An Overview of Sequential Approximation in Topology Optimization of Continuum Structure

This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures.These structures,commonly encountered in engineering applications,often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables.As a result,sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges.Over the past several decades,topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds.In comparison to the large-scale equation solution,sensitivity analysis,graphics post-processing,etc.,the progress of the sequential approximation functions and their corresponding optimizersmake sluggish progress.Researchers,particularly novices,pay special attention to their difficulties with a particular problem.Thus,this paper provides an overview of sequential approximation functions,related literature on topology optimization methods,and their applications.Starting from optimality criteria and sequential linear programming,the other sequential approximate optimizations are introduced by employing Taylor expansion and intervening variables.In addition,recent advancements have led to the emergence of approaches such as Augmented Lagrange,sequential approximate integer,and non-gradient approximation are also introduced.By highlighting real-world applications and case studies,the paper not only demonstrates the practical relevance of these methods but also underscores the need for continued exploration in this area.Furthermore,to provide a comprehensive overview,this paper offers several novel developments that aim to illuminate potential directions for future research.

The occurrence characteristics of oil in shales matrix from organic geochemical screening data and pore structure properties:An experimental study Author links open overlay panel

The occurrence characteristics of shale oil are of great significance to the movability of shale oil.In this study,the occurrence characteristics of oil in the shale matrix at Funing Formation shale in Subei Basin were quantitatively evaluated by organic geochemistry and microscopic pore structure characterization experiments.The Multiple Isothermal Stages Pyrolysis(MIS)experiment results show that the content of total oil,adsorbed oil,and free oil in the shales are 3.15-11.25 mg/g,1.41-4.95 mg/g,and 1.74-6.51 mg/g,respectively.among which the silicon-rich shale has the best oil-bearing.The relative content of free oil shows an increasing trend in pores with pore diameters greater than 3 nm.When the relative content of free oil reaches 100%,the pore size of silicon-rich shale is about 200 nm,while that of calcium-rich shale,clay-rich shale,and siliceous mixed shale is about 10 nm.The occurrence law of adsorbed oil is opposite to that of free oil,which indicates that shale oil will occur in the pores and fractures in a free state in a more extensive pore size range(>200 nm).This study also enables us to further understand the occurrence characteristics of shale oil under the interaction of occurrence state and occurrence space.

Optimized sequential therapy vs 10- and 14-d concomitant therapy for eradicating Helicobacter pylori: A randomized clinical trial

BACKGROUND A cure for Helicobacter pylori(H.pylori)remains a problem of global concern.The prevalence of antimicrobial resistance is widely rising and becoming a challenging issue worldwide.Optimizing sequential therapy seems to be one of the most attractive strategies in terms of efficacy,tolerability and cost.The most common sequential therapy consists of a dual therapy[proton-pump inhibitors(PPIs)and amoxicillin]for the first period(5 to 7 d),followed by a triple therapy for the second period(PPI,clarithromycin and metronidazole).PPIs play a key role in maintaining a gastric pH at a level that allows an optimal efficacy of antibiotics,hence the idea of using new generation molecules.This open-label prospective study randomized 328 patients with confirmed H.pylori infection into three groups(1:1:1):The first group received quadruple therapy consisting of twice-daily(bid)omeprazole 20 mg,amoxicillin 1 g,clarith-romycin 500 mg and metronidazole 500 mg for 10 d(QT-10),the second group received a 14 d quadruple therapy following the same regimen(QT-14),and the third group received an optimized sequential therapy consisting of bid rabe-prazole 20 mg plus amoxicillin 1 g for 7 d,followed by bid rabeprazole 20 mg,clarithromycin 500 mg and metronidazole 500 mg for the next 7 d(OST-14).AEs were recorded throughout the study,and the H.pylori eradication rate was determined 4 to 6 wk after the end of treatment,using the 13C urea breath test.RESULTS In the intention-to-treat and per-protocol analysis,the eradication rate was higher in the OST-14 group compared to the QT-10 group:(93.5%,85.5%P=0.04)and(96.2%,89.5%P=0.03)respectively.However,there was no statist-ically significant difference in eradication rates between the OST-14 and QT-14 groups:(93.5%,91.8%P=0.34)and(96.2%,94.4%P=0.35),respectively.The overall incidence of AEs was significantly lower in the OST-14 group(P=0.01).Furthermore,OST-14 was the most cost-effective among the three groups.CONCLUSION The optimized 14-d sequential therapy is a safe and effective alternative.Its eradication rate is comparable to that of the 14-d concomitant therapy while causing fewer AEs and allowing a gain in terms of cost.

