Modeling,Simulation,and Risk Analysis of Battery Energy Storage Systems in New Energy Grid Integration Scenarios

Energy storage batteries can smooth the volatility of renewable energy sources.The operating conditions during power grid integration of renewable energy can affect the performance and failure risk of battery energy storage system(BESS).However,the current modeling of grid-connected BESS is overly simplistic,typically only considering state of charge(SOC)and power constraints.Detailed lithium(Li)-ion battery cell models are computationally intensive and impractical for real-time applications and may not be suitable for power grid operating conditions.Additionally,there is a lack of real-time batteries risk assessment frameworks.To address these issues,in this study,we establish a thermal-electric-performance(TEP)coupling model based on a multitime scale BESS model,incorporating the electrical and thermal characteristics of Li-ion batteries along with their performance degradation to achieve detailed simulation of grid-connected BESS.Additionally,considering the operating characteristics of energy storage batteries and electrical and thermal abuse factors,we developed a battery pack operational riskmodel,which takes into account SOCand charge-discharge rate(Cr),using amodified failure rate to represent the BESS risk.By integrating detailed simulation of energy storage with predictive failure risk analysis,we obtained a detailed model for BESS risk analysis.This model offers a multi-time scale integrated simulation that spans month-level energy storage simulation times,day-level performance degradation,minutescale failure rate,and second-level BESS characteristics.It offers a critical tool for the study of BESS.Finally,the performance and risk of energy storage batteries under three scenarios—microgrid energy storage,wind power smoothing,and power grid failure response—are simulated,achieving a real-time state-dependent operational risk analysis of the BESS.

Analysis of risk factors leading to anxiety and depression in patients with prostate cancer after castration and the construction of a risk prediction model

BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.

Risk Analysis of Passalora sequoiae Invasion to China with Imported Coniferous Wood

According to international standard plant quarantine measures and principle risk analysis(ISPM No.11),a risk assessment was carried out for Passalora sequoiae through geographical distribution,possibility of colonization,probability of diffusion,economic importance and difficulty in risk management.Results show that P.sequoiae has a greater risk of introduction and diffusion,and it has distributed in parts of China.It is suggested that P.sequoiae should be added to the list of forest dangerous pests in China.Besides,porting departments should focus on the pathogen on imported host seedlings like Cryptomeria.

Analysis of the Tragedy of Vila Socóin Cubatão (São Paulo, Brazil) Using the Haddon Matrix: A Comprehensive Examination of Socio-Environmental Factors and Risk Mitigation Strategies

The tragedy of Vila Socó epitomizes the socio-environmental repercussions of rapid industrialization in Cubatão. Beginning in the 1940s with the construction of the Anchieta highway, the city experienced an influx of migrants drawn by burgeoning industries, leading to unplanned urban growth and the emergence of vulnerable communities like Vila Socó. This article examines the interconnected factors—such as demographic shifts, inadequate planning, and regulatory oversight—that culminated in the devastating fire of 1984, claiming numerous lives and highlighting systemic failures. Utilizing the Haddon Matrix, this study dissects the Vila Socó incident, emphasizing the roles of human error, infrastructure integrity, and socio-economic disparities in disaster causation. By contextualizing the tragedy within Cubatão’s industrial trajectory, it underscores the urgent need for comprehensive risk assessment and proactive mitigation strategies in rapidly developing regions globally. Beyond its immediate focus, this work offers broader insights into the dynamics of industrial disasters and their socio-economic implications. As pipelines continue to play a vital role in global energy infrastructure, the lessons drawn from Vila Socó’s tragedy resonate deeply, emphasizing the imperative of robust safety protocols and accountable governance to prevent similar catastrophes in the future.

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

BACKGROUND Gastrointestinal neoplasm(GN)significantly impact the global cancer burden and mortality,necessitating early detection and treatment.Understanding the evolution and current state of research in this field is vital.AIM To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research,focusing on key contributors,institutions,and thematic evolution.METHODS This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the"bibliometrix"R package,VOSviewer,and CiteSpace.The analysis focused on the distribution of publications,contributions by institutions and countries,and trends in keywords.The methods included data synthesis,network analysis,and visualization of international collaboration networks.RESULTS This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration.It highlights the United States'critical role in advancing this field,with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute.The last five years,substantial advancements have been made,representing nearly 45%of the examined literature.Publication rates have dramatically increased,from 20 articles in 2002 to 112 in 2022,reflecting intensified research efforts.This study underscores a growing trend toward interdisciplinary and international collaboration,with the Journal of Clinical Oncology standing out as a key publication outlet.This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks.CONCLUSION This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis.This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy,ultimately enhancing worldwide patient care.

