Observation On Efficacy and Underlying Mechanism of Cheek acupuncture On Ovulation induction For infertile Women With Pcos: Case Series

RATIONALE: Polycystic ovary syndrome (PCOS) is the most common reproductive endocrine disorder among women of childbearing age and is the primary cause of anovulatory infertility, accounting for 70% to 80% of cases. Ovulation induction is the main treatment approach for infertile patients with PCOS. Commonly utilized medications for this purpose are clomiphene citrate (CC) and letrozole (LE). Clomiphene citrate administration results in an ovulation rate ranging from 60% to 85%, while the pregnancy rate is limited to 35% to 40%, and a further reduction is observed in live birth rates. Letrozole demonstrates a slightly higher pregnancy rate and live birth rate compared to clomiphene citrate, although challenges persist in terms of longer stimulation cycles, multiple pregnancies, and the risk of ovarian hyperstimulation syndrome (OHSS). Clinical reports indicate that acupuncture therapy shows promising efficacy in treating patients with PCOS-related infertility, despite a partially unclear understanding of its underlying mechanisms. PATIENT CONCERNS: In this study, one patient did not achieve pregnancy despite more than a year of ovulation induction using clomiphene citrate and letrozole. However, after 3 months of receiving cheek acupuncture therapy, she successfully conceived and gave birth to a liveborn baby. Another patient achieved natural conception and live birth after 2 months of exclusive cheek acupuncture therapy. DIAGNOSIS: PCOS. INTERVENTIONS: Cheek acupuncture therapy. OUTCOMES: Both of them successfully conceived and gave birth to a liveborn baby. LESSONS: These findings suggest that cheek acupuncture therapy can effectively stimulate follicle development and ovulation, potentially improving endometrial receptivity. According to holographic theory, there is a biologically holographic model within the cheek region that shares a homology with the human body structure. This model provides an explanation for the regulatory effects of cheek acupuncture point stimulation on the Hypothalamic-Pituitary-Ovarian axis (HPO), which subsequently influences follicle development and ovulation in patients. Consequently, when cheek acupuncture therapy is applied alone or in combination with ovulation induction medication, patients have the ability to achieve successful pregnancy and experience a smooth delivery

GJB2 mutation spectrum in 2063 Chinese patients with nonsyndromic hearing impairment

Background: Mutations in GJB2 are the most common molecular defects responsible for autosomal recessive nonsyndromic hearing impairment (NSHI). The mutation spectra of this gene vary among different ethnic groups. Methods: In order to understand the spectrum and frequency of GJB2 mutations in the Chinese population, the coding region of the GJB2 gene from 2063 unrelated patients with NSHI was PCR amplified and sequenced. Results: A total of 23 pathogenic mutations were identified. Among them, five (p.W3X, c.99delT, c.155_c.158delTCTG, c.512_c.513insAACG, and p.Y152X) are novel. Three hundred and seven patients carry two confirmed pathogenic mutations, including 178 homozygotes and 129 compound heterozygotes. One hundred twenty five patients carry only one mutant allele. Thus, GJB2 mutations account for 17.9% of the mutant alleles in 2063 NSHI patients. Overall, 92.6% (684/739) of the pathogenic mutations are frame-shift truncation or nonsense mutations. The four prevalent mutations; c.235delC, c.299_c.300delAT, c.176_c.191del16, and c.35delG, account for 88.0% of all mutantalleles identified. The frequency of GJB2 mutations (alleles) varies from 4% to 30.4% among different regions of China. It also varies among different sub-ethnic groups. Conclusion: In some regions of China, testing of the three most common mutations can identify at least one GJB2 mutant allele in all patients. In other regions such as Tibet, the three most common mutations account for only 16% the GJB2 mutant alleles. Thus, in this region, sequencing of GJB2 would be recommended. In addition, the etiology of more than 80% of the mutant alleles for NSHI in China remains to be identified. Analysis of other NSHI related genes will be necessary

