Meta analysis of the efficacy of western medicine combined with Qiliqiangxin capsule versus western medicine alone in the treatment of chronic heart failure

Objective: To compare the clinical efficacy of conventional Western medicine combined with Qiliqiangxin capsule and western medicine alone in the treatment of chronic heart failure, and to prove that Qiliqiangxin capsule combined treatment has more advantages, providing reference for clinical decision-making in the treatment of chronic heart failure. Methods: Randomized controlled trials (RCTs) of conventional Western medicine treatment and Western medicine combined with Qiliqiangxin capsule in the treatment of chronic heart failure were searched in databases such as PubMed, Embase, Webofscience, CNKI, WanFang, VIP, and CBM. The bias risk assessment was conducted using the RCT tool recommended by Cochrane, and then the meta-analysis was performed using RevMan5.4 and Stata17 software. Compare the efficacy evaluation of cardiac function, left ventricular ejection fraction (LVEF), left ventricular end diastolic diameter (LVEDD), cardiac stroke output (SV), 6-minute walking test (6MWT), and N-terminal proBNP in the conventional western medicine combined with Qiliqiangxin capsule group (hereinafter referred to as the treatment group) and the conventional western medicine group (hereinafter referred to as the control group). Results: A total of 20 RCTs meeting the criteria were included, including 2953 patients, including 1508 in the treatment group and 1445 in the control group. The results of meta-analysis showed that the treatment group had significantly better cardiac function evaluation, LVEF, LVEDD, SV, 6MWT, and NT-proBNP improvement than the control group. Its central functional efficacy evaluation (OR=2.09,95% CI: 1.71-2.55, P<0.001), LVEF (WMD=7.05,95% CI: 5.30-8.79, P<0.00001), LVEDD (WMD=6.73, 95% CI: 3.18-10.29, P=0.0002), SV (WMD=6.73, 95% CI: 3.18-10.29, P=0.0002), 6MWT (SMD=0.70,95% CI: 0.54-0.87, P<0.00001), NT-proBNP (SMD=-1.95,95% CI: -2.5 2 to 1.38 (P<0.0001), with statistically significant differences. Conclusion: Conventional western medicine combined with Qiliqiangxin capsule can significantly improve the clinical efficacy of heart failure, improve LVEF, LVEDD, SV, and NT-proBNP index, and improve exercise tolerance. It is worth using for reference in the treatment.

Remaining useful life prediction of aero-engines based on random-coefficient regression model considering random failure threshold

Remaining useful life(RUL)prediction is one of the most crucial components in prognostics and health management(PHM)of aero-engines.This paper proposes an RUL prediction method of aero-engines considering the randomness of failure threshold.Firstly,a random-coefficient regression(RCR)model is used to model the degradation process of aeroengines.Then,the RUL distribution based on fixed failure threshold is derived.The prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation(MLE)method and the random coefficient is updated in real time under the Bayesian framework.The failure threshold in this paper is defined by the actual degradation process of aeroengines.After that,a expectation maximization(EM)algorithm is proposed to estimate the underlying failure threshold of aeroengines.In addition,the conditional probability is used to satisfy the limitation of failure threshold.Then,based on above results,an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold(RFT)is derived in a closed-form.Finally,a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.

Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

Numerical investigations on mechanical characteristics and failure mechanism of outwash deposits based on random meso-structures using discrete element method

Outwash deposit is a unique type of geological materials, and its features such as heterogeneity, discontinuity and nonlinearity determine the complexity of mechanical characteristics and failure mechanism. In this work, random meso-structure of outwash deposits was constructed by the technique of computer random simulation based on characteristics of its meso-structure in the statistical sense and some simplifications, and a series of large direct shear tests on numerical samples of outwash deposits with stone contents of 15%, 30%, 45% and 60% were conducted using the discrete element method to further investigate its mechanical characteristics and failure mechanism under external load. The results show that the deformation characteristics and shear strength of outwash deposits are to some extent improved with the increase of stone content, and the shear stress–shear displacement curves of outwash deposits show great differences at the post-peak stage due to the random spatial distribution and content of stones. From the mesoscopic view, normal directions of contacts between "soil" and "stone" particles undergo apparent deflection as the shear displacement continues during the shearing process, accompanying redistribution of the magnitude of contact forces during the shearing process. For outwash deposits, the shear zone formed after shear failure is an irregular stripe due to the movements of stones near the shear zone, and it expands gradually with the increase of stone content. In addition, there is an approximately linear relation between the mean increment of internal friction angle and the stone content lying between 30% and 60%, and a concave nonlinear relation between the mean increment of cohesion and stone content, which are in good agreement with the existing research results.