Enhancement of vertical phase separation in sequentially deposited organic photovoltaics through the independent processing of additives

Herein,the impact of the independent control of processing additives on vertical phase separation in sequentially deposited (SD) organic photovoltaics (OPVs) and its subsequent effects on charge carrier kinetics at the electron donor-acceptor interface are investigated.The film morphology exhibits notable variations,significantly depending on the layer to which 1,8-diiodooctane (DIO) was applied.Grazing incidence wide-angle X-ray scattering analysis reveals distinctly separated donor/acceptor phases and vertical crystallinity details in SD films.Time-of-flight secondary ion mass spectrometry analysis is employed to obtain component distributions in diverse vertical phase structures of SD films depending on additive control.In addition,nanosecond transient absorption spectroscopy shows that DIO control significantly affects the dynamics of separated charges in SD films.In SD OPVs,DIO appears to act through distinct mechanisms with minimal restriction,depending on the applied layer.This study emphasizes the significance of morphological optimization in improving device performance and underscores the importance of independent additive control in the advancement of OPV technology.

Learning Dual-Layer User Representation for Enhanced Item Recommendation

User representation learning is crucial for capturing different user preferences,but it is also critical challenging because user intentions are latent and dispersed in complex and different patterns of user-generated data,and thus cannot be measured directly.Text-based data models can learn user representations by mining latent semantics,which is beneficial to enhancing the semantic function of user representations.However,these technologies only extract common features in historical records and cannot represent changes in user intentions.However,sequential feature can express the user’s interests and intentions that change time by time.But the sequential recommendation results based on the user representation of the item lack the interpretability of preference factors.To address these issues,we propose in this paper a novel model with Dual-Layer User Representation,named DLUR,where the user’s intention is learned based on two different layer representations.Specifically,the latent semantic layer adds an interactive layer based on Transformer to extract keywords and key sentences in the text and serve as a basis for interpretation.The sequence layer uses the Transformer model to encode the user’s preference intention to clarify changes in the user’s intention.Therefore,this dual-layer user mode is more comprehensive than a single text mode or sequence mode and can effectually improve the performance of recommendations.Our extensive experiments on five benchmark datasets demonstrate DLUR’s performance over state-of-the-art recommendation models.In addition,DLUR’s ability to explain recommendation results is also demonstrated through some specific cases.

Proposal for sequential Stern-Gerlach experiment with programmable quantum processors

The historical significance of the Stern–Gerlach(SG)experiment lies in its provision of the initial evidence for space quantization.Over time,its sequential form has evolved into an elegant paradigm that effectively illustrates the fundamental principles of quantum theory.To date,the practical implementation of the sequential SG experiment has not been fully achieved.In this study,we demonstrate the capability of programmable quantum processors to simulate the sequential SG experiment.The specific parametric shallow quantum circuits,which are suitable for the limitations of current noisy quantum hardware,are given to replicate the functionality of SG devices with the ability to perform measurements in different directions.Surprisingly,it has been demonstrated that Wigner’s SG interferometer can be readily implemented in our sequential quantum circuit.With the utilization of the identical circuits,it is also feasible to implement Wheeler’s delayed-choice experiment.We propose the utilization of cross-shaped programmable quantum processors to showcase sequential experiments,and the simulation results demonstrate a strong alignment with theoretical predictions.With the rapid advancement of cloud-based quantum computing,such as BAQIS Quafu,it is our belief that the proposed solution is well-suited for deployment on the cloud,allowing for public accessibility.Our findings not only expand the potential applications of quantum computers,but also contribute to a deeper comprehension of the fundamental principles underlying quantum theory.

