Gap junctions in polycystic ovary syndrome: Implications for follicular arrest.

Gap junctions are specialized intercellular conduits that provide a direct pathway between neighboring cells, which are involved in numerous physiological processes, such as cellular differentiation, cell growth, and metabolic coordination. The effect of gap junctional hemichannels in folliculogenesis is particularly obvious, and the down-regulation of connexins is related to abnormal follicle growth. Polycystic ovary syndrome (PCOS) is a ubiquitous endocrine disorder of the reproductive system, affecting the fertility of adult women due to anovulation. Exciting evidence shows that gap junction is involved in the pathological process related to PCOS and affects the development of follicles in women with PCOS. In this review, we examine the expression of connexins in follicular cells of PCOS and figure out whether such communication could have consequences for PCOS women. While along with results from clinical and related animal studies, we summarize the mechanism of connexins involved in the pathogenesis of PCOS.

Bushen Huoxue Recipe inhibits endometrial epithelial-mesenchymal transition through the transforming growth factor-ß/nuclear factor kappa-B pathway to improve polycystic ovary syndrome-mediated infertility.

OBJECTIVE: To investigate the target and mechanism of action of Bushen Huoxue Recipe (BSHX) for the treatment of infertility in polycystic ovary syndrome (PCOS), to provide a basis for the development and clinical application of herbal compounds. METHODS: Prediction and validation of active ingredients and targets of BSHX for the treatment of PCOS by using network pharmacology-molecular docking technology. In an animal experiment, the rats were randomly divided into four groups (control group, model group, BSHX group, metformin group, n = 16 in each group), and letrozole combined with high-fat emulsion gavage was used to establish a PCOS rat model. Body weight, vaginal smears, and number of embryos were recorded for each group of rats. Hematoxylin-eosin (HE) staining was used to observe the morphological changes of ovarian and endometrial tissues, and an enzyme-linked immunosorbent assay (ELISA) was used to detect the serum inflammatory factor levels. Expression levels of transforming growth factor-ß (TGF-ß), transforming growth factor beta activated kinase 1 (TAK1), nuclear factor kappa-B (NF-κB), Vimentin, and E-cadherin proteins were measured by western blot (WB). RESULTS: Ninety active pharmaceutical ingredients were obtained from BSHX, involving 201 protein targets, of which 160 were potential therapeutic targets. The active ingredients of BSHX exhibited lower binding energy with tumor necrosis factor-α (TNF-α), TGF-ß, TAK1, and NF-κB protein receptors (< -5.0 kcal/mol). BSHX significantly reduced serum TNF-α levels in PCOS rats (p < .01), effectively regulated the estrous cycle, restored the pathological changes in the ovary and endometrium, improved the pregnancy rate, and increased the number of embryos. The results of WB suggested that BSHX can down-regulate protein expression levels of TGF-ß and NF-κB in endometrial tissue (p < .05), promote the expression level of E-cadherin protein (p < .001), intervene in the endometrial epithelial-mesenchymal transition (EMT) process. CONCLUSIONS: TGF-ß, TAK1, NF-κB, and TNF-α are important targets of BSHX for treating infertility in PCOS. BSHX improves the inflammatory state of PCOS, intervenes in the endometrial EMT process through the TGF-ß/NF-κB pathway, and restores endometrial pathological changes, further improving the pregnancy outcome in PCOS.

A UV-Vis spectroscopic detection method for cobalt ions in zinc sulfate solution based on discrete wavelet transform and extreme gradient boosting.

