The Study of SRSF1 Regulates Abnormal Alternative Splicing of BCL2L11 and the Role in Refractory Acute Ayeloid Leukemia.

INTRODUCTION: Abnormalities in splicing factors, such as mutations or deregulated expression, can lead to aberrant splicing of target genes, potentially contributing to the pathogenesis of acute myeloid leukemia (AML). Despite this, the precise mechanism underlying the abnormal alternative splicing induced by SRSF1, a splicing factor associated with poor AML prognosis, remains elusive. METHODS: Using strict splicing criteria, we globally screened for alternative splicing(AS) events in NPMc-positive and NPMc-negative AML samples from TCGA. An AS network associated with AML prognosis was then established. Functional assays, including CCK-8, flow cytometry, and Western blot, were conducted on K562 and THP-1 cells overexpressing SRSF1. Cell viability following 72-hour Omipalisib treatment was also assessed. To explore the mechanism of SRSF1-induced AS, we created a BCL2L11 miniGene with a site-specific mutation at its branch point. The AS patterns of both wild-type and mutant miniGenes were analyzed following SRSF1 overexpression in HEK-293T, along with the subcellular localization of different spliceosomes. RESULTS: SRSF1 was significantly associated with AML prognosis. Notably, its expression was markedly upregulated in refractory AML patients compared to those with a favorable chemotherapy response. Overexpression of SRSF1 promoted THP-1 cell proliferation, suppressed apoptosis, and reduced sensitivity to Omipalisib. Mechanistically, SRSF1 recognized an aberrant branch point within the BCL2L11 intron, promoting the inclusion of a cryptic exon 3, which in turn led to apoptosis arrest. CONCLUSIONS: Overexpression of SRSF1 and the resulting abnormal splicing of BCL2L11 are associated with drug resistance and poor prognosis in AML.

Kaempferol attenuates particle-induced osteogenic impairment by regulating ER stress via the IRE1α/XBP1s pathway.

Periprosthetic osteolysis and subsequent aseptic loosening are the primary causes of failure following total joint arthroplasty. Wear particle-induced osteogenic impairment is recognized as an important contributing factor in the development of osteolysis, with endoplasmic reticulum (ER) stress emerging as a pivotal underlying mechanism. Hence, searching for potential therapeutic targets and agents capable of modulating ER stress in osteoblasts is crucial for preventing aseptic loosening. Kaempferol (KAE), a natural flavonol compound, has shown promising osteoprotective effects and anti-ER stress properties in diverse diseases. However, the influence of KAE on ER stress-mediated osteogenic impairment induced by wear particles remains unclear. In this study, we observed that KAE effectively relieved TiAl6V4 particles (TiPs)-induced osteolysis by improving osteogenesis in a mouse calvarial model. Furthermore, we demonstrated that KAE could attenuate ER stress-mediated apoptosis in osteoblasts exposed to TiPs, both in vitro and in vivo. Mechanistically, our results revealed that KAE mitigated ER stress-mediated apoptosis by upregulating the IRE1α/XBP1s pathway while concurrently partially inhibiting the IRE1α-regulated RIDD and JNK activation. Collectively, our findings suggest that KAE is a prospective therapeutic agent for treating wear particle-induced osteolysis, and highlight the IRE1α/XBP1s pathway as a potential therapeutic target for preventing aseptic loosening.

Correlation Between Hemoglobin Levels and Polycystic Ovary Syndrome Metabolic Disorder.

