To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the stre...To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the strength and elastic modulus of mortar and concrete prepared with mechanism aggregates of the corresponding lithology,and the stress-strain curves of concrete were investigated.In this paper,a coarse aggregate and mortar matrix bonding assumption is proposed,and a prediction model for the elastic modulus of mortar is established by considering the lithology of the mechanism sand and the slurry components.An equivalent coarse aggregate elastic modulus model was established by considering factors such as coarse aggregate particle size,volume fraction,and mortar thickness between coarse aggregates.Based on the elastic modulus of the equivalent coarse aggregate and the remaining mortar,a prediction model for the elastic modulus of the two and three components of concrete in series and then in parallel was established,and the predicted values differed from the measured values within 10%.It is proposed that the coarse aggregate elastic modulus in highstrength concrete is the most critical factor affecting the elastic modulus of concrete,and as the coarse aggregate elastic modulus increases by 27.7%,the concrete elastic modulus increases by 19.5%.展开更多
BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR...BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR)is a common polypectomy proce-dure in clinical practice,but it has a high postoperative recurrence rate.Currently,there is no predictive model for the recurrence of colorectal polyps after EMR.AIM To construct and validate a machine learning(ML)model for predicting the risk of colorectal polyp recurrence one year after EMR.METHODS This study retrospectively collected data from 1694 patients at three medical centers in Xuzhou.Additionally,a total of 166 patients were collected to form a prospective validation set.Feature variable screening was conducted using uni-variate and multivariate logistic regression analyses,and five ML algorithms were used to construct the predictive models.The optimal models were evaluated based on different performance metrics.Decision curve analysis(DCA)and SHapley Additive exPlanation(SHAP)analysis were performed to assess clinical applicability and predictor importance.RESULTS Multivariate logistic regression analysis identified 8 independent risk factors for colorectal polyp recurrence one year after EMR(P<0.05).Among the models,eXtreme Gradient Boosting(XGBoost)demonstrated the highest area under the curve(AUC)in the training set,internal validation set,and prospective validation set,with AUCs of 0.909(95%CI:0.89-0.92),0.921(95%CI:0.90-0.94),and 0.963(95%CI:0.94-0.99),respectively.DCA indicated favorable clinical utility for the XGBoost model.SHAP analysis identified smoking history,family history,and age as the top three most important predictors in the model.CONCLUSION The XGBoost model has the best predictive performance and can assist clinicians in providing individualized colonoscopy follow-up recommendations.展开更多
BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To ...BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023.A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.RESULTS A total of 171 patients were included in this study:93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN.The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800,respectively,while the least absolute shrinkage and selection operator(LASSO)regression model showed sensitivity and specificity values of 0.842 and 0.840,respectively.The area under the curve(AUC)for the logistic model was 0.896,slightly lower than the AUC of 0.904 for the LASSO model.Internal validation with 30%of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model.The LASSO model provided greater utility in clinical decision-making.CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.展开更多
Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the s...Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.展开更多
This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hype...This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.展开更多
Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive cont...Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.展开更多
Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluct...Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluctuation caused by new energy units,this paper proposes a new energy power system frequency regulation strategy with multiple units including the doubly-fed pumped storage unit(DFPSU).Firstly,based on the model predictive control(MPC)theory,the state space equations are established by considering the operating characteristics of the units and the dynamic behavior of the system;secondly,the proportional-differential control link is introduced to minimize the frequency deviation to further optimize the frequency modulation(FM)output of the DFPSU and inhibit the rapid fluctuation of the frequency;lastly,it is verified on theMatlab/Simulink simulation platform,and the results show that the model predictive control with proportional-differential control link can further release the FM potential of the DFPSU,increase the depth of its FM,effectively reduce the frequency deviation of the system and its rate of change,realize the optimization of the active output of the DFPSU and that of other units,and improve the frequency response capability of the system.展开更多
Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential S...Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.展开更多
Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction mode...Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction model for postoperative recurrence in pancreatic cancer patients, incorporating multiple preoperative, intraoperative, and postoperative factors to assist clinical decision-making. Methods: A retrospective study was conducted on 216 patients who underwent surgical treatment for pancreatic malignancy at the First Affiliated Hospital of Chongqing Medical University between January 2015 and January 2023. An independent external validation cohort of 76 patients from the Second Affiliated Hospital of Chongqing Medical University was used to validate the model. Seven independent risk factors for postoperative recurrence were identified through univariate and multivariate Cox regression analyses. The model’s performance was evaluated using the concordance index (C-index) and ROC curves, and its accuracy and clinical value were assessed using calibration curves and decision curve analysis (DCA). Results: The predictive model demonstrated good discriminatory power, with a C-index of 0.72 in the training cohort and 0.66 in the validation cohort. The ROC curves for predicting recurrence at 3, 6, and 12 months postoperatively showed AUC values ranging from 0.72 to 0.83, indicating strong predictive value. Calibration curves and DCA confirmed the model’s accuracy and clinical utility. Conclusion: This study successfully developed and validated a clinical prediction model that incorporates seven independent risk factors for postoperative recurrence in pancreatic cancer. The model provides a useful tool for predicting recurrence risk, aiding in the identification of high-risk patients, and informing clinical decision-making.展开更多
BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk...BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.展开更多
BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed t...BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed that CRC patients may experience postoperative cognitive dysfunction(POCD).AIM To establish a risk prediction model for POCD in CRC patients and investigate the preventive value of dexmedetomidine(DEX).METHODS A retrospective analysis was conducted on clinical data from 140 CRC patients who underwent surgery at the People’s Hospital of Qian Nan from February 2020 to May 2024.Patients were allocated into a modeling group(n=98)and a validation group(n=42)in a 7:3 ratio.General clinical data were collected.Additionally,in the modeling group,patients who received DEX preoperatively were incorporated into the observation group(n=54),while those who did not were placed in the control group(n=44).The incidence of POCD was recorded for both cohorts.Data analysis was performed using statistical product and service solutions 20.0,with t-tests orχ^(2) tests employed for group comparisons based on the data type.Least absolute shrinkage and selection operator regression was applied to identify influencing factors and reduce the impact of multicollinear predictors among variables.Multivariate analysis was carried out using Logistic regression.Based on the identified risk factors,a risk prediction model for POCD in CRC patients was developed,and the predictive value of these risk factors was evaluated.RESULTS Significant differences were observed between the cognitive dysfunction group and the non-cognitive dysfunction group in diabetes status,alcohol consumption,years of education,anesthesia duration,intraoperative blood loss,intraoperative hypoxemia,use of DEX during surgery,intraoperative use of vasoactive drugs,surgical time,systemic inflammatory response syndrome(SIRS)score(P<0.05).Multivariate Logistic regression analysis identified that diabetes[odds ratio(OR)=4.679,95%confidence interval(CI)=1.382-15.833],alcohol consumption(OR=5.058,95%CI:1.255-20.380),intraoperative hypoxemia(OR=4.697,95%CI:1.380-15.991),no use of DEX during surgery(OR=3.931,95%CI:1.383-11.175),surgery duration≥90 minutes(OR=4.894,95%CI:1.377-17.394),and a SIRS score≥3(OR=4.133,95%CI:1.323-12.907)were independent risk factors for POCD in CRC patients(P<0.05).A risk prediction model for POCD was constructed using diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score as factors.A receiver operator characteristic curve analysis of these factors revealed the model’s predictive sensitivity(88.56%),specificity(70.64%),and area under the curve(AUC)(AUC=0.852,95%CI:0.773-0.919).The model was validated using 42 CRC patients who met the inclusion criteria,demonstrating sensitivity(80.77%),specificity(81.25%),and accuracy(80.95%),and AUC(0.805)in diagnosing cognitive impairment,with a 95%CI:0.635-0.896.CONCLUSION Logistic regression analysis identified that diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score vigorously influenced the occurrence of POCD.The risk prediction model based on these factors demonstrated good predictive performance for POCD in CRC individuals.This study offers valuable insights for clinical practice and contributes to the prevention and management of POCD under CRC circumstances.展开更多
BACKGROUND Sepsis is a severe complication in hospitalized patients with diabetic foot(DF),often associated with high morbidity and mortality.Despite its clinical significance,limited tools exist for early risk predic...BACKGROUND Sepsis is a severe complication in hospitalized patients with diabetic foot(DF),often associated with high morbidity and mortality.Despite its clinical significance,limited tools exist for early risk prediction.AIM To identify key risk factors and evaluate the predictive value of a nomogram model for sepsis in this population.METHODS This retrospective study included 216 patients with DF admitted from January 2022 to June 2024.Patients were classified into sepsis(n=31)and non-sepsis(n=185)groups.Baseline characteristics,clinical parameters,and laboratory data were analyzed.Independent risk factors were identified through multivariable logistic regression,and a nomogram model was developed and validated.The model's performance was assessed by its discrimination(AUC),calibration(Hosmer-Lemeshow test,calibration plots),and clinical utility[decision curve analysis(DCA)].RESULTS The multivariable analysis identified six independent predictors of sepsis:Diabetes duration,DF Texas grade,white blood cell count,glycated hemoglobin,Creactive protein,and albumin.A nomogram integrating these factors achieved excellent diagnostic performance,with an AUC of 0.908(95%CI:0.865-0.956)and robust internal validation(AUC:0.906).Calibration results showed strong agreement between predicted and observed probabilities(Hosmer-Lemeshow P=0.926).DCA demonstrated superior net benefit compared to extreme intervention scenarios,highlighting its clinical utility.CONCLUSION The nomogram prediction model,based on six key risk factors,demonstrates strong predictive value,calibration,and clinical utility for sepsis in patients with DF.This tool offers a practical approach for early risk stratification,enabling timely interventions and improved clinical management in this high-risk population.展开更多