Sensitivity and resistance risk analysis of Didymella bryoniae populations to fluopyram

Fluopyram is an succinate dehydrogenase inhibitors(SDHI)fungicide that has been registered in China to control gummy stem blight(GSB)in watermelons for many years.However,whether the field pathogens of GSB are still sensitive to fluopyram or not is unknown.Therefore,we collected 69 Didymella bryoniae isolates from the fields that usually use fluopyram to control GSB to determine the sensitivity change.The EC_(50)(50%inhibition effect)values of fluopyram against D.bryoniae ranged from 0.0691 to 0.3503μg mL^(–1) and the variation factor was 5.07.The mean EC_(50) value was(0.1579±0.0669)μg mL^(–1) and the curve of sensitivity was unimodal.No resistant strains were found in the isolates,which means that the pathogens were still sensitive to fluopyram.The minimal inhibition concentration(MIC)of fluopyram against D.bryoniae was 3μg mL^(–1).Four low-resistant mutants and two medium-resistant mutants were obtained using fungicide taming and the resistance of mutants could be inherited stably.The growth rate of mutants decreased significantly compared with that of wild-type strains while the biomass of most mutants was similar to that of wild-type strains.The sensitivity of most resistant mutants to various stresses was increased compared with that of wild-type strains.The virulence of mutants receded except for low-resistant mutant XN51FR-1,which had the same lesion area as XN51 on the watermelon leaves.The results indicated that the fitness of resistant mutants was decreased compared with that of wild-type strains.The cross-resistance assay indicated that fluopyram-resistant mutants were positive cross-resistant to all six SDHI fungicides in this test but were still sensitive to fluazinam and tebuconazole.So the resistance risk of D.bryoniae to fluopyram was moderate.In addition,we found that the SdhB gene of low-resistant mutant XN30FR-1 had three new point mutations at positions K258N,A259P,and H277N.Medium-resistant mutant XN52FR-1 showed a mutation at position H277N and other mutants did not have any point mutation.

Cybersecurity Risk Management through Behavior-Based Contextual Analysis of Online Logs

This paper studies cyber risk management by integrating contextual log analysis with User and Entity Behavior Analytics (UEBA). Leveraging Python scripting and PostgreSQL database management, the solution enriches log data with contextual and behavioral information from Linux system logs and semantic datasets. By incorporating Common Vulnerability Scoring System (CVSS) metrics and customized risk scoring algorithms, the system calculates Insider Threat scores to identify potential security breaches. The integration of contextual log analysis and UEBA [1] offers a proactive defense against insider threats, reducing false positives and prioritizing high-risk alerts.

Prediction of the potential distribution and analysis of the freezing injury risk of winter wheat on the Loess Plateau under climate change

Determining the suitable areas for winter wheat under climate change and assessing the risk of freezing injury are crucial for the cultivation of winter wheat.We used an optimized Maximum Entropy(MaxEnt)Model to predict the potential distribution of winter wheat in the current period(1970-2020)and the future period(2021-2100)under four shared socioeconomic pathway scenarios(SSPs).We applied statistical downscaling methods to downscale future climate data,established a scientific and practical freezing injury index(FII)by considering the growth period of winter wheat,and analyzed the characteristics of abrupt changes in winter wheat freezing injury by using the Mann-Kendall(M-K)test.The results showed that the prediction accuracy AUC value of the MaxEnt Model reached 0.976.The minimum temperature in the coldest month,precipitation in the wettest season and annual precipitation were the main factors affecting the spatial distribution of winter wheat.The total suitable area of winter wheat was approximately 4.40×10^(7)ha in the current period.In the 2070s,the moderately suitable areas had the greatest increase by 9.02×10^(5)ha under SSP245 and the least increase by 6.53×10^(5)ha under SSP370.The centroid coordinates of the total suitable areas tended to move northward.The potential risks of freezing injury in the high-latitude and-altitude areas of the Loess Plateau,China increased significantly.The northern areas of Xinzhou in Shanxi Province,China suffered the most serious freezing injury,and the southern areas of the Loess Plateau suffered the least.Environmental factors such as temperature,precipitation and geographical location had important impacts on the suitable area distribution and freezing injury risk of winter wheat.In the future,greater attention should be paid to the northward boundaries of both the winter wheat planting areas and the areas of freezing injury risk to provide the early warning of freezing injury and implement corresponding management strategies.

