPERO CRD42021292264. Results: A total of 15 studies were included in this systematic review. We found that the exposure of DEHP/MEHP significantly increased the ovary weight (p = 0.003), decreased the serum progesterone levels (p = 0.0008) and delayed the vaginal opening (p = 0.01). Conclusions: The DEHP/MEHP exposure has adverse effects on some aspects of female reproduction ability which tested in female rodent. However, more evidence is needed to strengthen the conclusion

Nano-in-Micro Delivery System Prepared by Co-Axial Air Flow for Oral Delivery of Conjugated Linoleic Acid

The preparation of a nano-in-micro delivery system (NiMDS) under mild conditions without using toxic organic solvents and expensive equipment still faces challenges. In this study, we introduced the co-axial air flow method to prepare NiMDS for the oral delivery of conjugated linoleic acid (CLA). The chitosan nanoparticles were prepared using the stearic-acid-modified chitosan through self-aggregation. Then, the chitosan nanoparticles were incorporated into alginate microparticles by the co-axial air flow method. The obtained chitosan nanoparticles and NiMDS were spherical in shape with the average sizes of 221–243 nm and 130–160 μm, respectively. Compared with alginate microparticles, the hybrid particles were of fewer fragments, were bigger in size, had a higher mechanical strength, and showed a controlled release in the phosphate buffer solution (pH 1.2 or 7.4). The release kinetics study showed that encapsulating the chitosan nanoparticles into the alginate microparticles inhibited the dissolution of alginate microparticles at the initial stage. These results revealed the potential of NiMDS as an ideal oral carrier for the sustained release of CLA in the gastrointestinal environment