BACKGROUND Anxiety and depression are highly prevalent among patients with cervical cancer(CC).However,few studies have systematically analyzed the psychological effects of tumor stage,treatment methods,and related fa...BACKGROUND Anxiety and depression are highly prevalent among patients with cervical cancer(CC).However,few studies have systematically analyzed the psychological effects of tumor stage,treatment methods,and related factors on these patients,or developed predictive models for these outcomes.AIM To identify factors influencing anxiety and depression in patients with CC and construct predictive models.METHODS We retrospectively analyzed data from 119 patients with CC treated at the Gynecology Department of Suzhou Ninth People’s Hospital between January 2017 and May 2025.Clinical data,psychological hope levels at diagnosis,and Self-Rating Anxiety Scale and Self-Rating Depression Scale scores during treatment were collected.Influencing factors were identified,and predictive models were developed.The model performance was evaluated using receiver operating characteristic(ROC)curves and the Hosmer-Lemeshow goodness-of-fit test.RESULTS During treatment,64.71%of the patients experienced anxiety and 52.10%experienced depression.Significant differences in family income,tumor stage,treatment modality,and hope level were observed between patients with and without anxiety/depression(P<0.05).Multivariate analysis showed that a family monthly income<5000 yuan,stage III-IV tumor,comprehensive treatment,and low hope level were independent risk factors(P<0.05).The predictive formula for anxiety was as follows:Logit(P)=0.795×monthly income+0.594×tumor stage+1.095×treatment method+1.184×hope level−9.176;for depression:Logit(P)=0.432×monthly income+0.518×tumor stage+0.727×treatment method+1.095×hope level−8.541.The area under the ROC curves were 0.865 for anxiety and 0.837 for depression.Goodness-of-fit test confirmed no overfitting(P>0.05).CONCLUSION Family income,tumor stage,treatment method,and hope level are key determinants of anxiety and depression in patients with CC.Predictive models incorporating these factors can effectively assess risk of anxiety and depression during treatment.展开更多
Objectives:This study aimed to develop and validate a stroke risk prediction model based on machine learning(ML)and regional healthcare big data,and determine whether it may improve the prediction performance compared...Objectives:This study aimed to develop and validate a stroke risk prediction model based on machine learning(ML)and regional healthcare big data,and determine whether it may improve the prediction performance compared with the conventional Logistic Regression(LR)model.Methods:This retrospective cohort study analyzed data from the CHinese Electronic health Records Research in Yinzhou(CHERRY)(2015–2021).We included adults aged 18–75 from the platform who had established records before 2015.Individuals with pre-existing stroke,key data absence,or excessive missingness(>30%)were excluded.Data on demographic,clinical measures,lifestyle factors,comorbidities,and family history of stroke were collected.Variable selection was performed in two stages:an initial screening via univariate analysis,followed by a prioritization of variables based on clinical relevance and actionability,with a focus on those that are modifiable.Stroke prediction models were developed using LR and four ML algorithms:Decision Tree(DT),Random Forest(RF),eXtreme Gradient Boosting(XGBoost),and Back Propagation Neural Network(BPNN).The dataset was split 7:3 for training and validation sets.Performance was assessed using receiver operating characteristic(ROC)curves,calibration,and confusion matrices,and the cutoff value was determined by Youden's index to classify risk groups.Results:The study cohort comprised 92,172 participants with 436 incident stroke cases(incidence rate:474/100,000 person-years).Ultimately,13 predictor variables were included.RF achieved the highest accuracy(0.935),precision(0.923),sensitivity(recall:0.947),and F1 score(0.935).Model evaluation demonstrated superior predictive performance of ML algorithms over conventional LR,with training/validation areaunderthe curve(AUC)sof0.777/0.779(LR),0.921/0.918(BPNN),0.988/0.980(RF),0.980/0.955(DT),and 0.962/0.958(XGBoost).Calibration analysis revealed a better fit for DT,LR and BPNN compared to RF and XGBoost model.Based on the optimal performance of the RF model,the ranking of factors in descending order of importance was:hypertension,age,diabetes,systolic blood pressure,waist,high-density lipoprotein Cholesterol,fasting blood glucose,physical activity,BMI,low-density lipoprotein cholesterol,total cholesterol,dietary habits,and family history of stroke.Using Youden's index as the optimal cutoff,the RF model stratified individuals into high-risk(>0.789)and low-risk(≤0.789)groups with robust discrimination.Conclusions:The ML-based prediction models demonstrated superior performance metrics compared to conventional LR and the RF is the optimal prediction model,providing an effective tool for risk stratifi cation in primary stroke prevention in community settings.展开更多
Testicular torsion is a urological emergency that requires prompt diagnosis and treatment,accounting for 10%-15%of cases of acute scrotum.[1]It occurs most frequently during the perinatal period and adolescence and ca...Testicular torsion is a urological emergency that requires prompt diagnosis and treatment,accounting for 10%-15%of cases of acute scrotum.[1]It occurs most frequently during the perinatal period and adolescence and can occur at any age.[2]The incidence of testicular torsion is 1/4,000 in males under 25 years of age and 1/160 in males over 25 years of age.[3]Unilateral torsion is relatively common,with a higher incidence on the left side.Testicular torsion is typically managed through surgical exploration.Necrotic testes,identified by a black appearance,require orchiectomy.[4]展开更多
BACKGROUND Low rectal cancer poses a significant surgical challenge because of its close proximity to the anal sphincter,often requiring radical resection with permanent colostomy to achieve oncological safety.Revisit...BACKGROUND Low rectal cancer poses a significant surgical challenge because of its close proximity to the anal sphincter,often requiring radical resection with permanent colostomy to achieve oncological safety.Revisited rectal anatomy,advances in surgical techniques and neoadjuvant therapies have enabled the possibility of sphincter-preserving procedures,however,it is uniformly not applicable.Selecting appropriate candidates for sphincter preservation is crucial,as an illadvised approach may compromise oncological outcome or lead to poor functional outcomes.Currently there is no consensus-which clinical,anatomical,or molecular factors most accurately predict the feasibility of sphincter-preserving surgery(SPS)in this subset of patients.By identifying these predictors,the