Exploring the feasibility of prestressed anchor cables as an alternative to temporary support in the excavation of super-large-span tunnel

Excavating super-large-span tunnels in soft rock masses presents significant challenges.To ensure safety,the sequential excavation method is commonly adopted.It utilizes internal temporary supports to spatially partition the tunnel face and divide the excavation into multiple stages.However,these internal supports generally impose spatial constraints,limiting the use of large-scale excavation equipment and reducing construction efficiency.To address this constraint,this study adopts the“Shed-frame”principle to explore the feasibility of an innovative support system,which aims to replace internal supports with prestressed anchor cables and thus provide a more spacious working space with fewer internal obstructions.To evaluate its effectiveness,a field case involving the excavation of a 24-m span tunnel in soft rock is presented,and an analysis of extensive field data is conducted to study the deformation characteristics of the surrounding rock and the mechanical behavior of the support system.The results revealed that prestressed anchor cables integrated the initial support with the shed,creating an effective“shed-frame”system,which successively maintained tunnel deformation and frame stress levels within safe regulatory bounds.Moreover,the prestressed anchor cables bolstered the surrounding rock effectively and reduced the excavation-induced disturbance zone significantly.In summary,the proposed support system balances construction efficiency and safety.These field experiences may offer valuable insights into the popularization and further development of prestressed anchor cable support systems.

A deep Koopman operator-based modelling approach for long-term prediction of dynamics with pixel-level measurements

Although previous studies have made some clear leap in learning latent dynamics from high-dimensional representations,the performances in terms of accuracy and inference time of long-term model prediction still need to be improved.In this study,a deep convolutional network based on the Koopman operator(CKNet)is proposed to model non-linear systems with pixel-level measurements for long-term prediction.CKNet adopts an autoencoder network architecture,consisting of an encoder to generate latent states and a linear dynamical model(i.e.,the Koopman operator)which evolves in the latent state space spanned by the encoder.The decoder is used to recover images from latent states.According to a multi-step ahead prediction loss function,the system matrices for approximating the Koopman operator are trained synchronously with the autoencoder in a mini-batch manner.In this manner,gradients can be synchronously transmitted to both the system matrices and the autoencoder to help the encoder self-adaptively tune the latent state space in the training process,and the resulting model is time-invariant in the latent space.Therefore,the proposed CKNet has the advantages of less inference time and high accuracy for long-term prediction.Experiments are per-formed on OpenAI Gym and Mujoco environments,including two and four non-linear forced dynamical systems with continuous action spaces.The experimental results show that CKNet has strong long-term prediction capabilities with sufficient precision.

Sequential Inverse Optimal Control of Discrete-Time Systems

This paper presents a novel sequential inverse optimal control(SIOC)method for discrete-time systems,which calculates the unknown weight vectors of the cost function in real time using the input and output of an optimally controlled discrete-time system.The proposed method overcomes the limitations of previous approaches by eliminating the need for the invertible Jacobian assumption.It calculates the possible-solution spaces and their intersections sequentially until the dimension of the intersection space decreases to one.The remaining one-dimensional vector of the possible-solution space’s intersection represents the SIOC solution.The paper presents clear conditions for convergence and addresses the issue of noisy data by clarifying the conditions for the singular values of the matrices that relate to the possible-solution space.The effectiveness of the proposed method is demonstrated through simulation results.

Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.