A Novel Dual-Scale Deep Belief Network Method for Daily Urban Water Demand Forecasting

Water demand forecasting applies data supports for the scheduling and decision-making of urban water supply systems. In this study, a new dual-scale deep belief network (DSDBN) approach for daily urban water demand forecasting was proposed. Original daily water demand time series was decomposed into several intrinsic mode functions (IMFs) and one residue component with ensemble empirical mode decomposition (EEMD) technique. Stochastic and deterministic terms were reconstructed through analyzing the frequency characteristics of IMFs and residue using generalized Fourier transform. The deep belief network (DBN) model was used for prediction using the two feature terms. The outputs of the double DBNs are summed as the final forecasting results. Historical daily water demand datasets from an urban waterworks in Zhuzhou, China, were investigated by the proposed DSDBN model. The mean absolute percentage error (MAPE), normalized root-mean-square error (NRMSE), correlation coefficient (CC) and determination coefficient (DC) were used as evaluation criteria. The results were compared with the autoregressive integrated moving average (ARIMA) model, feed forward neural network (FFNN) model, support vector regression (SVR) model, EEMD and their combinations, and single DBN model. The results obtained in the test period indicate that the proposed model has the smallest MAPE and NRMSE values of 1.291099 and 0.016625, respectively, and the largest CC and DC values of 0.976528 and 0.953512, respectively. Therefore, the proposed DSDBN method is a useful tool for daily urban water demand forecasting and outperforms other models in common use

A Facile Fabrication of Supported Ni/SiO₂ Catalysts for Dry Reforming of Methane with Remarkably Enhanced Catalytic Performance

Ni catalysts supported on SiO2 are prepared via a facile combustion method. Both glycine fuel and ammonium nitrate combustion improver facilitate the formation of much smaller Ni nanoparticles, which give excellent activity and stability, as well as a syngas with a molar ratio of H2/CO of about 1:1 due to the minimal side reaction toward revserse water gas shift (RWGS) in CH4 dry reforming

Fatigue Crack Growth Characteristics of 34CrMo4 Steel for Gas Cylinders by Cold Flow Forming after Hot Drawing

The fatigue crack growth (FCG) behavior of 34CrMo4 steel, a typical material for gas cylinders, has been investigated. Specimens were taken from the base material (BM) as well as the hot-drawn (HD) cylinder and cold-flow (CF) formed cylinder along the longitudinal and transverse directions. The FCG tests were conducted under different stress ratios for different materials and directions. The main purpose of this research was to explore the influences of the mechanical and thermal processes, sampling direction and stress ratio on the FCG behavior of 34CrMo4 steel. To further reveal the mechanism of crack propagation at different stages, the microstructures and fracture modes of FCG specimens were analyzed by scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD), respectively. The results showed that HD and CF materials exhibited better resistance to fatigue crack propagation than BM. The FCG rates of investigated materials can be accelerated by the increase in stress ratio. However, the sampling direction had little effect on the FCG rate. Finally, a driving force parameter (DFP) model was used to fit the experimental FCG data of three materials with different mechanical and thermal processes. A unified transition stage between the stable and unstable FCG stages of three materials under various experimental conditions was revealed by DFP model, playing an important role on the early warning of fatigue fracture for different types of 34CrMo4 steel

Hourly Urban Water Demand Forecasting Using the Continuous Deep Belief Echo State Network

Effective and accurate water demand prediction is an important part of the optimal scheduling of a city water supply system. A novel deep architecture model called the continuous deep belief echo state network (CDBESN) is proposed in this study for the prediction of hourly urban water demand. The CDBESN model uses a continuous deep belief network (CDBN) as the feature extraction algorithm and an echo state network (ESN) as the regression algorithm. The new architecture can model actual water demand data with fast convergence and global optimization ability. The prediction capacity of the CDBESN model is tested using historical hourly water demand data obtained from an urban waterworks in Zhuzhou, China. The performance of the proposed model is compared with those of ESN, continuous deep belief neural network, and support vector regression models. The correlation coefficient (r2), normalized root-mean-square error (NRMSE), and mean absolute percentage error (MAPE) are adopted as assessment criteria. Forecasting results obtained in the testing stage indicate that the CDBESN model has the largest r2 value of 0.995912 and the smallest NRMSE and MAPE values of 0.027163 and 2.469419, respectively. The prediction accuracy of the proposed model clearly outperforms those of the models it is compared with due to the good feature extraction ability of CDBN and the excellent feature learning ability of ESN