study seeks to support improved patient selection,enhance surgical planning,and ultimately contribute to better functional and oncological outcomes in patients with low rectal cancer.AIM To identify predictive factors that determine the feasibility of SPS in patients with low rectal cancer.METHODS A comprehensive literature search was conducted using PubMed/MEDLINE databases.The search focused on various factors influencing the feasibility of SPS in low rectal cancer.These included patient-related factors,anatomical considerations,findings from different imaging modalities,advancements in diagnostic tools and techniques,and the role of neoadjuvant chemoradiotherapy.The relevance of each factor in predicting the potential for sphincter preservation was critically analyzed and presented based on the current evidence RESULTS Multiple studies have identified a range of predictive factors influencing the feasibility of SPS in low rectal cancer.Patient-related factors include age,sex,preoperative continence status,comorbidities,and body mass index.Anatomical considerations,such as tumor distance from the anal verge,involvement of the external anal sphincter,and levator ani muscles,also play a critical role.Additionally,a favourable response to neoadjuvant chemoradiotherapy has been associated with improved suitability for sphincter preservation.Several biomarkers,such as inflammatory markers like interleukins and C-reactive protein,as well as tumor markers like carcinoembryonic antigen,are important.Molecular markers,including BRAF and KRAS mutations and microsatellite instability status,have been linked to prognosis and may further guide decision-making regarding sphincter-preserving approaches.Artificial intelligence(AI)can further add in to select an ideal patient for sphincter preservation.CONCLUSION SPS is feasible in low rectal cancer and depends on patient factors,tumor anatomy and biology,preoperative treatment response,and biomarkers.In addition,tools and technology including AI can further help in selecting an ideal patient for long term optimal outcome.展开更多
Background:Existing hepatocellular carcinoma(HCC)prediction models lack transferability and generalizability when applied to female populations,resulting in diminished performance and inadequate tools for accurate HCC...Background:Existing hepatocellular carcinoma(HCC)prediction models lack transferability and generalizability when applied to female populations,resulting in diminished performance and inadequate tools for accurate HCC risk stratification among females.This study aims to develop and validate a score-based prediction model for early detection of HCC in female hepatitis B surface antigen(HBsAg)carriers.Methods:Participants were recruited from a multicenter prospective cohort engaged in liver cancer screening across China including seven high-risk rural areas and one additional high-risk rural area.The study involved 7080 females as the derivation cohort and 2069 as the validation cohort,with all participants aged 35-70 years and HBsAg positive.Laboratory tests and epidemiological surveys were conducted.Key predictor variables were identified through LASSO regression analysis,and score-based prediction models were developed based on Cox proportional hazards model.Model performance including discrimination and calibration was evaluated,and compared to existing prediction models and screening strategies.Results:After a median follow-up of 3.69 and 5.42 years,147 and 45 HCC cases were identified in the derivation and validation cohorts,respectively.The female HCC(HCCF)model incorporating five independent variables:age,α-fetoprotein(AFP),albumin,alanine aminotransferase,and platelet,showed excellent performance with an area under the receiver operating characteristic curve(AUC)of 0.82(95%CI:0.78-0.86).The HCCF-Enhanced model which included cirrhosis,achieved an AUC of 0.85(95%CI:0.81-0.89).Both models demonstrated superior predictive performance than existing models,with strong predictive accuracy in the validation cohort:AUCs of 0.83(95%CI:0.77-0.89)and 0.88(95%CI:0.83-0.92),respectively.The HCCF model,at a score threshold of 7,achieved the largest Youden’s index and identified 32.80%of high-risk individuals.When combined with ultrasonography(US),the model detected 37 additional cases,significantly improved screening sensitivity and accuracy compared to the traditional AFP plus US strategy.Conclusions:The developed HCCF models with good performance for HCC prediction in HBsAg-positive females significantly improve screening efficiency and provide an effective tool for surveillance,ultimately helping to optimize prevention and management strategies for HCC.展开更多
BACKGROUND In cases of colorectal cancer(CRC)with obstruction,patients experience local tissue edema due to intestinal obstruction.This condition stimulates the accumulation of inflammatory factors,activates cancer ce...BACKGROUND In cases of colorectal cancer(CRC)with obstruction,patients experience local tissue edema due to intestinal obstruction.This condition stimulates the accumulation of inflammatory factors,activates cancer cells,and increases the risk of tumor recurrence.At present,analyses and evaluation tools for factors influencing early postoperative recurrence in patients with CRC and obstruction are limited.AIM To explore the influencing factors and construct a predictive model of the early postoperative recurrence of CRC with obstruction.METHODS Data from 181 patients with CRC and obstruction who underwent surgery in the Department of Gastrointestinal Surgery,Suzhou Ninth Hospital Affiliated to Soochow University,between January 2017 and May 2023 were retrospectively collected.Patients with CRC and obstruction were divided into a recurrence group and a non-recurrence group based on whether recurrence occurred during the 2-year follow-up after surgery.Datasets from the two groups were compared.Subsequently,multiple logistic regression was employed to analyze the influencing factors of the early postoperative recurrence of CRC with obstruction.The nomogram prediction model was drawn using R software,and its performance was evaluated by the goodness of fit test and receiver operating characteristic(ROC)curve analysis.The clinical benefit rate of the model was evaluated by decision curves.RESULTS Among the 181 patients with CRC and obstruction,52(28.73%)experienced tumor recurrence within 2 years after surgery.Significant differences were observed in preoperative carcinoembryonic antigen(CEA),preoperative systemic immuneinflammation