Exploring Sequential Feature Selection in Deep Bi-LSTM Models for Speech Emotion Recognition

Machine Learning(ML)algorithms play a pivotal role in Speech Emotion Recognition(SER),although they encounter a formidable obstacle in accurately discerning a speaker’s emotional state.The examination of the emotional states of speakers holds significant importance in a range of real-time applications,including but not limited to virtual reality,human-robot interaction,emergency centers,and human behavior assessment.Accurately identifying emotions in the SER process relies on extracting relevant information from audio inputs.Previous studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients(MFCCs)due to their ability to capture the periodic nature of audio signals effectively.Although these traits may improve their ability to perceive and interpret emotional depictions appropriately,MFCCS has some limitations.So this study aims to tackle the aforementioned issue by systematically picking multiple audio cues,enhancing the classifier model’s efficacy in accurately discerning human emotions.The utilized dataset is taken from the EMO-DB database,preprocessing input speech is done using a 2D Convolution Neural Network(CNN)involves applying convolutional operations to spectrograms as they afford a visual representation of the way the audio signal frequency content changes over time.The next step is the spectrogram data normalization which is crucial for Neural Network(NN)training as it aids in faster convergence.Then the five auditory features MFCCs,Chroma,Mel-Spectrogram,Contrast,and Tonnetz are extracted from the spectrogram sequentially.The attitude of feature selection is to retain only dominant features by excluding the irrelevant ones.In this paper,the Sequential Forward Selection(SFS)and Sequential Backward Selection(SBS)techniques were employed for multiple audio cues features selection.Finally,the feature sets composed from the hybrid feature extraction methods are fed into the deep Bidirectional Long Short Term Memory(Bi-LSTM)network to discern emotions.Since the deep Bi-LSTM can hierarchically learn complex features and increases model capacity by achieving more robust temporal modeling,it is more effective than a shallow Bi-LSTM in capturing the intricate tones of emotional content existent in speech signals.The effectiveness and resilience of the proposed SER model were evaluated by experiments,comparing it to state-of-the-art SER techniques.The results indicated that the model achieved accuracy rates of 90.92%,93%,and 92%over the Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS),Berlin Database of Emotional Speech(EMO-DB),and The Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets,respectively.These findings signify a prominent enhancement in the ability to emotional depictions identification in speech,showcasing the potential of the proposed model in advancing the SER field.

Mixed D-vine copula-based conditional quantile model for stochastic monthly streamflow simulation

Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.

Appropriate Combination of Crossover Operator and Mutation Operator in Genetic Algorithms for the Travelling Salesman Problem

Genetic algorithms(GAs)are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems.AsimpleGAbeginswith a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes.It uses a crossover operator to create better offspring chromosomes and thus,converges the population.Also,it uses a mutation operator to explore the unexplored areas by the crossover operator,and thus,diversifies the GA search space.A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution.However,appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem.In this present paper,we aim to study the benchmark traveling salesman problem(TSP).We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances.The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem.The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances.

Sequential neoadjuvant chemotherapy using pegylated liposomal doxorubicin and cyclophosphamide followed by taxanes with complete trastuzumab and pertuzumab treatment for HER2-positive breast cancer: A phase Ⅱ single-arm study