Phytotoxicity and Accumulation of Copper-Based Nanoparticles in Brassica under Cadmium Stress

The widespread use of copper-based nanoparticles expands the possibility that they enter the soil combined with heavy metals, having a toxic effect and posing a threat to the safety of vegetables. In this study, single and combined treatments of 2 mg/L Cd, 20 mg/L Cu NPs and 20 mg/L CuO NPs were added into Hoagland nutrient solution by hydroponics experiments. The experimental results show that copper-based Nanoparticles (NPs) can increase the photosynthetic rate of plants and increase the biomass of Brassica. Cu NPs treatment increased the Superoxide Dismutase (SOD), Peroxidase (POD) and catalase (CAT) activities of Brassica, and both NPs inhibited ascorbate peroxidase (APX) activity. We observed that Cd + Cu NPs exhibited antagonistic effects on Cd accumulation, inhibiting it by 12.6% in leaf and 38.6% in root, while Cd + CuO NPs increased Cd uptake by 73.1% in leaves and 22.5% in roots of Brassica. The Cu content in the shoots was significantly negatively correlated with Cd uptake. The Cd content of each component in plant subcellular is soluble component > cytoplasm > cell wall. Cu NPs + Cd inhibited the uptake of Zn, Ca, Fe, Mg, K and Mn elements, while CuO NPs + Cd promoted the uptake of Mn and Na elements. The results show that copper-based nanoparticles can increase the oxidative damage of plants under cadmium stress and reduce the nutritional value of plants

2011): The role of EDTA on Cadmium phytoextraction in a Cadmiumhyperaccumulator Rorippa globosa

Enhanced phytoextraction technologies have been proposed as an effective approach to the decontamination of heavy metals in soils. In this study, the application of ethylene diamine tetraacetic acid (0.5 and 1.0 g/kg EDTA) at preflowering stage depressed Rorippa globosa growth and Cd uptake, the dry biomass, Cd concentration and total metal accumulation (TMC) of shoots at the concentration of 1.0 g/kg EDTA resulted in 39.6, 3.1 and 41.0% reduction, respectively, relative to the control. In contrast, when EDTA was added at flowering and mature stages, it facilitated plant production and Cd absorption. Especially for 1.0 g/kg EDTA applied at mature stage, the maximum of shoot dry biomass, Cd concentration, TMC and remediation ratio (RR) were obtained, which were 4.7 g/pot, 210.3 mg/kg, 982.4 µg/pot and 1.6, respectively. Therefore, the moderate concentration of EDTA (1.0 g/kg) applied at optimal growing stage (mature stage) of R. globosa was more effective in increasing phytoextraction of Cd from contaminated soils

The Effect of Clamping Force on the Wear Behavior of a Steam Generator Tube

Anti-vibration bars (AVBs) are essential components of a steam generator (SG) and are used to prevent steam generator tubes (SGTs) from vibrating intensely because of flow-induced vibration. However, the contact force generated at contact surfaces between AVBs and tubes can change the natural frequency and wear behavior of the tube. Contact force is represented by clamping force in this study. Considering the effect of the clamping force on the natural frequency and sliding distance of SGT, dynamic wear behavior under different clamping forces was analyzed based on the finite element method, and the natural frequency of the tube was measured in the present work. Moreover, the wear experiment was conducted at room temperature to verify the conclusions of dynamic behavior analysis. The increase in clamping force reduces the sliding distance of SGT, and wear depth affected by both clamping force and sliding distance also decreases