Objective: The purpose of this study was to explore the relationship between hemoglobin levels and metabolic disorders in patients with PCOS. Methods: A total of 573 patients were selected, based on the hemoglobin level; 342 patients with PCOS were divided into two groups as follows: Group A (normal Hb group, n = 269) and Group B (high Hb group, n = 73); 231 non-PCOS patients were divided into two groups as follows: Group C (normal Hb group, n = 199), and Group D (high Hb group, n = 32). The general information, glucose and lipid metabolism indicators, and uric acid levels of all patients were compiled for data analysis. Results: (1) Hb, HGB concentration in mean red blood cells and RDW in PCOS patients were higher than those in non-PCOS patients, and MCV was lower than that in non-PCOS patients (P < 0.05); (2) Compared with Group A, patients in Group B had higher BMI, Hb, 2-hPG, FINS, 2-hINS, HOMA-IR, LDL-C, and uric acid levels while the QUICKI was lower; in Group C, the age, FSH, HDL-C, and LDL-C were higher, and AMH, BMI, T, TG, and uric acid level were lower (P<0.05); compared with Group D, AMH, BMI, FINS, HOMA-IR, TG, uric acid level increased, while age, FSH, and QUICKI decreased in Group B; and Hb and T decreased in Group C (P<0.05); (3) Pearson's correlation analysis indicated that Hb in PCOS patients was positively correlated with BMI, FPG, 2-hPG, FINS, 2-hINS, and HOMA-IR, and negatively correlated with the QUICKI (P<0.05); (4) Multi-factor logistic regression analysis suggested that the high Hb level in PCOS patients was an independent risk factor of IR (P<0.05). Conclusion: Hb level in patients with PCOS was associated with BMI and glucose metabolism indicators; a high Hb level may be an independent risk factor for IR.

Bibliometric and visual analysis of Kawasaki disease in children from 2012 to 2022.

Background: In recent years, the incidence of Kawasaki disease among the pediatric population has experienced a significant increase. With complications mainly affecting the cardiovascular system, Kawasaki disease has received widespread attention from scholars worldwide. Numerous articles on Kawasaki disease in children have been published far. However, there is a lack of studies that use visualization methods to perform a bibliometric analysis of the relevant literature. This study aims to obtain overall information on the output characteristics of publications on childhood Kawasaki disease between 2012 and 2022 through bibliometric analysis, identify research hotspots and frontiers, and provide new ideas and references for future clinical and scientific research. Methods: Literature meeting the inclusion criteria was screened from the Web of Science Core Collection, PubMed, and Scopus databases. Visual analysis of the literature by country, institution, journal, author, keywords, and references was performed using Citespace (6.1.R6), VOSviewer (1.6.18), and the online bibliometric website (https://bibliometric.com/). Results: A total of 4,867 eligible publications were included. The number of annual publications is generally rising, rapidly increasing since 2019. Among countries and institutions, China and KAOHSIUNG CHANG GUNG MEMORIAL HOSPITAL have the highest output of articles. With 104 publications, Ho-Chang Kuo has a high impact in the field of KD. The most cited author is Jane W. Newburger. The most prolific journal is FRONTIERS IN PEDIATRICS. CIRCULATION is the most frequently co-cited journal. The most popular keyword in frequency and centrality is "immunoglobulin". The reference with the highest burst intensity was Verdoni L, LANCET, 2020. Conclusion: Kawasaki disease in children remains a hot topic among pediatricians worldwide and is receiving increasing attention. We innovated the "national-institutional-journal" model, which promotes further international cooperation in this field. The hot topics in the field of pediatric KD are "KD pathogenesis", "immunoglobulin resistance and complementary therapy", and "cardiovascular complications". Frontiers include disease-related ("multisystem inflammatory syndrome", "coronavirus disease 2019", "hypotension"), treatment-related ("procalcitonin", " anakinra"), and pathogenesis ("polymerase chain reaction").

Change point detection for high dimensional data via kernel measure with application to human aging brain data.

Identifying the existence and locations of change points has been a broadly encountered task in many statistical application areas. The existing change point detection methods may produce unsatisfactory results for high-dimensional data since certain distributional assumptions are made on data, which are hard to verify in practice. Moreover, some parameters (such as the number of change points) need to be estimated beforehand for some methods, making their powers sensitive to these values. Here, we propose a kernel-based U $$ U $$ -statistic to identify change points (KUCP) for high dimensional data, which is free of distributional assumptions and sup-parameter estimations. Specifically, we employ a kernel function to describe similarities among the subjects and construct a U $$ U $$ -statistic to test the existence of change point for a given location. The asymptotic properties of the U $$ U $$ -statistic are deduced. We also develop a procedure to locate the change points sequentially via a dichotomy algorithm. Extensive simulations demonstrate that KUCP has higher sensitivity in identifying existence of change points and higher accuracy in locating these change points than its counterparts. We further illustrate its practical utility by analyzing a gene expression data of human brain to detect the time point when gene expression profiles begin to change, which has been reported to be closely related with aging brain.