Zinc is a crucial strategic metal resource. The concentration of cobalt ions in zinc refining solution significantly impacts the efficiency of zinc electrolysis production. The traditional method of detecting cobalt ions in zinc solution is time-consuming, labor-intensive and ineffective. However, optical detection offers the advantage of high efficiency and low cost, making it a potential replacement for the traditional method. In this study, the spectral curve of cobalt ions in zinc solution is detected by ultraviolet-visible (UV-Vis) spectrophotometry. Additionally, we propose a model for the concentration-absorbance relationship of cobalt ions in zinc solution based on discrete wavelet transform and extreme gradient boosting (DWT-XGBoost) algorithms. First, the spectral curve's information region is denoised by using Savitzky-Golay (S-G) smoothing. Then, the denoised spectra is utilized to extract features through discrete wavelet transform and principal component analysis. These features are used as inputs to the XGBoost model to establish prediction models for low and high cobalt ions in zinc solution. Bayesian optimization is implemented to adjust the model's hyperparameters, including learning rate, feature sampling ratio, to enhance the prediction performance. Finally, applying the model to zinc solution samples from a zinc smelter and compared with other state-of-the-art algorithms, the DWT-XGBoost algorithm exhibits the lowest RMSE, MAE and MAPE, with values of 0.034 mg/L, 0.025 mg/L, 6.983 % for low cobalt and with values of 0.231 mg/L, 0.067 mg/L and 0.472 % for high cobalt. The experimental results demonstrate that the DWT-XGBoost model exhibits significantly superior prediction performance.

Effect of Bushen Huoxue recipe on serum metabolomics in polycystic ovary syndrome rats.

OBJECTIVE: To investigate the effect of Bushen Huoxue Recipe (BSHXR) on serum metabolomics in polycystic ovary syndrome rat (PCOSR). METHODS: In our study, twenty-four 6-week-old Sprague-Dawley (SD) female rats were randomly divided into three groups: treatment group, model group and blank group. The blank group and other groups were gavaged in different ways each morning, and the rats were treated with normal saline or BSHXR containing liquid each afternoon. Liquid chromatography-mass spectrometry (LC-MS) was employed to study serum metabolites in the treatment group after the study as well as in the model and blank groups. RESULTS: There was a tendency to normalize the histomorphology of ovarian pathology and the abnormal sex hormone level of PCOSR was significantly improved after BSHXR treatment. The level of serum metabolites was greatly changed in PCOSR treated with the BSHXR. We identified 32 metabolic targets of BSHXR in PCOSR using LC-MS, and further revealed BSHXR targeted five major metabolic pathway: retrograde endocannabinoid signaling, taurine and hypotaurine metabolism, glycerophospholipid metabolism, primary bile acid biosynthesis, arginine and proline metabolism. Conclusion: Our study found that BSHXR plays a role in the treatment of PCOS by regulating key metabolic pathways in the PCOSR.

Alterations of gut microbiota biodiversity and relative abundance in women with PCOS: A systematic review and meta-analysis.

BACKGROUND: Numerous studies have implicated that the gut microbiota is associated with polycystic ovary syndrome (PCOS). However, a comprehensive data-based summary shown that the effects of the PCOS on the gut microbiota is minimal. We aim to assess the alterations of gut microbiota in women with PCOS. METHODS: An electronic search of PubMed, Web of Science, Embase, Cochrane Library and Ovid was conducted for eligible studies published from inception to 28 March 2023, without any language or regional restrictions. We used Newcastle-Ottawa Quality Assessment Scale (NOS) to complete the assessment of the risk of bias and Stata 15.1 software to performed meta-analysis. RESULTS: There were 19 human observational studies in total with 617 women with PCOS and 439 healthy individuals were identified. Compared to the control group, the Chao index (WMD -28.88, 95% CI -45.78 to -11.98, I2 = 100%), Shannon index (WMD -0.11, 95% CI -0.18 to 0.00, I2 = 92.2%); and observed operational taxonomic units (OTUs) counts (WMD - 23.48, 95% CI -34.44 to -12. 53, I2 = 99.6%) were significantly lower in women with PCOS. The relative abundance of Bacteroidaceae was significantly higher (WMD 0.12, 95% CI 0.02 to 0.22, I2 = 9.2%), however there were no statistical differences in Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, Alcaligenaceae, Bifidobacteriaceae, Clostridiaceae, Enterobacteriaceae, Lachnospiraceae, Prevotellaceae, Ruminococcaceae, Veillonellaceae, Bacteroides, Bifidobacterium, Blautia, Dialister, Escherichia-Shigella, Faecalibacterium, Lachnoclostridium, Lachnospira, Megamonas, Phascolarctobacterium, Prevotella, Roseburia, and Subdoligranulum. CONCLUSION: We demonstrated the alpha diversity of gut microbiota and the relative abundance of Bacteroidaceae in women with PCOS are altered. The results indicates that dysbiosis may be a potential pathogenetic factor in PCOS and provided reliable information to investigate the role of gut microbiota in the development and progression of PCOS.