Methods for predicting the remaining useful life of equipment in consideration of the random failure threshold

The value range of the failure threshold will generate an uncertain influence on the prediction results for the remaining useful life(RUL) of equipment. Most of the existing studies on the RUL prediction assume that the failure threshold is a fixed value,as they have difficulty in reflecting the random variation of the failure threshold. In connection with the inadequacies of the existing research, an in-depth analysis is carried out to study the effect of the random failure threshold(RFT) on the prediction results for the RUL. First, a nonlinear degradation model with unit-to-unit variability and measurement error is established based on the nonlinear Wiener process. Second, the expectation-maximization(EM) algorithm is used to solve the estimated values of the parameters of the prior degradation model, and the Bayesian method is used to iteratively update the posterior distribution of the random coefficients. Then, the effects of three types of RFT constraint conditions on the prediction results for the RUL are analyzed, and the probability density function(PDF) of the RUL is derived. Finally,the degradation data of aero-turbofan engines are used to verify the correctness and advantages of the method.

Vericiguat treatment of heart failure: A systematic review and metaanalysis

BACKGROUND Heart failure(HF),an end-stage manifestation of various cardiac diseases,poses an enormous economic and health burden on society.Vericiguat may be an effective drug in the treatment of HF.AIM To explore by meta-analysis the efficacy and safety of Vericiguat in treating chronic heart failure.METHODS Databases,including PubMed,EMBASE,Web of Science,and Cochrane Library,were searched to collect all published randomized controlled trials(RCTs)on Vericiguat treatment of chronic heart failure from the earliest electronic records to those published in March 2023.Two investigators independently screened the literature according to inclusion and exclusion criteria,evaluated the quality of the studies,and extracted valid data before conducting a meta-analysis using RevMan5.4.RESULTS Four RCTs with 5919 patients were included,and the meta-analysis showed that treatment with 10 mg Vericiguat reduced the incidence of the primary endpoint(a composite of cardiovascular mortality and first heart-failure-related hospital-ization)in patients with chronic heart failure compared to placebo[relative risk(RR)=0.91,95%confidence interval(CI):0.85–0.98,P=0.01],and reduced the incidence of heart-failure-related hospitalization(RR=0.92,95%CI:0.84–1.00,P=0.05).However,for the incidence of cardiovascular and all-cause death,there were no significant differences between the Vericiguat and placebo groups.In addition,the two groups did not show significant differences in blood pressure,heart rate,and Kansas Cardiomyopathy Questionnaire physical limitation score.In terms of safety,10 mg Vericiguat did not increase the risk of adverse effects in patients with chronic heart failure.Vericiguat may increase the risk of symp-tomatic hypotension(RR=1.17,95%CI:0.98–1.39,P=0.08)and syncope(RR=1.18,95%CI:0.90–1.55,P=0.24),but not significantly.CONCLUSION Vericiguat(10 mg)was more effective than placebo in treating patients with chronic heart failure and had a better safety profile.

Chinese herbal medicine for chronic heart failure:a multicenter,randomized,double-blind,placebo-controlled trial

Objective:The purpose of this study was to assess the efficacy and safety of Chinese herbal medicine(CHM)in the treatment of chronic heart failure(CHF)patients according to syndrome differentiation.Methods:In this multicenter,randomized,double-blind,placebo-controlled clinical trial,a total of 220 CHF patients were assigned to receive CHM or placebo granules without decoction according to syndrome differentiation in addition to their standard western treatment for 4 weeks.The change in the left ventricular ejection fraction(LVEF)was the primary outcome,and the changes in the TCM syndrome scores(TCM-SS)and New York Heart Association functional classification(NYHA-FC)were the secondary outcomes.

Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learning models to predict heart failure.The fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for prediction.Some supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best results.Some boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data set.Python and Scikit-learns are used for ML.Tensor Flow and Keras,along with Python,are used for ANN model train-ing.The DT and RF algorithms achieved the highest accuracy of 95%among the classifiers.Meanwhile,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.