Worldwide Bronchiolitis obliterans research: A bibliometric analysis of the published literature between 2002 and 2022.

Bronchiolitis obliterans (BO) is a rare and irreversible chronic respiratory disease. The diagnosis of BO is challenging, and there still needs to be specific therapies and uniform treatment guidelines available. Research on BO has grown steadily over the past 20 years, and with the continued interest of researchers in this area, a bibliometric study of BO becomes necessary. This topic aims to assess the current state of research in BO over the last 2 decades and to identify research hotspots and emerging directions. Information on BO-related articles were obtained from the Science Citation Index Expand of the Web of Science Core Collection (WOSCC [SCI-E]) database. Citespace (6.1.R6), VOSviewer (1.6.18), and the online bibliometrics website (https://bibliometric.com/) were used for bibliometric analysis mainly to include country/region, institution, author, journal, keywords, and references and to construct visual knowledge network diagrams. A total of 4153 publications from the WOSCC [SCI-E] database were included in this study. Most publications come from the United States, Japan, and Germany, which collaborate relatively more frequently. Research institutions in the United States, especially the University of Washington, published the largest number of BO-related articles. Regarding authors, Vos, R is the most productive author, while Verleden, GM is the most influential in BO. In addition, JOURNAL OF HEART AND LUNG TRANSPLANTATION is the journal with the most published articles. The most cited article is Estenne M, 2002. Based on the clustering analysis of keywords and references, the diagnosis of bronchiolitis obliterans syndrome (BOS), treatment of BOS, and risk factors of BO are the current research hotspots and future research trends. We analyzed the publication trends in BO by bibliometrics and mapped the knowledge network of major contributing countries/regions, institutions, authors, and journals. Current research hotspots were found based on the main keywords and references. The outcome may help researchers identify potential collaborators, collaborating institutions, and hot fronts in BO to enhance collaboration on critical issues and improve the diagnosis and treatment of BO.

Association of Serum Vitamin D With Periodontal Disease.

OBJECTIVE: There are conflicting reports on the relationship between vitamin D and periodontal disease. Our research is intended to further analyse the association between serum 25(OH)D3, a vitamin D precursor and periodontal disease based on a large national survey sample in Japan. METHODS: We downloaded the 2009-2018 National Health and Nutrition Examination Survey (NHANES) cycle, which included a total of 23,324 samples. Logistic regression of factors influencing perioral disease including periodntal disease, and subgroup logistic regression were performed to analyse the relationship between serum vitamin D and perioral disease, using WTMEC2YR as weights for regression analysis. Then machine learning model-based prediction of perioral disease onset was performed, and the machine learning algorithms used included boost tree, artificial neural network, AdaBoost, and random forest. RESULTS: We evaluated the vitamin D, age, sex, race, education, marriage, body mass index, ratio of family income to poverty (PIR), smoking, alcohol consumption, diabetes, and hypertension as variables in the included samples. Vitamin D was negatively associated with perioral disease; compared with Q1, the odds ratios and 95% CI were 0.8 (0.67-0.96) for Q2, 0.84 (0.71-1.00) for Q3, and 0.74 (0.6-0.92) for Q4 (P for trend <.05), respectively. The results of the subgroup analysis showed that the effect of 25(OH)D3 on periodontal disease was more pronounced in women younger than 60 years. Based on the accuracy and receiver operating characteristic curve, we concluded that a boost tree was a relatively good model to predict periodontal disease. CONCLUSIONS: Vitamin D might be a protective factor for periodontal disease, and boost tree analysis we emplyed was a relatively good model to predict perioral disease.

Screening properties of trend tests in genetic association studies.