index(SII),tumor,node,and metastasis(TNM)stage,differentiation degree,nerve infiltration,and Ki-67 expression between the recurrence and non-recurrence groups(P<0.05).Multivariate logistic regression analysis showed that high preoperative CEA(OR=2.094,P=0.008),high preoperative SII(OR=2.795,P<0.001),TNM stage III(OR=1.644,P=0.027),poor differentiation(OR=1.861,P=0.035),and high Ki-67 expression(OR=2.467,P=0.001)were all influencing factors for early postoperative recurrence of CRC with obstruction.The area under the ROC curve of the nomograph model constructed based on this was 0.890,the goodness of fit deviation test was conducted(χ^(2)=3.903,P=0.866),and the decision curve display model demonstrated practical value in clinical practice.CONCLUSION The early recurrence rate of CRC with obstruction is high.CEA,SII,TNM staging,differentiation degree,and Ki-67 expression are factors related to early postoperative recurrence.A nomogram prediction model incorporating these factors can effectively evaluate the risk of early postoperative recurrence in patients with CRC.展开更多
AIM:To explore the relationship between matrix metalloproteinases(MMPs)expression levels in the tumor and the prognosis of uveal melanoma(UM)and to construct prognostic prediction models.METHODS:Transcriptome sequenci...AIM:To explore the relationship between matrix metalloproteinases(MMPs)expression levels in the tumor and the prognosis of uveal melanoma(UM)and to construct prognostic prediction models.METHODS:Transcriptome sequencing data from 17 normal choroid tissues and 53 UM tumor tissues were collected.Based on the differential gene expression levels and their function,MMPs family was selected for establishing risk-score system and prognostic prediction model with machine learning.Tumor microenvironment(TME)analysis was also applied for the impact of immune cell infiltration on prognosis of the disease.RESULTS:Eight MMPs were significantly different expression levels between normal and the tumor tissues.MMP-2 and MMP-28 were selected to construct a risk-score system and divided patients accordingly into high-and low-risk groups.The prediction model based on the risk-score achieved an accuracy of approximately 80%at 1-,3-,and 5-year after diagnosis.Besides,a Nomogram prognostic prediction model which based on risk-score and pathological type(independent prognostic factors after Cox regression analysis)demonstrated good consistency between the predicted outcomes at 1-,3-,and 5-year after diagnosis and the actual prognosis of patients.TME analysis revealed that the high-risk group exhibited higher immune and stromal scores and increased infiltration of tumor-associated macrophages(TAMs)and regulatory T cells compared to the low-risk group.CONCLUSION:Based on MMP-2 and MMP-28 expression levels,our prediction model demonstrates accurate long-term prognosis prediction for UM patients.The aggregation of TAMs and regulatory T cells in the TME of UM may be associated with an unfavorable prognosis.展开更多
BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high...BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high.AIM To compare the clinical characteristics of patients with cirrhosis,cirrhosis combined with intrapulmonary vascular dilatation(IPVD),and HPS,and to establish predictive models for IPVD and HPS.METHODS Patients with cirrhosis were prospectively screened at a liver-specialized university teaching hospital.Clinical information and blood samples were collected,and biomarker levels in blood samples were measured.Patients with cirrhosis were divided into three groups:Those with pure cirrhosis,those with combined IPVD,and those with HPS based on contrast-enhanced transthoracic echocardiography results and the pulmonary alveolar-arterial oxygen gradient values.Univariate logistic regression and Least Absolute Shrinkage and Selection Operator(LASSO)regression methods were utilized to identify risk factors for IPVD and HPS,and nomograms were constructed to predict IPVD and HPS.RESULTS A total of 320 patients were analyzed,with 101 diagnosed with IPVD,of whom 54 were diagnosed with HPS.There were statistically significant differences in clinical parameters among these three groups of patients.Among the tested biomarkers,sphingosine 1 phosphate,angiopoietin-2,and platelet-derived growth factor BB were significantly associated with IPVD and HPS in patients with cirrhosis.Following LASSO logistic regression screening,prediction models for IPVD and HPS were established.The area under the receiver operating characteristic curve for IPVD prediction was 0.792(95%confidence interval[CI]:0.737-0.847),and for HPS prediction was 0.891(95%CI:0.848-0.934).CONCLUSION This study systematically compared the clinical characteristics of patients with cirrhosis,IPVD,and HPS,and constructed predictive models for IPVD and HPS based on clinical parameters and laboratory indicators.These models showed good predictive value for IPVD and HPS in patients with cirrhosis.They can assist clinicians in the early prognosis assessment of patients with cirrhosis,ultimately benefiting the patients.展开更多
基金Funded by State Railway Administration Research Project(No.2023JS007)National Natural Science Foundation of China(No.52438002)+1 种基金Research and Development Programs for Science and Technology of China Railways Corporation(No.J2023G003)New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the strength and elastic modulus of mortar and concrete prepared with mechanism aggregates of the corresponding lithology,and the stress-strain curves of concrete were investigated.In this paper,a coarse aggregate and mortar matrix bonding assumption is proposed,and a prediction model for the elastic modulus of mortar is established by considering the lithology of the mechanism sand and the slurry components.An equivalent coarse aggregate elastic modulus model was established by considering factors such as coarse aggregate particle size,volume fraction,and mortar thickness between coarse aggregates.Based on the elastic modulus of the equivalent coarse aggregate and the remaining mortar,a prediction model for the elastic modulus of the two and three components of concrete in series and then in parallel was established,and the predicted values differed from the measured values within 10%.It is proposed that the coarse aggregate elastic modulus in highstrength concrete is the most critical factor affecting the elastic modulus of concrete,and as the coarse aggregate elastic modulus increases by 27.7%,the concrete elastic modulus increases by 19.5%.