Intertwined depressive and cognitive trajectories and the risk of dementia and death in older adults:a competing risk analysis

Background Depressive symptoms and cognitive impairment often interact,rendering their associations controversial.To date,their joint trajectories and associations with dementia and death remain underexplored.Aims To explore the interactions between depressive symptoms and cognitive function,their developmental trajectories and the associations with all-cause dementia,Alzheimer’s disease(AD)and all-cause death in older adults.Methods Data were from the Health and Retirement Study.Depressive symptoms and cognitive function were measured using the 8-item Centre for Epidemiologic Studies Depression Scale and the Telephone Interview of Cognitive Status,respectively.All-cause dementia and AD were defined by self-reported or proxy-reported physician diagnoses.All-cause death was determined by interviews.The restricted cubic spline,group-based trajectory modelling and subdistribution hazard regression were used.Results Significant interactions between depressive symptoms and cognitive function in 2010 in their association with new-onset all-cause dementia and AD from 2010 to 2020 were found,especially in women(p for interaction<0.05).Independent trajectory analysis showed that emerging or high(vs no)depressive trajectories and poor or rapidly decreased cognitive trajectories(vs very good)from 1996 to 2010 were at significantly higher risk of subsequent all-cause dementia,AD and all-cause death.15 joint trajectories of depressive symptoms and cognitive function from 1996 to 2010 were determined,where rapidly decreased cognitive function was more common in those with no depressive symptoms.Compared with older adults with the trajectory of no depressive symptoms and very good cognitive function,those with the trajectory of no depressive symptoms but rapidly decreased cognitive function were much more likely to develop new-onset all-cause dementia and death,with subdistribution hazard ratios(95%confidence intervals)of 4.47(2.99 to 6.67)and 1.84(1.43 to 2.36),especially in women.Conclusions To effectively mitigate the risk of dementia and death,it is crucial to acknowledge the importance of preventing cognitive decline in older adults without depressive symptoms,particularly in women.

Hotspots and trends of risk factors in gastric cancer: A visualization and bibliometric analysis

BACKGROUND The lack of specific symptoms of gastric cancer(GC)causes great challenges in its early diagnosis.Thus it is essential to identify the risk factors for early diagnosis and treatment of GC and to improve the survival rates.AIM To assist physicians in identifying changes in the output of publications and research hotspots related to risk factors for GC,constructing a list of key risk factors,and providing a reference for early identification of patients at high risk for GC.METHODS Research articles on risk factors for GC were searched in the Web of Science core collection,and relevant information was extracted after screening.The literature was analyzed using Microsoft Excel 2019,CiteSpace V,and VOSviewer 1.6.18.RESULTS A total of 2514 papers from 72 countries and 2507 research institutions were retrieved.China(n=1061),National Cancer Center(n=138),and Shoichiro Tsugane(n=36)were the most productive country,institution,or author,respectively.The research hotspots in the study of risk factors for GC are summarized in four areas,namely:Helicobacter pylori(H.pylori)infection,single nucleotide polymorphism,bio-diagnostic markers,and GC risk prediction models.CONCLUSION In this study,we found that H.pylori infection is the most significant risk factor for GC;single-nucleotide polymorphism(SNP)is the most dominant genetic factor for GC;bio-diagnostic markers are the most promising diagnostic modality for GC.GC risk prediction models are the latest current research hotspot.We conclude that the most important risk factors for the development of GC are H.pylori infection,SNP,smoking,diet,and alcohol.