In genome-wide association study, extracting disease-associated genetic variants among millions of single nucleotide polymorphisms is of great importance. When the response is a binary variable, the Cochran-Armitage trend tests and associated MAX test are among the most widely used methods for association analysis. However, the theoretical guarantees for applying these methods to variable screening have not been built. To fill this gap, we propose screening procedures based on adjusted versions of these methods and prove their sure screening properties and ranking consistency properties. Extensive simulations are conducted to compare the performances of different screening procedures and demonstrate the robustness and efficiency of MAX test-based screening procedure. A case study on a dataset of type 1 diabetes further verifies their effectiveness.

Using preoperative control nutritional status scores as prognostic factors for endometrial cancer.

Background: Previous investigations have reported that controlling nutritional (CONUT) status scores, incorporating total cholesterol (TC) and serum albumin (SA) values, and total lymphocyte (LY) counts, are reliable malignant tumor predictors. However, CONUT scores for predicting endometrial cancer (EC) remain unexplored. Objective: To evaluate preoperative CONUT scores as prognostic factors for postoperative EC. Methods: We retrospectively evaluated preoperative CONUT scores in 785 surgically resected EC patients at our hospital between June 2012 and May 2016. Using time-dependent receiver operating characteristic (ROC) analyses, patients were split into: 1) CONUT-high (CH) (≥1) and 2) CONUT-low (CL) (<1) groups. Relationships between CONUT scores and different clinicopathological, pathological differentiation, muscle layer infiltration depth, and prognosis factors were examined, and Cox regression analyses performed to assess prognostic values on overall survival (OS) rates. Results: We assigned 404 (51.5%) and 381 (58.5%) patients to CH and CL groups, respectively. In the CH group, body mass index (BMI), prognostic nutrition index (PNI), and LY/monocyte ratios (LMR) were decreased, however, neutrophil/LY (NLR) and platelet/LY ratios (PLR) were increased. Pathological differentiation analyses showed that G1 proportions were higher in the CL group, while G2 and G3 proportions were more prevalent in the CH group. Muscle layer infiltration depth in CL patients was < 50%, while that it was ≥50% in the CH group. No significant differences in OS rates were recorded between CH and CL groups over 60 months. However long-term survival (LTS) rates after 60 months in the CH group were significantly lower when compared with the CL group, and was more obvious in type II EC patients. Also, periuterine infiltration and preoperative CONUT scores were independent prognostic factors for OS rates as indicated by multi-factor analyses. Conclusion: CONUT scores not only facilitated the estimation of nutritional status, but were highly beneficial for predicting OS rates in patients with EC after curative resection. CONUT scores provided high predictive values for LTS rates over 60 months in these patients.

A maximum kernel-based association test to detect the pleiotropic genetic effects on multiple phenotypes.

MOTIVATION: Testing the association between multiple phenotypes with a set of genetic variants simultaneously, rather than analyzing one trait at a time, is receiving increasing attention for its high statistical power and easy explanation on pleiotropic effects. The kernel-based association test (KAT), being free of data dimensions and structures, has proven to be a good alternative method for genetic association analysis with multiple phenotypes. However, KAT suffers from substantial power loss when multiple phenotypes have moderate to strong correlations. To handle this issue, we propose a maximum KAT (MaxKAT) and suggest using the generalized extreme value distribution to calculate its statistical significance under the null hypothesis. RESULTS: We show that MaxKAT reduces computational intensity greatly while maintaining high accuracy. Extensive simulations demonstrate that MaxKAT can properly control type I error rates and obtain remarkably higher power than KAT under most of the considered scenarios. Application to a porcine dataset used in biomedical experiments of human disease further illustrates its practical utility. AVAILABILITY AND IMPLEMENTATION: The R package MaxKAT that implements the proposed method is available on Github https://github.com/WangJJ-xrk/MaxKAT.

Divided-and-combined omnibus test for genetic association analysis with high-dimensional data.