Objective: Despite cardiotoxicity overlap, the trastuzumab/pertuzumab and anthracycline combination remains crucial due to significant benefits. Pegylated liposomal doxorubicin(PLD), a less cardiotoxic anthracycline, was evaluated for efficacy and cardiac safety when combined with cyclophosphamide and followed by taxanes with trastuzumab/pertuzumab in human epidermal growth factor receptor-2(HER2)-positive early breast cancer(BC).Methods: In this multicenter, phase II study, patients with confirmed HER2-positive early BC received four cycles of PLD(30-35 mg/m^(2)) and cyclophosphamide(600 mg/m^(2)), followed by four cycles of taxanes(docetaxel,90-100 mg/m^(2) or nab-paclitaxel, 260 mg/m^(2)), concomitant with eight cycles of trastuzumab(8 mg/kg loading dose,then 6 mg/kg) and pertuzumab(840 mg loading dose, then 420 mg) every 3 weeks. The primary endpoint was total pathological complete response(tp CR, yp T0/is yp N0). Secondary endpoints included breast p CR(bp CR),objective response rate(ORR), disease control rate, rate of breast-conserving surgery(BCS), and safety(with a focus on cardiotoxicity).Results: Between May 27, 2020 and May 11, 2022, 78 patients were treated with surgery, 42(53.8%) of whom had BCS. After neoadjuvant therapy, 47 [60.3%, 95% confidence interval(95% CI), 48.5%-71.2%] patients achieved tp CR, and 49(62.8%) achieved bp CR. ORRs were 76.9%(95% CI, 66.0%-85.7%) and 93.6%(95% CI,85.7%-97.9%) after 4-cycle and 8-cycle neoadjuvant therapy, respectively. Nine(11.5%) patients experienced asymptomatic left ventricular ejection fraction(LVEF) reductions of ≥10% from baseline, all with a minimum value of >55%. No treatment-related abnormal cardiac function changes were observed in mean N-terminal pro-BNP(NT-pro BNP), troponin I, or high-sensitivity troponin.Conclusions: This dual HER2-blockade with sequential polychemotherapy showed promising activity with rapid tumor regression in HER2-positive BC. Importantly, this regimen showed an acceptable safety profile,especially a low risk of cardiac events, suggesting it as an attractive treatment approach with a favorable risk-benefit balance.

Embryo Transfer Strategies for Women with Recurrent Implantation Failure During the Frozen-thawed Embryo Transfer Cycles:Sequential Embryo Transfer or Double-blastocyst Transfer?

Objective Both sequential embryo transfer(SeET)and double-blastocyst transfer(DBT)can serve as embryo transfer strategies for women with recurrent implantation failure(RIF).This study aims to compare the effects of SeET and DBT on pregnancy outcomes.Methods Totally,261 frozen-thawed embryo transfer cycles of 243 RIF women were included in this multicenter retrospective analysis.According to different embryo quality and transfer strategies,they were divided into four groups:group A,good-quality SeET(GQ-SeET,n=38 cycles);group B,poor-quality or mixed-quality SeET(PQ/MQ-SeET,n=31 cycles);group C,good-quality DBT(GQ-DBT,n=121 cycles);and group D,poor-quality or mixed-quality DBT(PQ/MQ-DBT,n=71 cycles).The main outcome,clinical pregnancy rate,was compared,and the generalized estimating equation(GEE)model was used to correct potential confounders that might impact pregnancy outcomes.Results GQ-DBT achieved a significantly higher clinical pregnancy rate(aOR 2.588,95%CI 1.267–5.284,P=0.009)and live birth rate(aOR 3.082,95%CI 1.482–6.412,P=0.003)than PQ/MQ-DBT.Similarly,the clinical pregnancy rate was significantly higher in GQ-SeET than in PQ/MQ-SeET(aOR 4.047,95%CI 1.218–13.450,P=0.023).The pregnancy outcomes of GQ-SeET were not significantly different from those of GQ-DBT,and the same results were found between PQ/MQ-SeET and PQ/MQ-DBT.Conclusion SeET relative to DBT did not seem to improve pregnancy outcomes for RIF patients if the embryo quality was comparable between the two groups.Better clinical pregnancy outcomes could be obtained by transferring good-quality embryos,no matter whether in SeET or DBT.Embryo quality plays a more important role in pregnancy outcomes for RIF patients.