Semi-Supervised Deep Learning-Based Multi-component Spectral Calibration Modeling for UV-vis and Near-Infrared Spectroscopy without Information Loss.

Spectral analysis is an important method for characterizing and identifying chemical species. However, quantitative spectral analysis of multiple chemical properties in the real world has always been a challenging problem due to the strong correlation, massive noise, and serious information overlapping of the spectral features. Here, we present a new semi-supervised spectral calibration method based on information lossless decoupling of spectral features named NICEM. To realize the separation and extraction of key latent features, the method uses the flow-based model non-linear independent component estimation (NICE) to learn the sample distribution. The spectral data information is transformed into independent latent variables obeying Gaussian distribution by the reversible structure of deep network without information loss, so as to find the essential properties and realize the feature nonlinear decomposition. Moreover, the association between the input latent feature variables and attributes is evaluated by the maximum mutual information coefficient to eliminate the adverse effects of irrelevant information in the latent variable space and mine key information. Since the latent variables are independent in each dimension, the NICEM method is easier to establish an accurate semi-supervised multi-component calibration model even for high overlapping and complex spectral data. The applicability of the proposed spectral modeling method is demonstrated by using three ultraviolet-visible and near-infrared spectral data sets with 15 physical and chemical properties including diesel fuels, corn, and multi-metal ions solution. Results show that the proposed NICEM method has the highest determination coefficient (R2) and significantly improves extrapolation compared with the seven state-of-the-art methods. The proposed method is intuitive because it obviates complex feature engineering and prior knowledge and is a promising spectral calibration tool for quantitative analysis in other spectroscopy applications.

Guizhi Fuling Wan inhibits autophagy of granulosa cells in polycystic ovary syndrome mice via H19/miR-29b-3p.

OBJECTIVE: To investigate the potential molecular mechanism of traditional Chinese medicine Guizhi Fuling Wan (GZFLW) inhibiting granulosa cells (GCs) autophagy in polycystic ovary syndrome (PCOS). METHODS: Control GCs and model GCs were cultured and treated with blank serum or GZFLW-containing serum. The levels of H19 and miR-29b-3p in GCs were detected using qRT-PCR, target genes of miR-29b-3p were identified using luciferase assay. The protein expressions of Phosphatase and tensin homolog (PTEN), Matrix Metalloproteinase (MMP)-2, and Bax were measured using western blot. The level of autophagy was detected via MDC staining, the degree of autophagosomes and autophagic polymers was observed using dual fluorescence-tagged mRFP-eGFP-LC3. RESULTS: GZFLW intervention reduced the expression of autophagy-related proteins PTEN, MMP-2 and Bax, by upregulating the expression of miR-29b-3p and downregulated the expression of H19 (p < .05 or p < .01). The number of autophagosomes and autophagy polymers was significantly decreased by GZFLW treatment. However, the inhibition of miR-29b-3p and overexpression of H19 induced a significant increase in the number of autophagosomes and autophagic polymers, which attenuated the inhibitory effect of GZFLW on autophagy (p < .05 or p < .01). In addition, inhibition of miR-29b-3p or overexpression of H19 can attenuate the effect of GZFLW on the expression of PTEN, MMP-2 and Bax proteins (p < .05 or p < .01). CONCLUSION: Our study found that GZFLW inhibits autophagy in PCOS GCs via H19/miR-29b-3p pathway.

A Model Transfer Method among Spectrometers Based on Improved Deep Autoencoder for Concentration Determination of Heavy Metal Ions by UV-Vis Spectra.