Advances in biologic technology enable researchers to obtain a huge amount of genetic and genomic data, whose dimensions are often quite high on both phenotypes and variants. Testing their association with multiple phenotypes has been a hot topic in recent years. Traditional single phenotype multiple variant analysis has to be adjusted for multiple testing and thus suffers from substantial power loss due to ignorance of correlation across phenotypes. Similarity-based method, which uses the trace of product of two similarity matrices as a test statistic, has emerged as a useful tool to handle this problem. However, it loses power when the correlation strength within multiple phenotypes is middle or strong, for some signals represented by the eigenvalues of phenotypic similarity matrix are masked by others. We propose a divided-and-combined omnibus test to handle this drawback of the similarity-based method. Based on the divided-and-combined strategy, we first divide signals into two groups in a series of cut points according to eigenvalues of the phenotypic similarity matrix and combine analysis results via the Cauchy-combined method to reach a final statistic. Extensive simulations and application to a pig data demonstrate that the proposed statistic is much more powerful and robust than the original test under most of the considered scenarios, and sometimes the power increase can be more than 0.6. Divided-and-combined omnibus test facilitates genetic association analysis with high-dimensional data and achieves much higher power than the existing similarity based method. In fact, divided-and-combined omnibus test can be used whenever the association analysis between two multivariate variables needs to be conducted.

Natural variation of codon repeats in COLD11 endows rice with chilling resilience.

Abnormal temperature caused by global climate change threatens the rice production. Defense signaling network for chilling has been uncovered in plants. However, less is known about repairing DNA damage produced from overwhelmed defense and its evolution during domestication. Here, we genetically identified a major QTL, COLD11, using the data-merging genome-wide association study based on an algorithm combining polarized data from two subspecies, indica and japonica, into one system. Rice loss-of-function mutations of COLD11 caused reduced chilling tolerance. Genome evolution analysis of representative rice germplasms suggested that numbers of GCG sequence repeats in the first exon of COLD11 were subjected to strong domestication selection during the northern expansion of rice planting. The repeat numbers affected the biochemical activity of DNA repair protein COLD11/RAD51A1 in renovating DNA damage under chilling stress. Our findings highlight a potential way to finely manipulate key genes in rice genome and effectively improve chilling tolerance through molecular designing.

Role of EGFR expressed on the granulosa cells in the pathogenesis of polycystic ovarian syndrome.

Polycystic ovarian syndrome (PCOS) is one of the most common endocrinological disorders affecting between 6 to 20% of reproductive aged women. However, the etiology of PCOS is still unclear. Epidermal growth factor receptor (EGFR) plays a critical role in the growth and development of ovarian follicles. In our previous study, we showed that the expression level of EGFR was significantly higher in the cumulus granulosa cells from women with PCOS than that of normal women, suggesting that EGFR may play a potential role in the pathogenesis of PCOS. The present study further evaluated the association between EGFR and PCOS through both in clinical observation and animal experiments. We firstly validated the differential expression of EGFR in cumulus granulosa cells between PCOS patients and normal subjects by qRT-PCR and immunofluorescence staining. Then we generated a mouse model (n=20) of PCOS by injecting dehydroepiandrosterone (DHEA). The PCOS mice were then injected with an E corpus GFR inhibitor (AG1478) (n=10), which significantly improved the sex hormone levels in the estrous cycle stage, and the serum levels of LH, FSH and testosterone were compared with the PCOS mice without EGFR inhibitor treatment (n=10). Decreasing the expression level of EGFR in the PCOS mice also improved the ovulatory function of their ovaries which was indicated by the multifarious follicle stage in these mice as compared with the PCOS mice without EGFR inhibitor treatment. Also, the number of corpopa lutea were higher in the control group and the EGFR inhibitor treated group than in the PCOS group. The sex hormone levels and reproductive function were not significantly different between the control mice and the PCOS mice treated with the EGFR inhibitor. Our results demonstrated that EGF/EGFR signaling affected the proliferation of cumulus granulosa cells, oocyte maturation and meiosis, and played a potential role in the pathogenesis of PCOS. Therefore, the selective inhibition of EGFR may serve as a novel strategy for the clinical management of PCOS.

A resample-replace lasso procedure for combining high-dimensional markers with limit of detection.