A highly sensitive ratiometric near-infrared nanosensor based on erbium-hyperdoped silicon quantum dots for iron(Ⅲ) detection

Ratiometric fluorescent detection of iron(Ⅲ)(Fe^(3+))offers inherent self-calibration and contactless analytic capabilities.However,realizing a dual-emission near-infrared(NIR)nanosensor with a low limit of detection(LOD)is rather challenging.In this work,we report the synthesis of water-dispersible erbium-hyperdoped silicon quantum dots(Si QDs:Er),which emit NIR light at the wavelengths of 810 and 1540 nm.A dual-emission NIR nanosensor based on water-dispersible Si QDs:Er enables ratiometric Fe^(3+)detection with a very low LOD(0.06μM).The effects of pH,recyclability,and the interplay between static and dynamic quenching mechanisms for Fe^(3+)detection have been systematically studied.In addition,we demonstrate that the nanosensor may be used to construct a sequential logic circuit with memory functions.

Learning Sequential and Structural Dependencies Between Nucleotides for RNA N6-Methyladenosine Site Identification

N6-methyladenosine(m6A)is an important RNA methylation modification involved in regulating diverse biological processes across multiple species.Hence,the identification of m6A modification sites provides valuable insight into the biological mechanisms of complex diseases at the post-transcriptional level.Although a variety of identification algorithms have been proposed recently,most of them capture the features of m6A modification sites by focusing on the sequential dependencies of nucleotides at different positions in RNA sequences,while ignoring the structural dependencies of nucleotides in their threedimensional structures.To overcome this issue,we propose a cross-species end-to-end deep learning model,namely CR-NSSD,which conduct a cross-domain representation learning process integrating nucleotide structural and sequential dependencies for RNA m6A site identification.Specifically,CR-NSSD first obtains the pre-coded representations of RNA sequences by incorporating the position information into single-nucleotide states with chaos game representation theory.It then constructs a crossdomain reconstruction encoder to learn the sequential and structural dependencies between nucleotides.By minimizing the reconstruction and binary cross-entropy losses,CR-NSSD is trained to complete the task of m6A site identification.Extensive experiments have demonstrated the promising performance of CR-NSSD by comparing it with several state-of-the-art m6A identification algorithms.Moreover,the results of cross-species prediction indicate that the integration of sequential and structural dependencies allows CR-NSSD to capture general features of m6A modification sites among different species,thus improving the accuracy of cross-species identification.

Efficacy and safety of Xileisan combined with mesalazine for ulcerative colitis:A meta-analysis and trial sequential analysis

BACKGROUND The benefits and risks of Xileisan(XLS)in the treatment of ulcerative colitis(UC)remain unclear.AIM The present study aimed to evaluate the efficacy and safety of the combination of XLS and mesalazine when treating UC.METHODS We searched eight databases for clinical trials evaluating the combination of XLS and mesalazine in the treatment of UC,up to January 2024.Meta-analysis and trial sequential analysis(TSA)were performed using Revman 5.3 and TSA 0.9.5.10 beta,respectively.RESULTS The present study included 13 clinical studies involving 990 patients,of which 501 patients received XLS combined with mesalazine while 489 patients received mesalazine alone.The meta-analysis showed that,in terms of efficacy,the combination of XLS and mesalazine significantly improved the clinical efficacy rate by 22%[risk ratio(RR)=1.22;95%CI:1.15–1.28;P<0.00001]and mucosal improvement rate by 25%(RR=1.25;95%CI:1.12–1.39;P=0.0001),while significantly reducing the duration of abdominal pain by 2.25 days[mean difference(MD)=-2.25;95%CI:-3.35 to-1.14;P<0.0001],diarrhea by 2.06 days(MD=-2.06;95%CI:-3.92 to-0.20;P=0.03),hematochezia by 2.32 days(MD=-2.32;95%CI:-4.02 to-0.62;P=0.008),tumor necrosis factor alpha by 16.25 ng/mL(MD=-16.25;95%CI:-20.48 to-12.01;P<0.00001),and interleukin-6 by 14.14 ng/mL(MD=-14.14;95%CI:-24.89 to-3.39;P=0.01).The TSA indicated conclusiveness in the meta-analysis of the efficacy endpoints.In terms of safety,the meta-analysis revealed that the combination of XLS and mesalazine did not increase the occurrence of total and gastrointestinal adverse events,abdominal distension,and erythema(P>0.05).The TSA showed non conclusive findings in the meta-analysis of the safety endpoints.Harbord’s test showed no publication bias(P=0.734).CONCLUSION Treatment with XLS alleviated the clinical symptoms,intestinal mucosal injury,and inflammatory response in patients with UC,while demonstrating good safety.