Remaining useful life estimation based on Wiener degradation processes with random failure threshold

Remaining useful life(RUL) estimation based on condition monitoring data is central to condition based maintenance(CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the failure threshold has not been studied thoroughly. In this work, by using the truncated normal distribution to model random failure threshold(RFT), an analytical and closed-form RUL distribution based on the current observed data was derived considering the posterior distribution of the drift parameter. Then, the Bayesian method was used to update the prior estimation of failure threshold. To solve the uncertainty of the censored in situ data of failure threshold, the expectation maximization(EM) algorithm is used to calculate the posteriori estimation of failure threshold. Numerical examples show that considering the randomness of the failure threshold and updating the prior information of RFT could improve the accuracy of real time RUL estimation.

Fault-tolerant topology in the wireless sensor networks for energy depletion and random failure

Nodes in the wireless sensor networks （WSNs） are prone to failure due to energy depletion and poor environment, which could have a negative impact on the normal operation of the network. In order to solve this problem, in this paper, we build a fault-tolerant topology which can effectively tolerate energy depletion and random failure. Firstly, a comprehensive failure model about energy depletion and random failure is established. Then an improved evolution model is presented to generate a fault-tolerant topology, and the degree distribution of the topology can be adjusted. Finally, the relation between the degree distribution and the topological fault tolerance is analyzed, and the optimal value of evolution model parameter is obtained. Then the target fault-tolerant topology which can effectively tolerate energy depletion and random failure is obtained. The performances of the new fault tolerant topology are verified by simulation experiments. The results show that the new fault tolerant topology effectively prolongs the network lifetime and has strong fault tolerance.

Immediate Effect of Electro-acupuncture on Endometrial Blood Flow in Patients with Recurrent Implantation Failure:A Randomized Controlled Trial

Objective To investigate the immediate effects of electro-acupuncture(EA)on endometrial blood flow among recurrent implantation failure(RIF)patients.Methods Eighty RIF patients,enrolled from March 2022 to December 2022,were randomly allocated into either the EA group(40 cases)or the waiting-list(WL)group(40 cases)by using a random number table.The EA group underwent acupuncture at points of Shenting(GV 24),Baihui(GV 4),Benshen(GB 13),bilateral Zigong(EX-CA 1),Huangshu(KI 16),Sanyinjiao(SP 6)and Xuehai(SP10),and electric acupuncture apparatus was connected to EX-CA 1,KI 16,SP 6,and SP 10 with disperse-dense waves at 4/20 Hz frequencies for 30 min after transvaginal ultrasound,while the WL group received no intervention.The primary outcome measured was the endometrial volume blood flow.The secondary outcomes included the bilateral uterine artery index,endometrial volume,endometrial blood flow type,vascular distribution index(VI^(MV))for endometrial and ovary,clinical pregnancy rate,and embryo implantation rate.Results In the EA group,there was a notable decrease in the bilateral pulsatility index and a significant improvement in the endometrial blood flow type post-EA(P<0.05).Both the endometrial blood flow type and VI^(MV) for the endometrium and right ovary were markedly higher in the EA group compared to the WL group post-treatment(P<0.05).Conversely,no significant disparities were observed in vascular index,flow index,vascular blood flow index,uterine arterial blood flow indices,endometrial volume,clinical pregnancy rate and embryo implantation rate between the two groups after treatment(P>0.05).Besides,no adverse events related to EA were observed.Conclusions EA can promptly ameliorate VI^(MV) for the endometrial and right ovary,and endometrial blood flow type.Future randomized controlled trials are warranted to investigate the long-term effects of EA on blood flow of RIF patients and its implications for pregnancy outcomes.(Trial registration No.ChiCTR2200057377).