Dynamical analysis,control,boundedness,and prediction for a fractional-order financial risk system

This paper delves into the dynamical analysis,chaos control,Mittag–Leffler boundedness(MLB),and forecasting a fractional-order financial risk(FOFR)system through an absolute function term.To this end,the FOFR system is first proposed,and the adomian decomposition method(ADM)is employed to resolve this fractional-order system.The stability of equilibrium points and the corresponding control schemes are assessed,and several classical tools such as Lyapunov exponents(LE),bifurcation diagrams,complexity analysis(CA),and 0–1 test are further extended to analyze the dynamical behaviors of FOFR.Then the global Mittag–Leffler attractive set(MLAS)and Mittag–Leffler positive invariant set(MLPIS)for the proposed financial risk(FR)system are discussed.Finally,a proficient reservoir-computing(RC)method is applied to forecast the temporal evolution of the complex dynamics for the proposed system,and some simulations are carried out to show the effectiveness and feasibility of the present scheme.

Risk Factors of Depression Screened by Two-Sample Mendelian Randomization Analysis:A Systematic Review

Objective This study explored the potentially modifiable factors for depression and major depressive disorder(MDD)from the MR-Base database and further evaluated the associations between drug targets with MDD.Methods We analyzed two-sample of Mendelian randomization(2SMR)using genetic variant depression(n=113,154)and MDD(n=208,811)from Genome-Wide Association Studies(GWAS).Separate calculations were performed with modifiable risk factors from MR-Base for 1,001 genomes.The MR analysis was performed by screening drug targets with MDD in the DrugBank database to explore the therapeutic targets for MDD.Inverse variance weighted(IVW),fixed-effect inverse variance weighted(FE-IVW),MR-Egger,weighted median,and weighted mode were used for complementary calculation.Results The potential causal relationship between modifiable risk factors and depression contained 459 results for depression and 424 for MDD.Also,the associations between drug targets and MDD showed that SLC6A4,GRIN2A,GRIN2C,SCN10A,and IL1B expression are associated with an increased risk of depression.In contrast,ADRB1,CHRNA3,HTR3A,GSTP1,and GABRG2 genes are candidate protective factors against depression.Conclusion This study identified the risk factors causally associated with depression and MDD,and estimated 10 drug targets with significant impact on MDD,providing essential information for formulating strategies to prevent and treat depression.

Risk Prediction Model for Hypertensive Disorders of Pregnancy Based on Routine Laboratory Indicators and Risk Factors: A Retrospective Analysis

Background: Hypertensive disorder of pregnancy (HDP) is a group of diseases in which pregnancy and elevated blood pressure coexist. There is still a lack of reliable clinical tools to predict the incidence of HDP. The purpose of this study was to establish and validate a nomogram prediction model for assessing the risk of HDP in pregnant women based on laboratory indicators and HDP risk factors. Method: A total of 307 pregnant women who were hospitalized in the obstetrics and gynecology department of our hospital were included in this study, and were randomly divided into a training cohort and validation cohort at a ratio of 7:3. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for the development of HDP on laboratory indicators as well as risk factors for HDP in the training cohort of patients. The results of the multivariate regression model were visualized by forest plots. A nomogram was constructed based on the results of multivariate logistic regression to predict the risk of HDP in pregnant women. The validity of the risk prediction model was evaluated by the area under the receiver operating characteristic curve (AUC), the consistency index (C-index), the calibration curve and the decision curve analysis (DCA). Results: BMI ≥ 25 Kg/m2, total cholesterol in early pregnancy, uric acid and proteinuria in late pregnancy were independent risk factors for HDP. The AUC and C-index of the nomogram constructed by the above four factors were both 0.848. The calibration curve is closely fitted with the ideal diagonal, showing a good consistency between the nomogram prediction and the actual observation of HDP. The DCA has demonstrated the great clinical utility of nomogram. Internal verification proves the reliability of the predicted nomograms. Conclusion: The BTUP nomogram model based on laboratory indicators and risk factors proposed in this study showed good predictive value for the risk assessment of HDP. It is expected to provide evidence for clinical prediction of the risk of HDP in pregnant women.

Analysis of Perinatal Outcomes of Twin Pregnancy in a Referral Hospital for High Risk

Introduction: The incidence of twin pregnancies has increased significantly in recent decades. These pregnancies require more attention due to their worse outcomes than singleton pregnancies. Objective: To analyze the characteristics and perinatal outcomes of twin pregnancies at the Caxias do Sul General Hospital. Methods: This is a descriptive and retrospective study that included all births related to twin pregnancies between March 1998 and June 2018. Maternal and perinatal variables were analyzed. Descriptive analyses were carried out using measures of central tendency and dispersion for continuous variables (mean and standard deviation or median and interquartile range), according to a prior assessment of distribution using the Shapiro-Wilk test, and absolute (n) and relative (n%) frequencies for categorical variables. Results: 172 pairs of twins/21,972 births (0.8%) were identified. There was a high percentage of interpartum interval of less than 12 months, adherence and prenatal visits, body mass index, and need for neonatal intensive care. Stillbirth and neomortality rates were within acceptable parameters. Conclusion: The sample studied showed a percentage similar to that in the literature, a high rate of maternal and perinatal complications, characterizing it as a high-risk fetal pregnancy.