In disease screening, a biomarker combination developed by combining multiple markers tends to have a higher sensitivity than an individual marker. Parametric methods for marker combination rely on the inverse of covariance matrices, which is often a non-trivial problem for high-dimensional data generated by modern high-throughput technologies. Additionally, another common problem in disease diagnosis is the existence of limit of detection (LOD) for an instrument - that is, when a biomarker's value falls below the limit, it cannot be observed and is assigned an NA value. To handle these two challenges in combining high-dimensional biomarkers with the presence of LOD, we propose a resample-replace lasso procedure. We first impute the values below LOD and then use the graphical lasso method to estimate the means and precision matrices for the high-dimensional biomarkers. The simulation results show that our method outperforms alternative methods such as either substitute NA values with LOD values or remove observations that have NA values. A real case analysis on a protein profiling study of glioblastoma patients on their survival status indicates that the biomarker combination obtained through the proposed method is more accurate in distinguishing between two groups.

Prediction of renal damage in children with IgA vasculitis based on machine learning.

This article is objected to explore the value of machine learning algorithm in predicting the risk of renal damage in children with IgA vasculitis by constructing a predictive model and analyzing the related risk factors of IgA vasculitis Nephritis in children. Case data of 288 hospitalized children with IgA vasculitis from November 2018 to October 2021 were collected. The data included 42 indicators such as demographic characteristics, clinical symptoms and laboratory tests, etc. Univariate feature selection was used for feature extraction, and logistic regression, support vector machine (SVM), decision tree and random forest (RF) algorithms were used separately for classification prediction. Lastly, the performance of four algorithms is compared using accuracy rate, recall rate and AUC. The accuracy rate, recall rate and AUC of the established RF model were 0.83, 0.86 and 0.91 respectively, which were higher than 0.74, 0.80 and 0.89 of the logistic regression model; higher than 0.70, 0.80 and 0.89 of SVM model; higher than 0.74, 0.80 and 0.81 of the decision tree model. The top 10 important features provided by RF model are: Persistent purpura ≥4 weeks, Cr, Clinic time, ALB, WBC, TC, Relapse, TG, Recurrent purpura and EB-DNA. The model based on RF algorithm has better performance in the prediction of children with IgA vasculitis renal damage, indicated by better classification accuracy, better classification effect and better generalization performance.

Jujuboside A Exhibits an Antiepileptogenic Effect in the Rat Model via Protection against Traumatic Epilepsy-Induced Oxidative Stress and Inflammatory Responses.

Traumatic brain injuries (TBI) are the greatest source of death in trauma, and post-traumatic epilepsy (PTE) is one of the common complications of TBI. Oxidative stress and inflammatory responses play an important role in the process of PTE. Many studies have shown that Jujuboside A has powerful antioxidant and anti-inflammatory properties. However, it is not known whether Jujuboside A has an anti-epileptic effect. The influences of Jujuboside A in the experimental FeCl3-induced model of PTE were tested by estimating the grade of seizures and performing behavioral tests. Following that, we detected oxidative stress indicators and inflammatory factors. Additionally, western blotting was used to test the protein levels of signaling molecules in MAPK pathways. In this study, Jujuboside A was found to have improved the recognition deficiency and epilepsy syndromes in the experimental rat model. Moreover, oxidative stress and inflammatory responses induced by FeCl3 injection were relieved by Jujuboside A. In addition, Jujuboside A was found to be capable of reducing the increased expression of p-P38 and p-ERK1/2 caused by iron ions. Collectively, our results demonstrated that Jujuboside A exhibits an antiepileptogenic effect by alleviating oxidative stress and inflammatory responses via the p38 and ERK1/2 pathways.

The Effects of Human Umbilical Cord Mesenchymal Stem Cell Transplantation on Female Fertility Restoration in Mice.