Ultraviolet Visible (UV-Vis) spectroscopy detection technology has been widely used in quantitative analysis for its advantages of rapid and non-destructive determination. However, the difference of optical hardware severely restricts the development of spectral technology. Model transfer is one of the effective methods to establish models on different instruments. Due to the high dimension and nonlinearity of spectral data, the existing methods cannot effectively extract the hidden differences in spectra of different spectrometers. Thus, based on the necessity of spectral calibration model transfer between the traditional large spectrometer and the micro-spectrometer, a novel model transfer method based on improved deep autoencoder is proposed to realize spectral reconstruction between different spectrometers. Firstly, two autoencoders are used to train the spectral data of the master and slave instrument, respectively. Then, the hidden variable constraint is added to enhance the feature representation of the autoencoder, which makes the two hidden variables equal. Combined with a Bayesian optimization algorithm for the objective function, the transfer accuracy coefficient is proposed to characterize the model transfer performance. The experimental results show that after model transfer, the spectrum of the slave spectrometer is basically coincident with the master spectrometer and the wavelength shift is eliminated. Compared with the two commonly used direct standardization (DS) and piecewise direct standardization (PDS) algorithms, the average transfer accuracy coefficient of the proposed method is improved by 45.11% and 22.38%, respectively, when there are nonlinear differences between different spectrometers.

The impact of herbal medicine in regulating intestinal flora on female reproductive disorders.

As an important part of the human intestinal microecology, the intestinal flora is involved in a number of physiological functions of the host. Several studies have shown that imbalance of intestinal flora and its regulation of the intestinal barrier, intestinal immune response, and intestinal flora metabolites (short-chain fatty acids and bile acids) can affect the development and regression of female reproductive disorders. Herbal medicine has unique advantages in the treatment of female reproductive disorders such as polycystic ovary syndrome, endometriosis and premature ovarian insufficiency, although its mechanism of action is still unclear. Therefore, based on the role of intestinal flora in the occurrence and development of female reproduction-related diseases, the progress of research on the diversity, structure and composition of intestinal flora and its metabolites regulated by botanical drugs, Chinese herbal formulas and active ingredients of Chinese herbal medicines is reviewed, with a view to providing reference for the research on the mechanism of action of Chinese herbal medicines in the treatment of female reproductive disorders and further development of new herbal medicines.

Fault Detection for Wind Turbine Blade Bolts Based on GSG Combined with CS-LightGBM.

Aiming at the problem of class imbalance in the wind turbine blade bolts operation-monitoring dataset, a fault detection method for wind turbine blade bolts based on Gaussian Mixture Model-Synthetic Minority Oversampling Technique-Gaussian Mixture Model (GSG) combined with Cost-Sensitive LightGBM (CS-LightGBM) was proposed. Since it is difficult to obtain the fault samples of blade bolts, the GSG oversampling method was constructed to increase the fault samples in the blade bolt dataset. The method obtains the optimal number of clusters through the BIC criterion, and uses the GMM based on the optimal number of clusters to optimally cluster the fault samples in the blade bolt dataset. According to the density distribution of fault samples in inter-clusters, we synthesized new fault samples using SMOTE in an intra-cluster. This retains the distribution characteristics of the original fault class samples. Then, we used the GMM with the same initial cluster center to cluster the fault class samples that were added to new samples, and removed the synthetic fault class samples that were not clustered into the corresponding clusters. Finally, the synthetic data training set was used to train the CS-LightGBM fault detection model. Additionally, the hyperparameters of CS-LightGBM were optimized by the Bayesian optimization algorithm to obtain the optimal CS-LightGBM fault detection model. The experimental results show that compared with six models including SMOTE-LightGBM, CS-LightGBM, K-means-SMOTE-LightGBM, etc., the proposed fault detection model is superior to the other comparison methods in the false alarm rate, missing alarm rate and F1-score index. The method can well realize the fault detection of large wind turbine blade bolts.

Fault Detection of Wind Turbine Gearboxes Based on IBOA-ERF.