Survival and risk analysis of patients with COVID-19 in Iran

Objective:To evaluate the risk factors of death caused by COVID-19 in Iran.Methods:This study was a retrospective cohort study from February 20,2020,to August 22,2022,in the hospitals in Isfahan,Iran.The data were collected through a researcher-made checklist.To determine the risk factors of the death,logistic regression and Cox regression models were used.For each variable,the odds ratio and 95%confidence interval were also reported.Results:1885 Patients were included.The age of deceased persons was significantly higher than that of the surviving persons.The risk of death for the age group above 60 years was about 14 times higher than that of people aged 19-35 years[95%CI:14.41(2.02-102.99),P<0.01].Hypertension[95%CI:1.92(1.47-2.5),P<0.01],diabetes[95%CI:1.62(1.23-2.13),P<0.001],and chronic obstructive pulmonary disease[95%CI:1.92(1.47-2.50),P<0.01]were also risk factors of mortality.Conclusions:This study reveals that the mortality rate due to COVID-19 is associated with old age,longer hospitalization in the ICU,increased length of stay,and comorbidities of high blood pressure,diabetes,and chronic pulmonary disease.

DYNAMIC ANALYSIS OF A TYPE OF FINANCIAL RISK CONTAGION MODEL INVOLVING IMMUNITY PERIOD AND SELF-RESCUE

In this paper,we study the dynamics of a Susceptible-Exposed-Infectious-Recovered(SEIR)nancial risk contagion model with time delay.Using stability theory and Hopf bifurcation theory,equilibria stability and Hopf bifurcation are analyzed in detail.Based on the epidemic model,we improve it by taking prior prevention and self-rescue into consideration,conclude pre-ventive intensity and self-rescue capabilities e ect the number of infections.At the same time,the analytical conditions for Hopf bifurcation are obtained,and the relevant results are veri ed by numerical simulations.

Analysis of risk factors of suicidal ideation in adolescent patients with depression and construction of prediction model

BACKGROUND Major depressive disorder is a common mental illness among adolescents and is the largest disease burden in this age group.Most adolescent patients with depression have suicidal ideation(SI);however,few studies have focused on the factors related to SI,and effective predictive models are lacking.AIM To construct a risk prediction model for SI in adolescent depression and provide a reference assessment tool for prevention.METHODS The data of 150 adolescent patients with depression at the First People's Hospital of Lianyungang from June 2020 to December 2022 were retrospectively analyzed.Based on whether or not they had SI,they were divided into a SI group(n=91)and a non-SI group(n=59).The general data and laboratory indices of the two groups were compared.Logistic regression was used to analyze the factors influencing SI in adolescent patients with depression,a nomogram prediction model was constructed based on the analysis results,and internal evaluation was performed.Receiver operating characteristic and calibration curves were used to evaluate the model’s efficacy,and the clinical application value was evaluated using decision curve analysis(DCA).RESULTS There were differences in trauma history,triggers,serum ferritin levels(SF),highsensitivity C-reactive protein levels(hs-CRP),and high-density lipoprotein(HDLC)levels between the two groups(P<0.05).Logistic regression analysis showed that trauma history,predisposing factors,SF,hs-CRP,and HDL-C were factors influencing SI in adolescent patients with depression.The area under the curve of the nomogram prediction model was 0.831(95%CI:0.763–0.899),sensitivity was 0.912,and specificity was 0.678.The higher net benefit of the DCA and the average absolute error of the calibration curve were 0.043,indicating that the model had a good fit.CONCLUSION The nomogram prediction model based on trauma history,triggers,ferritin,serum hs-CRP,and HDL-C levels can effectively predict the risk of SI in adolescent patients with depression.