文摘BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR)is a common polypectomy proce-dure in clinical practice,but it has a high postoperative recurrence rate.Currently,there is no predictive model for the recurrence of colorectal polyps after EMR.AIM To construct and validate a machine learning(ML)model for predicting the risk of colorectal polyp recurrence one year after EMR.METHODS This study retrospectively collected data from 1694 patients at three medical centers in Xuzhou.Additionally,a total of 166 patients were collected to form a prospective validation set.Feature variable screening was conducted using uni-variate and multivariate logistic regression analyses,and five ML algorithms were used to construct the predictive models.The optimal models were evaluated based on different performance metrics.Decision curve analysis(DCA)and SHapley Additive exPlanation(SHAP)analysis were performed to assess clinical applicability and predictor importance.RESULTS Multivariate logistic regression analysis identified 8 independent risk factors for colorectal polyp recurrence one year after EMR(P<0.05).Among the models,eXtreme Gradient Boosting(XGBoost)demonstrated the highest area under the curve(AUC)in the training set,internal validation set,and prospective validation set,with AUCs of 0.909(95%CI:0.89-0.92),0.921(95%CI:0.90-0.94),and 0.963(95%CI:0.94-0.99),respectively.DCA indicated favorable clinical utility for the XGBoost model.SHAP analysis identified smoking history,family history,and age as the top three most important predictors in the model.CONCLUSION The XGBoost model has the best predictive performance and can assist clinicians in providing individualized colonoscopy follow-up recommendations.
基金Supported by the National Key Research and Development Program of China,No.2022YFC2503600。
文摘BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023.A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.RESULTS A total of 171 patients were included in this study:93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN.The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800,respectively,while the least absolute shrinkage and selection operator(LASSO)regression model showed sensitivity and specificity values of 0.842 and 0.840,respectively.The area under the curve(AUC)for the logistic model was 0.896,slightly lower than the AUC of 0.904 for the LASSO model.Internal validation with 30%of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model.The LASSO model provided greater utility in clinical decision-making.CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.
基金supported in part by the National Natural Science Foundation of China under Grant 52077002。
文摘Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.
基金supported by the National Natural Science Foundation of China(12072090).
文摘This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.
文摘Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.
基金supported by the National Natural Science Foundation of China(Project No.52377082)the Scientific Research Program of Jilin Provincial Department of Education(Project No.JJKH20230123KJ).
文摘Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluctuation caused by new energy units,this paper proposes a new energy power system frequency regulation strategy with multiple units including the doubly-fed pumped storage unit(DFPSU).Firstly,based on the model predictive control(MPC)theory,the state space equations are established by considering the operating characteristics of the units and the dynamic behavior of the system;secondly,the proportional-differential control link is introduced to minimize the frequency deviation to further optimize the frequency modulation(FM)output of the DFPSU and inhibit the rapid fluctuation of the frequency;lastly,it is verified on theMatlab/Simulink simulation platform,and the results show that the model predictive control with proportional-differential control link can further release the FM potential of the DFPSU,increase the depth of its FM,effectively reduce the frequency deviation of the system and its rate of change,realize the optimization of the active output of the DFPSU and that of other units,and improve the frequency response capability of the system.
文摘Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.
文摘Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction model for postoperative recurrence in pancreatic cancer patients, incorporating multiple preoperative, intraoperative, and postoperative factors to assist clinical decision-making. Methods: A retrospective study was conducted on 216 patients who underwent surgical treatment for pancreatic malignancy at the First Affiliated Hospital of Chongqing Medical University between January 2015 and January 2023. An independent external validation cohort of 76 patients from the Second Affiliated Hospital of Chongqing Medical University was used to validate the model. Seven independent risk factors for postoperative recurrence were identified through univariate and multivariate Cox regression analyses. The model’s performance was evaluated using the concordance index (C-index) and ROC curves, and its accuracy and clinical value were assessed using calibration curves and decision curve analysis (DCA). Results: The predictive model demonstrated good discriminatory power, with a C-index of 0.72 in the training cohort and 0.66 in the validation cohort. The ROC curves for predicting recurrence at 3, 6, and 12 months postoperatively showed AUC values ranging from 0.72 to 0.83, indicating strong predictive value. Calibration curves and DCA confirmed the model’s accuracy and clinical utility. Conclusion: This study successfully developed and validated a clinical prediction model that incorporates seven independent risk factors for postoperative recurrence in pancreatic cancer. The model provides a useful tool for predicting recurrence risk, aiding in the identification of high-risk patients, and informing clinical decision-making.
文摘BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.
基金Supported by the Research Fund of Qiannan Medical College for Nationalities,No.Qnyz202222.
文摘BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed that CRC patients may experience postoperative cognitive dysfunction(POCD).AIM To establish a risk prediction model for POCD in CRC patients and investigate the preventive value of dexmedetomidine(DEX).METHODS A retrospective analysis was conducted on clinical data from 140 CRC patients who underwent surgery at the People’s Hospital of Qian Nan from February 2020 to May 2024.Patients were allocated into a modeling group(n=98)and a validation group(n=42)in a 7:3 ratio.General clinical data were collected.Additionally,in the modeling group,patients who received DEX preoperatively were incorporated into the observation group(n=54),while those who did not were placed in the control group(n=44).The incidence of POCD was recorded for both cohorts.Data analysis was performed using statistical product and service solutions 20.0,with t-tests orχ^(2) tests employed for group comparisons based on the data type.Least absolute shrinkage and selection operator regression was applied to identify influencing factors and reduce the impact of multicollinear predictors among variables.Multivariate analysis was carried out using Logistic regression.Based on the identified risk factors,a risk prediction model for POCD in CRC patients was developed,and the predictive value of these risk factors was evaluated.RESULTS Significant differences were observed between the cognitive dysfunction group and the non-cognitive dysfunction group in diabetes status,alcohol consumption,years of education,anesthesia duration,intraoperative blood loss,intraoperative hypoxemia,use of DEX during surgery,intraoperative use of vasoactive drugs,surgical time,systemic inflammatory response syndrome(SIRS)score(P<0.05).Multivariate Logistic regression analysis identified that diabetes[odds ratio(OR)=4.679,95%confidence interval(CI)=1.382-15.833],alcohol consumption(OR=5.058,95%CI:1.255-20.380),intraoperative hypoxemia(OR=4.697,95%CI:1.380-15.991),no use of DEX during surgery(OR=3.931,95%CI:1.383-11.175),surgery duration≥90 minutes(OR=4.894,95%CI:1.377-17.394),and a SIRS score≥3(OR=4.133,95%CI:1.323-12.907)were independent risk factors for POCD in CRC patients(P<0.05).A risk prediction model for POCD was constructed using diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score as factors.A receiver operator characteristic curve analysis of these factors revealed the model’s predictive sensitivity(88.56%),specificity(70.64%),and area under the curve(AUC)(AUC=0.852,95%CI:0.773-0.919).The model was validated using 42 CRC patients who met the inclusion criteria,demonstrating sensitivity(80.77%),specificity(81.25%),and accuracy(80.95%),and AUC(0.805)in diagnosing cognitive impairment,with a 95%CI:0.635-0.896.CONCLUSION Logistic regression analysis identified that diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score vigorously influenced the occurrence of POCD.The risk prediction model based on these factors demonstrated good predictive performance for POCD in CRC individuals.This study offers valuable insights for clinical practice and contributes to the prevention and management of POCD under CRC circumstances.