As one of the key components of wind turbines, gearboxes are under complex alternating loads for a long time, and the safety and reliability of the whole machine are often affected by the failure of internal gears and bearings. Aiming at the difficulty of optimizing the parameters of wind turbine gearbox fault detection models based on extreme random forest, a fault detection model with extreme random forest optimized by the improved butterfly optimization algorithm (IBOA-ERF) is proposed. The algebraic sum of the false alarm rate and the missing alarm rate of the fault detection model is constructed as the fitness function, and the initial position and position update strategy of the individual are improved. A chaotic mapping strategy is introduced to replace the original population initialization method to enhance the randomness of the initial population distribution. An adaptive inertia weight factor is proposed, combined with the landmark operator of the pigeon swarm optimization algorithm to update the population position iteration equation to speed up the convergence speed and improve the diversity and robustness of the butterfly optimization algorithm. The dynamic switching method of local and global search stages is adopted to achieve dynamic balance between global exploration and local search, and to avoid falling into local optima. The ERF fault detection model is trained, and the improved butterfly optimization algorithm is used to obtain optimal parameters to achieve fast response of the proposed model with good robustness and generalization under high-dimensional data. The experimental results show that, compared with other optimization algorithms, the proposed fault detection method of wind turbine gearboxes has a lower false alarm rate and missing alarm rate.

Quality-Driven Regularization for Deep Learning Networks and Its Application to Industrial Soft Sensors.

The growth of data collection in industrial processes has led to a renewed emphasis on the development of data-driven soft sensors. A key step in building an accurate, reliable soft sensor is feature representation. Deep networks have shown great ability to learn hierarchical data features using unsupervised pretraining and supervised fine-tuning. For typical deep networks like stacked auto-encoder (SAE), the pretraining stage is unsupervised, in which some important information related to quality variables may be discarded. In this article, a new quality-driven regularization (QR) is proposed for deep networks to learn quality-related features from industrial process data. Specifically, a QR-based SAE (QR-SAE) is developed, which changes the loss function to control the weights of the different input variables. By choosing an appropriate inductive bias for the weight matrix, the model provides quality-relevant information for predictive modeling. Finally, the proposed QR-SAE is used to predict the quality of a real industrial hydrocracking process. Comparative experiments show that QR-SAE can extract quality-related features and achieve accurate prediction performance.

Optimal setting strategy of electrocoagulation process in heavy metal wastewater treatment plant.

With the increasingly stringent environmental protection policies of various countries, the contradiction between the treatment cost and the purification degree of environmental pollutants has become increasingly significant, which has become a major factor restricting the efficient operation of wastewater treatment plants. Hence, keeping the ion concentration at the outlet as low as possible while reducing the cost are the main objectives of treating heavy metal wastewater by electrocoagulation (EC) process. However, due to the complicated mechanism and uncertain production conditions, it is difficult to achieve those goals by manually setting the current through operators' experience. In this paper, we develop a dynamic multi-objective optimization strategy for EC process to balance these two conflicting production targets. First, we define the removal efficiency (RE) to measure the effectiveness of the EC process. Due to the anodic passivation and cathodic polarization in the EC process, the current reversing period (CRP) is proposed and optimized to ensure the stable performance of the electrodes. Then the current setting problem is formulated as a constrained multi-objective optimization problem with competing objectives of RE and cost. An interval-adjustable control parameterization (CP) approach is developed to reduce the complexity of this optimization problem. To compute this optimization problem, a heuristic method named multi-objective state transition algorithm (MOSTA) with evaluation value is investigated. The effectiveness of our model and optimization strategy is demonstrated by a successful implementation in an EC process of a wastewater treatment plant in Chenzhou, China.

A Characteristic Interval Modeling Method for Simultaneous Detection of Multiple Metal Ions.