GIS Application in Urban Flood Risk Analysis: Midar as a Case Study

The significance of this study lies in its exploration of the advanced applications of Geographic Information Systems (GIS) in assessing urban flood risks, with a specific focus on Midar, Morocco. This research is pivotal as it showcases that GIS technology is not just a tool for mapping, but a critical component in urban planning and emergency management strategies. By meticulously identifying and mapping flood-prone areas in Midar, the study provides invaluable insights into the potential vulnerabilities of urban landscapes to flooding. Moreover, this research demonstrates the practical utility of GIS in mitigating material losses, a significant concern in flood-prone urban areas. The proactive approach proposed in this study, centered around the use of GIS, aims to safeguard Midar’s population and infrastructure from the devastating impacts of floods. This approach serves as a model for other urban areas facing similar challenges, highlighting the indispensable role of GIS in disaster preparedness and response. Overall, the study underscores the transformative potential of GIS in enhancing urban resilience, making it a crucial tool in the fight against natural disasters like floods.

Dislocation of primary total hip arthroplasty:Analysis of risk factors and preventive options

Total hip arthroplasty(THA)is one of the most successful elective operations in orthopedic surgery for improving pain and functional disability in patients with end-stage joint disease.However,dislocation continues to be a troublesome complication after THA,as it is a leading cause of revision and is associated with substantial social,health,and economic costs.It is a relatively rare,usually early occurrence that depends on both the patients’characteristics and the surgical aspects.The most recent and important finding is the special attention to be given preoperatively to spinopelvic mobility,which is closely related to the incidence of dislocation.Consequently,clinical and radiographic assessment of the lumbar spine is mandatory to identify an altered pelvic tilt that could suggest a different positioning of the cup.Lumbar spinal fusion is currently considered a risk factor for dislocation and revision regardless of whether it is performed prior to or after THA.Surgical options for its treatment and prevention include the use of prostheses with large diameter of femoral head size,dual mobility constructs,constrained liners,and modular neck stems.

Analysis of risk factors for postpartum depression after cesarean section in women with early-onset preeclampsia

BACKGROUND Early-onset preeclampsia significantly increases maternal and fetal morbidity and mortality.Many pregnant women with early onset preeclampsia choose cesarean section as their delivery method.Although extensive research has explored the association between postpartum depression(PPD)and cesarean section,few studies have investigated the risk factors after cesarean section in women with early-onset preeclampsia.AIM To examine these risk factors through a retrospective,observational analysis of 287 women who underwent a cesarean section for early preeclampsia between June 2014 and March 2024.METHODS Participants were assessed in person during the 32nd week of pregnancy,2 days post-cesarean,and 6 weeks postpartum.According to the Edinburgh Postnatal Depression Scale(EPDS),participants who underwent cesarean section were divided into PPD(n=60)and non-PPD groups(n=227).Furthermore,PPD was diagnosed at 6 weeks postpartum according to depressive symptoms(EPDS score≥11).The demographic and clinical features of PPD were screened.Multivariate logistic regression analysis was used to identify PPD risk factors.RESULTS The prevalence of PPD was 20.9%(60/287)among the 287 women who underwent cesarean section for early-onset preeclampsia.Multivariate logistic regression analyses revealed that advanced age(age>40 years)[odds ratio(OR)=1.93,95%CI:1.31-2.82],previous preeclampsia(OR=7.15,95%CI:5.81-8.85),pre-pregnancy obesity(OR=2.42,95%CI:1.62-3.63),gestational diabetes mellitus(OR=3.52,95%CI:2.51-4.92),preexisting hypertension(OR=1.35,95%CI:1.03-1.89),PPD symptoms(EPDS≥11)at 2 days postpartum(OR=6.15,95%CI:1.32-28.35),high prenatal self-rating anxiety scale score(OR=1.13,95%CI:1.06-1.18),and pain at 6 weeks postpartum(OR=2.16,95%CI:1.28-3.66)were independently associated with PPD.CONCLUSION Risk factors for PPD after cesarean section in women with early-onset preeclampsia include advanced age(age>40 years),pre-pregnancy obesity,previous preeclampsia,gestational diabetes mellitus,preexisting hypertension,PPD symptoms(EPDS≥11)at 2 days postpartum,prenatal anxiety,and pain at 6 weeks postpartum.The early identi-fication of these factors and interventions can mitigate the risk of PPD.