BACKGROUND: Female fertility refers to the capacity to produce oocytes and achieve fertilization and pregnancy, and it is impaired by age, disease, environment and social pressure. However, no effective therapy that restores female reproductive ability has been established. Mesenchymal Stromal Cells (MSCs) exhibit multilineage differentiation potential and have attracted considerable attention as a tool for restoring female fertility. METHODS: This study used human umbilical cord-MSCs (Huc-MSCs) to restore fertility in aging female mice and mice with chemotherapy-induced damage through the rescue of ovarian function and reconstruction of the fallopian tubes and uterus. In our study, two mouse models were generated: aging mice (35 weeks of age) and mice with chemotherapy-induced damage. RESULTS: The effect of MSCs on the ovaries, fallopian tubes and uterus was evaluated by analyzing gonadal hormone levels and by performing morphological and statistical analyses. The levels of estradiol (E2) and follicle-stimulating hormone (FSH) exhibited significant recovery after Huc-MSC transplantation in both aging mice and chemotherapy-treated mice. Huc-MSC treatment also increased the number of primordial, developing and preovulatory follicles in the ovaries of mice. Moreover, MSCs were shown to rescue the morphology of the fallopian tubes and uterus through mechanisms such as cilia regeneration in the fallopian tubes and reformation of glands and endometrial tissue in the uterus. CONCLUSION: Huc-MSCs may represent an effective treatment for restoring female fertility through recovery from chemotherapy-induced damage and rescue of female reproductive organs from the effects of aging.

A retrospective cohort study of obstetric complications and birth outcomes in women with polycystic ovarian syndrome.

Polycystic ovary syndrome (PCOS) is a common endocrine disorder in women and a high risk factor for adverse pregnancy complications. Therefore, we aimed to analyse the relationship between PCOS and pregnancy complications in a large sample from China. Additionally, since obesity and assisted reproductive technology (ART) are common in women with PCOS, we also aimed to determine whether both of these factors increased the complication incidence for women with PCOS. A retrospective cohort study that included 1357 pregnant women with PCOS and 6940 without PCOS was performed. Our results indicated women with PCOS had higher incidence of gestational diabetes mellitus (GDM), hypertension, postpartum haemorrhage, preterm birth, macrosomia and cervical incompetence. Additionally, obesity was associated with an increased incidence of hypertension and GDM in women with PCOS generally. ART did not result in an increase in the obstetric complication rate in women with PCOS. In conclusion, PCOS appeared to result in an increased risk of adverse pregnancy complications. Obesity may further increase the risks of hypertension and GDM among women with PCOS. However, ART did not increase the risk of pregnancy complications, which suggests that ART is a relatively safe and effective method to address infertility problems in women with PCOS.IMPACT STATEMENTWhat is already known on this subject? There are several studies evaluating the associations of PCOS with the risk of pregnancy complications. However, reports about the risk of pregnancy complications between PCOS women with and without obesity or ART are limited.What do the results of this study add? PCOS appeared to increase the risk of adverse pregnancy complications, including GDM, pregnancy-induced hypertension, postpartum haemorrhage, preterm birth, macrosomia and cervical incompetence. Obesity further increased the risks of hypertension and GDM in women with PCOS, but it did not increase the incidence of macrosomia and postpartum haemorrhage. Additionally, ART did not increase the risk of adverse pregnancy complications among women with PCOS, except for postpartum haemorrhage.What are the implications of these findings for clinical practice and/or further research? This study contributes to the literature because it showed that PCOS independently increased the risk of adverse pregnancy complications in a large sample of patients. Second, obesity is a high risk factor for adverse complications in pregnant women with PCOS. Third, ART is a relatively safe and effective method for addressing infertility problems for women with PCOS.

A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy.

Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.

Analysis of Sports Performance Prediction Model Based on GA-BP Neural Network Algorithm.

There are many factors that affect athletes' sports performance in sports competitions. The traditional sports performance prediction method is difficult to obtain more accurate sports performance prediction results and corresponding data analysis in a short time, which is not conducive for coaches to formulate targeted and scientific training sprint plans for athletes' problems. Therefore, based on GA-BP neural network algorithm, this paper constructs a sports performance prediction model and carries out experiments and analysis. The experimental results show that GA-BP neural network algorithm has a faster convergence speed than BP neural network and can achieve the expected error accuracy in a shorter time, which overcomes the problems of the BP neural network. At the same time, different from the previous models, GA-BP neural network algorithm can get the athlete training model according to the relationship between quality training indicators and special sports training results, which can more intuitively show the advantages and disadvantages of athletes. In the final sports performance prediction results, GA-BP neural network prediction results have higher accuracy, better stability, better prediction effect, and higher application value than BP neural network.