Aiming at the problems of low accuracy and large prediction errors caused by the serious overlap of multi-metal spectral signals in zinc smelting industrial wastewater, a characteristic interval modeling method is proposed. First, according to the absorption spectra of mixed solution, the characteristic intervals of copper and nickel are preliminarily screened by using different partition lengths. Second, take the smallest root mean squares error of cross validation and the largest correlation coefficient as the evaluation indicators, compare the full-spectral model and each local model, and select the optimal feature sub-intervals of copper and nickel. Last, the partial least squares method is used to model the combined wavelengths of the optimal sub-intervals to realize the simultaneous detection of copper and nickel. The linear determination ranges are 0.3-3.0 mg/L for copper and nickel. the correlation coefficients of copper and nickel are 0.9974 and 0.9966, respectively. The results show that the method reduces the complexity of the wavelength variable screening process, improves the accuracy of the model, and lays the foundation for the accurate analysis of polymetallic ions in zinc smelting industrial wastewater.

Short Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images.

This paper is concerned with the problem of short circuit detection in infrared image for metal electrorefining with an improved Faster Region-based Convolutional Neural Network (Faster R-CNN). To address the problem of insufficient label data, a framework for automatically generating labeled infrared images is proposed. After discussing factors that affect sample diversity, background, object shape, and gray scale distribution are established as three key variables for synthesis. Raw infrared images without fault are used as backgrounds. By simulating the other two key variables on the background, different classes of objects are synthesized. To improve the detection rate of small scale targets, an attention module is introduced in the network to fuse the semantic segment results of U-Net and the synthetic dataset. In this way, the Faster R-CNN can obtain rich representation ability about small scale object on the infrared images. Strategies of parameter tuning and transfer learning are also applied to improve the detection precision. The detection system trains on only synthetic dataset and tests on actual images. Extensive experiments on different infrared datasets demonstrate the effectiveness of the synthetic methods. The synthetically trained network obtains a mAP of 0.826, and the recall rate of small latent short circuit is superior to that of Faster R-CNN and U-Net, effectively avoiding short-circuit missed detection.

A Nonlinear Integrated Modeling Method of Extended Kalman Filter Based on Adaboost Algorithm.

In the zinc hydrometallurgical purification process, the concentration ratio of zinc ion to trace nickel ion is as high as 105, so that the nickel spectral signal is completely covered by high concentration zinc signal, resulting in low sensitivity and nonlinear characteristics of nickel spectral signal. Aiming at the problem that it is difficult to detect nickel in zinc sulfate solution, this paper proposes a nonlinear integrated modeling method of extended Kalman filter based on Adaboost algorithm. First, a non-linear nickel model is established based on nickel standard solution. Second, an extended Kalman filter wavelength optimization method based on correlation coefficient is proposed to select wavelength variables with high signal sensitivity, large amount of information and strong nonlinear correlation. Finally, a nonlinear integrated modeling method based on Adaboost algorithm is proposed, which uses extended Kalman filter as a basic submodel, and realizes the stable detection of trace nickel through the weighted combination of multiple basic models. The results show that the average relative error of this method for detecting nickel is 4.56%, which achieves accurate detection of trace nickel in zinc sulfate solution.