文摘BACKGROUND Sepsis is a severe complication in hospitalized patients with diabetic foot(DF),often associated with high morbidity and mortality.Despite its clinical significance,limited tools exist for early risk prediction.AIM To identify key risk factors and evaluate the predictive value of a nomogram model for sepsis in this population.METHODS This retrospective study included 216 patients with DF admitted from January 2022 to June 2024.Patients were classified into sepsis(n=31)and non-sepsis(n=185)groups.Baseline characteristics,clinical parameters,and laboratory data were analyzed.Independent risk factors were identified through multivariable logistic regression,and a nomogram model was developed and validated.The model's performance was assessed by its discrimination(AUC),calibration(Hosmer-Lemeshow test,calibration plots),and clinical utility[decision curve analysis(DCA)].RESULTS The multivariable analysis identified six independent predictors of sepsis:Diabetes duration,DF Texas grade,white blood cell count,glycated hemoglobin,Creactive protein,and albumin.A nomogram integrating these factors achieved excellent diagnostic performance,with an AUC of 0.908(95%CI:0.865-0.956)and robust internal validation(AUC:0.906).Calibration results showed strong agreement between predicted and observed probabilities(Hosmer-Lemeshow P=0.926).DCA demonstrated superior net benefit compared to extreme intervention scenarios,highlighting its clinical utility.CONCLUSION The nomogram prediction model,based on six key risk factors,demonstrates strong predictive value,calibration,and clinical utility for sepsis in patients with DF.This tool offers a practical approach for early risk stratification,enabling timely interventions and improved clinical management in this high-risk population.
基金Supported by 2024 Hospital-Level Research Start-up Fund,No.YK202426Suzhou Wujiang District"Science and Education for Health"Project,No.WWK202201Development Fund Project of the Affiliated Hospital of Xuzhou Medical University,No.XYFY202423.
文摘BACKGROUND Anxiety and depression are highly prevalent among patients with cervical cancer(CC).However,few studies have systematically analyzed the psychological effects of tumor stage,treatment methods,and related factors on these patients,or developed predictive models for these outcomes.AIM To identify factors influencing anxiety and depression in patients with CC and construct predictive models.METHODS We retrospectively analyzed data from 119 patients with CC treated at the Gynecology Department of Suzhou Ninth People’s Hospital between January 2017 and May 2025.Clinical data,psychological hope levels at diagnosis,and Self-Rating Anxiety Scale and Self-Rating Depression Scale scores during treatment were collected.Influencing factors were identified,and predictive models were developed.The model performance was evaluated using receiver operating characteristic(ROC)curves and the Hosmer-Lemeshow goodness-of-fit test.RESULTS During treatment,64.71%of the patients experienced anxiety and 52.10%experienced depression.Significant differences in family income,tumor stage,treatment modality,and hope level were observed between patients with and without anxiety/depression(P<0.05).Multivariate analysis showed that a family monthly income<5000 yuan,stage III-IV tumor,comprehensive treatment,and low hope level were independent risk factors(P<0.05).The predictive formula for anxiety was as follows:Logit(P)=0.795×monthly income+0.594×tumor stage+1.095×treatment method+1.184×hope level−9.176;for depression:Logit(P)=0.432×monthly income+0.518×tumor stage+0.727×treatment method+1.095×hope level−8.541.The area under the ROC curves were 0.865 for anxiety and 0.837 for depression.Goodness-of-fit test confirmed no overfitting(P>0.05).CONCLUSION Family income,tumor stage,treatment method,and hope level are key determinants of anxiety and depression in patients with CC.Predictive models incorporating these factors can effectively assess risk of anxiety and depression during treatment.