Effect of sequential nursing care combined with communication intervention on visual recovery and pain after cataract ultrasound emulsification

BACKGROUND Cataracts are a common ophthalmic disease and postoperative vision recovery is crucial to patient quality of life.Rational and efficient care models play an impor-tant role in promoting vision recovery.AIM To evaluate the clinical effectiveness of procedural nursing care combined with communication intervention in vision recovery after cataract ultrasound emulsi-fication.METHODS A randomized controlled study was conducted on 100 patients with cataracts who underwent ultrasound emulsification surgery.They were randomly assigned to an experimental group or a control group.The experimental group received procedural nursing combined with Connect,Introduce,Communicate,Ask,Respond,Exit(CICARE)communication intervention,whereas the control group received conventional nursing.The effectiveness of the nursing model was assessed by comparing differences in vision recovery,pain scores,and mental health status between the two groups.RESULTS It was found that over time the visual acuity of patients in both groups gradually recovered and patients in the experimental group had lower pain scores and superior mental health status than the control group(P<0.05).CONCLUSION Procedural nursing combined with CICARE communication intervention has positive effects on vision recovery in patients after cataract ultrasound emulsification.

Sequential experimental observation on the curative effect of Yingbupu decoction of Zhuang medicine on stage I and II acute kidney injury

Objective:To observe the clinical efficacy of the Zhuang medicine Yingbupu decoction on stage I and II acute kidney injury through sequential test.Methods:The open one-way qualitative response sequential design of experiments was adopted,and the patients with AKI in phase I and II who met the inclusion criteria were divided into the treatment group and the control group according to the order of hospitalization by random number table.On the basis of basic treatment,the treatment group was treated with Zhuang medicine Yingbupu decoction,and the control group was treated with Jinshuibao tablet.The clinical efficacy,TCM syndrome score,24 h urine volume,serum creatinine(Scr),microalbumin in urine(mAlb),neutrophil Gelatinase related lipid delivery albumin(NGAL)of the two groups were compared,and the adverse reactions and complications of the two groups were observed.Results:After 14 d of treatment,when the treatment group reached the 10th case,the experimental line contacted the upper bound U-line and reached the experimental standard to terminate the experiment.The effective hypothesis was accepted,and it was believed that the Zhuang medicine Yingbupu decoction had a therapeutic effect on stage I and II AKI.The conclusion was drawn that the treatment group received the Zhuang medicine Yingbupu.The clinical effective rate and improvement days were similar between the two groups,and there was no significant difference(P>0.05).However,the integral value of traditional Chinese medicine syndrome in the treatment group was lower than that in the control group(P<0.05),After treatment,the Scr,mAlb,and NGAL levels of patients in both groups were lower than before treatment(P<0.05).After treatment,the Scr,mAlb,and NGAL values in the treatment group were significantly lower than those in the control group(P<0.05).After treatment,the 24-hour urine volume in both groups was higher than that before treatment,and the values in the treatment group were significantly higher than those in the control group(P<0.05).During the treatment period,there were no significant adverse reactions or complications in either group.Conclusion:The Zhuang medicine Yingbupu decoction is effective in treating stage I and II AKI,and the Zhuang medicine Yingbupu can significantly improve the symptoms and quality of life of patients with stage I and II AKI.Its improvement of renal function is better than that of Jinshuibao tablets,and its safety is good.