Guizhi Fuling Wan reduces autophagy of granulosa cell in rats with polycystic ovary syndrome via restoring the PI3K/AKT/mTOR signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Guizhi Fuling Wan (GFW) is a traditional Chinese medicine used to remove blood stasis and dissipate phlegm for treating gynecological diseases that was invented by Zhang Zhongjing in the Eastern Han dynasty. In recent years, GFW has been widely used to treat patients with polycystic ovary syndrome (PCOS). Clinical and animal studies have shown that it is effective in the treatment of PCOS, but its mechanism is unknown. Generally, it works by regulating autophagy via the PI3K/AKT/mTOR signaling pathway. AIM OF THE STUDY: This study investigated the effects and mechanism of GFW in PCOS rats with insulin resistance (IR) in order to provide better understanding of its observed clinical effects and a theoretical basis for the study of traditional Chinese medicine. MATERIALS AND METHODS: Eighty-four female Sprague-Dawley rats were randomly divided into seven groups (n = 12 per group): 1) control, 2) PCOS model, 3) low-dose GFW, 4) medium-dose GFW, 5) high-dose GFW, 6) metformin, and 7) medium-dose GFW plus LY294002. In all non-control groups, we induced PCOS through daily letrozole combined with intragastric high-fat emulsion for 21 days. After treatment, rats were sacrificed and serum follicle-stimulating hormone (FSH), testosterone (T), progesterone, luteinizing hormone (LH), 17ß-estradiol, fasting insulin (FINS), and fasting plasma glucose levels were measured by enzyme-linked immunosorbent assay (ELISA). The LH/FSH ratios and HOMA-IR values were calculated. Ovarian morphology was observed by hematoxylin and eosin staining, and all follicles were counted under a microscope. MDC-positive vesicles were used as markers to detect autophagy, and the expression levels of p62, Beclin1, and LC3-II were examined by immunostaining. Western blotting was used to measure PI3K/AKT/mTOR pathway activation, granulosa cell apoptosis, and autophagy. RESULTS: Compared with the PCOS model group, GFW-treated rats had less atretic and cystic follicles, and more mature follicles and corpus lutea. The GFW-treated rats had lower serum T, LH, and FINS levels than the PCOS model group, as well as lower LH/FSH ratios and HOMA-IR values. GFW treatment resulted in significantly reduced levels of cleaved-Caspase-3, cleaved-Caspase-9, BAX, Beclin1, Atg5, and LC3-II. Phosphorylation of PI3K, AKT, and mTOR was significantly higher in GFW-treated rats compared with the PCOS model group. The phosphorylation of PI3K, AKT, and mTOR was decreased with the use of a PI3K antagonist. CONCLUSIONS: Our results indicate that GFW inhibited granulosa cell autophagy and promoted follicular development to attenuate ovulation disorder in PCOS-IR rats. This was associated with activation of the PI3K/AKT/mTOR signaling pathway.

Efficacy of coenzyme Q10 supplementation on glucose metabolism, lipid profiles, and biomarkers of inflammation in women with polycystic ovary syndrome: A protocol for a systematic review and meta-analysis.

BACKGROUND: Polycystic ovary syndrome (PCOS) is one of the common gynecological endocrine system diseases. It is characterized by excessive androgen, rare or anovulation, and polycystic ovary morphology. Coenzyme Q10 (CoQ10) is a fat-soluble natural vitamin, which has a continuous oxidation-reduction cycle and is an effective antioxidant that can protect ovaries from oxidative damage. This study aims to systematically summarize and analyze the scientific literatures on glucose metabolism index, lipid profiles, inflammatory factor, and sex hormone level of PCOS patients treated with CoQ10 to provide a reference basis for clinical treatment. METHODS: We will retrieve the following electronic databases from the built-in until March 2021: Cochrane Library, PubMed, EMBASE, Web of Science, China National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature Database (CBM), Clinical Trials. gov, Chinese Scientific Journal Database (VIP), and Wang-fang database. Two reviewers will independently scan the articles searched, de-duplication, filtering, quality assessment. Differences will be resolved by discussion between the 2 reviewers or by a third reviewers. All analyses were systematic to evaluate interventions based on the Cochrane handbook. Meta-analysis and/or subgroup analysis will be performed on the basis of the included studies. DISCUSSION: This review will be to investigate the efficacy of CoQ10 supplementation on glucose metabolism, lipid profiles, and biomarkers of inflammation in women with PCOS and provide a high-quality synthesis to assess whether CoQ10 is an effective and safe intervention for PCOS. The results of the analysis will be published in a scientific journal after peer-review. SYSTEMATIC REVIEW REGISTRATION: INPLASY 2020100013.

Efficacy of different forms of Guizhi Fuling Wan on reproduction and metabolism in women with polycystic ovary syndrome: A protocol for a meta-analysis of randomized controlled trials.