Optimal Production Control of Hybrid Manufacturing/Remanufacturing Failure-Prone Systems under Diffusion-Type Demand

The problem of production control for a hybrid manufacturing/remanufacturing system under uncertainty is analyzed. Two sources of uncertainty are considered: machines are subject to random breakdowns and repairs, and demand level is modeled as a diffusion type stochastic process. Contrary to most of studies where the demand level is considered constant and fewer results where the demand is modeled as a Poisson process with few discrete levels and exponentially distributed switching time, the demand is modeled here as a diffusion type process. In particular Wiener and Ornstein-Uhlenbeck processes for cumulative demands are analyzed. We formulate the stochastic control problem and develop optimality conditions for it in the form of Hamilton-Jacobi-Bellman (HJB) partial differential equations (PDEs). We demonstrate that HJB equations are of the second order contrary to the case of constant demand rate (corresponding to the average demand in our case), where HJB equations are linear PDEs. We apply the Kushner-type finite difference scheme and the policy improvement procedure to solve HJB equations numerically and show that the optimal production policy is of hedging-point type for both demand models we have introduced, similarly to the known case of a constant demand. Obtained results allow to compute numerically the optimal production policy in hybrid manufacturing/ remanufacturing systems taking into account the demand variability, and also show that Kushner-type discrete scheme can be successfully applied for solving underlying second order HJB equations.

Probabilistic assessment of slope failure considering anisotropic spatial variability of soil properties

Anisotropic spatial variability of soil properties is frequently encountered in geotechnical engineering practice due to the complex depositional process.To quantitatively evaluate the response of slope failure related to anisotropic spatial variability of soil properties and reveal the underlying influence of anisotropic spatial variability of soil properties on the slope reliability,this study integrates the random finite difference method(RFDM)into a probabilistic assessment framework and adopts general spatial variability and a cohesive-frictional soil slope example for illustration.A parametric analysis is carried out to investigate the influence of general anisotropic spatial variability of soil properties on slope failure probability and failure characteristics.The results show that the directional angles of scales of fluctuation of general anisotropic spatial variability significantly affect the slope failure probability.The dominant failure mode is the intermediate type in most cases of general anisotropic spatial variability,which is distinguished from the shallow failure mode occurring in the homogenous state.Overestimation of cross-correlation between c and u(qc;u),scales of fluctuation(dmax and dmin)in general anisotropic spatially variable soils significantly influences the average slip mass volumes of deep and multi-slip failure mode.Compared with transverse anisotropic spatial variability,general anisotropic spatial variability significantly ampli-fies the effects of qc;u,dmax and dmin on slope reliability.

Flap failure prediction in microvascular tissue reconstruction using machine learning algorithms

BACKGROUND Microvascular tissue reconstruction is a well-established,commonly used technique for a wide variety of the tissue defects.However,flap failure is associated with an additional hospital stay,medical cost burden,and mental stress.Therefore,understanding of the risk factors associated with this event is of utmost importance.AIM To develop machine learning-based predictive models for flap failure to identify the potential factors and screen out high-risk patients.METHODS Using the data set of 946 consecutive patients,who underwent microvascular tissue reconstruction of free flap reconstruction for head and neck,breast,back,and extremity,we established three machine learning models including random forest classifier,support vector machine,and gradient boosting.Model performances were evaluated by the indicators such as area under the curve of receiver operating characteristic curve,accuracy,precision,recall,and F1 score.A multivariable regression analysis was performed for the most critical variables in the random forest model.RESULTS Post-surgery,the flap failure event occurred in 34 patients(3.6%).The machine learning models based on various preoperative and intraoperative variables were successfully developed.Among them,the random forest classifier reached the best performance in receiver operating characteristic curve,with an area under the curve score of 0.770 in the test set.The top 10 variables in the random forest were age,body mass index,ischemia time,smoking,diabetes,experience,prior chemotherapy,hypertension,insulin,and obesity.Interestingly,only age,body mass index, and ischemic time were statistically associated with the outcomes.CONCLUSIONMachine learning-based algorithms, especially the random forest classifier, were very important incategorizing patients at high risk of flap failure. The occurrence of flap failure was a multifactordrivenevent and was identified with numerous factors that warrant further investigation.Importantly, the successful application of machine learning models may help the clinician indecision-making, understanding the underlying pathologic mechanisms of the disease, andimproving the long-term outcome of patients.

Numerical Study on the Probability of Fatigue Failure of a Semi-Submerged Platform Structure

On the basis of Miner's linear cumulative damage theorem, random variables are introduced to evaluate objectively the fatigue damage of a semi-submerged platform structure and a method is presented to analyze the fatigue reliability of the structure in its design life. The reliability of the method is verified through numerical examples and some conclusions are drawn, which have certain guiding and reference value for design and inspection.