BACKGROUND: Polycystic ovary syndrome (PCOS), is a common endocrine disorder in women characterized by increased androgen levels, ovulatory dysfunction, and polycystic ovaries. Western medicine is widely used for the treatment of PCOS, but patient satisfaction is low, largely due to its associated gastrointestinal symptoms of nausea and diarrhea. Guizhi Fuling Wan (GFW) is a traditional Chinese medicine used to remove blood stasis and dissipate phlegm for treating gynecological diseases that was invented by Zhang Zhongjing in the Eastern Han dynasty. In recent years, GFW has been widely used to treat patients with PCOS. This study aims to assess the efficacy and safety of GFW in the treatment of PCOS through a systematic review and meta-analysis. METHODS: All randomized controlled trials connected with GFW targeting PCOS will be searched in the following electronic bibliographic databases from their earliest recorded publications to December 2020 without any language restrictions: MEDLINE, Embase, PubMed, Web of Science, China National Knowledge Infrastructure, Chinese Biological Medicine Database, Wan-fang data, Chinese Technical Periodicals, and other databases. The primary outcomes include Sex hormone levels, ovulation rate, pregnancy rate, and total effective rate. The secondary outcomes were Total cholesterol, triglyceride, low-density lipoprotein, high-density lipoprotein, fasting glucose, fasting insulin, insulin sensitivity index, body mass index, hypertrichosis score, acne score, adverse reactions, etc. Two reviewers will independently conduct cations retrieval, de-duplication, filtering, quality assessment, and data analysis by Endnote X9.1 and Review Manager software (RevMan V.5.3). Meta-analysis and/or subgroup analysis will be performed on the included data. DISCUSSION: This study will investigate the application of GFW in the treatment or prevention of PCOS, and provide a high-quality synthesis to judge whether GFW is an effective and safe intervention for PCOS. PROSPERO REGISTRATION NUMBER: CRD42020192405.

Guizhi Fuling Wan, Chinese Herbal Medicine, Ameliorates Insulin Sensitivity in PCOS Model Rats With Insulin Resistance via Remodeling Intestinal Homeostasis.

Polycystic ovary syndrome (PCOS) is a common endocrine disease with reproductive dysfunction and metabolic disorder in women of childbearing age. Gastrointestinal microbiome contributes to PCOS through mediating insulin resistance. Guizhi Fuling Wan, Chinese herbal medicine, can treat PCOS with insulin resistance (PCOS-IR), but the underlying mechanism is not clear. The aim of this study was to characterize the exact mechanism of Guizhi Fuling Wan action and whether it is related to the regulation of intestinal flora structure. We induced PCOS-IR rat model by means of letrozole sodium carboxymethyl cellulose (CMC-na) solution combined with high-fat emulsion administration and randomly divided it into blank control group (K), model control group (M), low dose of Guizhi Fuling Wan group (D), middle dose of Guizhi Fuling Wan group (Z), high dose of Guizhi Fuling Wan group (G) and positive drug (Metformin) control group (Y). After 36 days of modeling and treatment, serum and stool samples from all rats were collected for a follow-up analysis. The data display that, compared with K group, elevated testosterone and HOMA-IR, turbulent estrous cycles and polycystic ovaries in M group, indicating the PCOS-IR rat model is successfully established. Increased fasting insulin is associated with higher inflammation(plasma TNF-α, IL-6, and HS-CPR concentration were determined) in M group, and the altered intestinal flora (compared with the K group, in M group the relative abundance of Alloprevotella was decreased significantly, while the relative abundance of Lachnospiraceae UCG-008, Lachnospiraceae NK4A136, Lactobacillus, Ruminiclostridium 9, and Ruminococcaceae UCG-003 was increased significantly) induced the secretion of inflammatory markers. On the other hand, Guizhi Fuling Wan can alleviate inflammation, improve insulin resistence: Lower inflammation decreased fasting insulin can be seen in G group compared with M group, this effect is related to the regulating effect of Guizhi Fuling Wan on intestinal flora (in G group, the relative abundance of Alloprevotella, Ruminococcaceae UCG-003, and Lachnospiraceae UCG-008 was increased significantly, compared with M group). This research demonstrates Guizhi Fuling Wan improve insulin resistance in polycystic ovary syndrome with the underlying mechanism of regulating intestinal flora to control inflammation. It would be useful to promote the therapeutic effect of Guizhi Fuling Wan on PCOS-IR.