Analysis of functional failure mode of commercial deep sub-micron SRAM induced by total dose irradiation

Functional failure mode of commercial deep sub-micron static random access memory（SRAM） induced by total dose irradiation is experimentally analyzed and verified by circuit simulation. We extensively characterize the functional failure mode of the device by testing its electrical parameters and function with test patterns covering different functional failure modes. Experimental results reveal that the functional failure mode of the device is a temporary function interruption caused by peripheral circuits being sensitive to the standby current rising. By including radiation-induced threshold shift and off-state leakage current in memory cell transistors, we simulate the influence of radiation on the functionality of the memory cell. Simulation results reveal that the memory cell is tolerant to irradiation due to its high stability, which agrees with our experimental result.

Simulation of failure process of jointed rock

A modified discontinuous deformation analysis (DDA) algorithm was proposed to simulate the failure behavior of jointed rock. In the proposed algorithm, by using the Monte-Carlo technique, random joint network was generated in the domain of interest. Based on the joint network, the triangular DDA block system was automatically generated by adopting the advanced front method. In the process of generating blocks, numerous artificial joints came into being, and once the stress states at some artificial joints satisfy the failure criterion given beforehand, artificial joints will turn into real joints. In this way, the whole fragmentation process of rock mass can be replicated. The algorithm logic was described in detail, and several numerical examples were carried out to obtain some insight into the failure behavior of rock mass containing random joints. From the numerical results, it can be found that the crack initiates from the crack tip, the growth direction of the crack depends upon the loading and constraint conditions, and the proposed method can reproduce some complicated phenomena in the whole process of rock failure.

Estimation Based on Progressive First-Failure Censored Sampling with Binomial Removals

In this paper, the inference for the Burr-X model under progressively first-failure censoring scheme is discussed. Based on this new censoring were the number of units removed at each failure time has a discrete binomial distribution. The maximum likelihood, Bootstrap and Bayes estimates for the Burr-X distribution are obtained. The Bayes estimators are obtained using both the symmetric and asymmetric loss functions. Approximate confidence interval and highest posterior density interval (HPDI) are discussed. A numerical example is provided to illustrate the proposed estimation methods developed here. The maximum likelihood and the different Bayes estimates are compared via a Monte Carlo simulation study.

真武汤加减治疗慢性心力衰竭合并利尿剂抵抗的Meta分析

目的系统评价真武汤加减联合西药常规治疗慢性心力衰竭(Chronic heart failure,CHF)合并利尿剂抵抗(Diuretic resistance,DR)的临床疗效。方法检索自建库起至2023年11月1日中国知网、万方数据库、维普数据库、中国生物医学文献数据库、PubMed、Cochrane Library、Embase、Web of Science、Clinical Trials、中国临床试验注册中心等数据库中真武汤加减治疗CHF合并DR的随机对照试验,采用Review Manager 5.4软件对文献数据进行Meta分析。结果共纳入15篇文献,包括1187例患者。Meta分析结果显示,与单纯西药对照组相比,真武汤加减联合西药常规治疗组患者在24 h总尿量[n=1058,MD=397.30,95%CI(269.44,525.16),P<0.01]、临床总有效率[n=625,RR=1.24,95%CI(1.15,1.34),P<0.01]、左心室射血分数(Left ventricular ejection fractions,LVEF)[n=697,MD=4.32,95%CI(2.51,6.112),P<0.01]、氨基末端脑利钠肽前体(N-terminal pro-B-type natriuretic peptide,NT-proBNP)水平[n=282,MD=734.4,95%CI(828.93,639.87),P<0.01]、外周血IL-6水平[n=170,MD=-5.70,95%CI(-7.23,-4.16),P<0.01]、外周血TNF-α水平[n=170,MD=-12.79,95%CI(-15.53,-10.04),P<0.01]方面均有明显改善,而左室舒张末内径未见明显改善[n=266,MD=-4.50,95%CI(-9.36,0.35),P=0.07]。药物安全性良好,加载中药治疗可显著降低低钾血症发生率[n=177,RR=0.32,95%CI(0.18,0.57),P<0.01]。结论在单纯西药治疗基础上,予真武汤加减可改善CHF合并DR患者24 h尿量、提高临床总有效率、改善患者心功能、降低炎症因子水平,且安全性较好。