基金funded by Beijing Natural Science Foundation-Haidian Original Innovation Joint Fund(Grant No.L222103)the National Natural Science Foundation of China(Grant No.72174012)。
文摘Objectives:This study aimed to develop and validate a stroke risk prediction model based on machine learning(ML)and regional healthcare big data,and determine whether it may improve the prediction performance compared with the conventional Logistic Regression(LR)model.Methods:This retrospective cohort study analyzed data from the CHinese Electronic health Records Research in Yinzhou(CHERRY)(2015–2021).We included adults aged 18–75 from the platform who had established records before 2015.Individuals with pre-existing stroke,key data absence,or excessive missingness(>30%)were excluded.Data on demographic,clinical measures,lifestyle factors,comorbidities,and family history of stroke were collected.Variable selection was performed in two stages:an initial screening via univariate analysis,followed by a prioritization of variables based on clinical relevance and actionability,with a focus on those that are modifiable.Stroke prediction models were developed using LR and four ML algorithms:Decision Tree(DT),Random Forest(RF),eXtreme Gradient Boosting(XGBoost),and Back Propagation Neural Network(BPNN).The dataset was split 7:3 for training and validation sets.Performance was assessed using receiver operating characteristic(ROC)curves,calibration,and confusion matrices,and the cutoff value was determined by Youden's index to classify risk groups.Results:The study cohort comprised 92,172 participants with 436 incident stroke cases(incidence rate:474/100,000 person-years).Ultimately,13 predictor variables were included.RF achieved the highest accuracy(0.935),precision(0.923),sensitivity(recall:0.947),and F1 score(0.935).Model evaluation demonstrated superior predictive performance of ML algorithms over conventional LR,with training/validation areaunderthe curve(AUC)sof0.777/0.779(LR),0.921/0.918(BPNN),0.988/0.980(RF),0.980/0.955(DT),and 0.962/0.958(XGBoost).Calibration analysis revealed a better fit for DT,LR and BPNN compared to RF and XGBoost model.Based on the optimal performance of the RF model,the ranking of factors in descending order of importance was:hypertension,age,diabetes,systolic blood pressure,waist,high-density lipoprotein Cholesterol,fasting blood glucose,physical activity,BMI,low-density lipoprotein cholesterol,total cholesterol,dietary habits,and family history of stroke.Using Youden's index as the optimal cutoff,the RF model stratified individuals into high-risk(>0.789)and low-risk(≤0.789)groups with robust discrimination.Conclusions:The ML-based prediction models demonstrated superior performance metrics compared to conventional LR and the RF is the optimal prediction model,providing an effective tool for risk stratifi cation in primary stroke prevention in community settings.
基金supported by the National Natural Science Foundation of China(82371709).
文摘Testicular torsion is a urological emergency that requires prompt diagnosis and treatment,accounting for 10%-15%of cases of acute scrotum.[1]It occurs most frequently during the perinatal period and adolescence and can occur at any age.[2]The incidence of testicular torsion is 1/4,000 in males under 25 years of age and 1/160 in males over 25 years of age.[3]Unilateral torsion is relatively common,with a higher incidence on the left side.Testicular torsion is typically managed through surgical exploration.Necrotic testes,identified by a black appearance,require orchiectomy.[4]
文摘BACKGROUND Low rectal cancer poses a significant surgical challenge because of its close proximity to the anal sphincter,often requiring radical resection with permanent colostomy to achieve oncological safety.Revisited rectal anatomy,advances in surgical techniques and neoadjuvant therapies have enabled the possibility of sphincter-preserving procedures,however,it is uniformly not applicable.Selecting appropriate candidates for sphincter preservation is crucial,as an illadvised approach may compromise oncological outcome or lead to poor functional outcomes.Currently there is no consensus-which clinical,anatomical,or molecular factors most accurately predict the feasibility of sphincter-preserving surgery(SPS)in this subset of patients.By identifying these predictors,the study seeks to support improved patient selection,enhance surgical planning,and ultimately contribute to better functional and oncological outcomes in patients with low rectal cancer.AIM To identify predictive factors that determine the feasibility of SPS in patients with low rectal cancer.METHODS A comprehensive literature search was conducted using PubMed/MEDLINE databases.The search focused on various factors influencing the feasibility of SPS in low rectal cancer.These included patient-related factors,anatomical considerations,findings from different imaging modalities,advancements in diagnostic tools and techniques,and the role of neoadjuvant chemoradiotherapy.The relevance of each factor in predicting the potential for sphincter preservation was critically analyzed and presented based on the current evidence RESULTS Multiple studies have identified a range of predictive factors influencing the feasibility of SPS in low rectal cancer.Patient-related factors include age,sex,preoperative continence status,comorbidities,and body mass index.Anatomical considerations,such as tumor distance from the anal verge,involvement of the external anal sphincter,and levator ani muscles,also play a critical role.Additionally,a favourable response to neoadjuvant chemoradiotherapy has been associated with improved suitability for sphincter preservation.Several biomarkers,such as inflammatory markers like interleukins and C-reactive protein,as well as tumor markers like carcinoembryonic antigen,are important.Molecular markers,including BRAF and KRAS mutations and microsatellite instability status,have been linked to prognosis and may further guide decision-making regarding sphincter-preserving approaches.Artificial intelligence(AI)can further add in to select an ideal patient for sphincter preservation.CONCLUSION SPS is feasible in low rectal cancer and depends on patient factors,tumor anatomy and biology,preoperative treatment response,and biomarkers.In addition,tools and technology including AI can further help in selecting an ideal patient for long term optimal outcome.
基金supported by the Capital’s Funds for Health Improve-ment and Research(grant number:2024-1G-4023)。
文摘Background:Existing hepatocellular carcinoma(HCC)prediction models lack transferability and generalizability when applied to female populations,resulting in diminished performance and inadequate tools for accurate HCC risk stratification among females.This study aims to develop and validate a score-based prediction model for early detection of HCC in female hepatitis B surface antigen(HBsAg)carriers.Methods:Participants were recruited from a multicenter prospective cohort engaged in liver cancer screening across China including seven high-risk rural areas and one additional high-risk rural area.The study involved 7080 females as the derivation cohort and 2069 as the validation cohort,with all participants aged 35-70 years and HBsAg positive.Laboratory tests and epidemiological surveys were conducted.Key predictor variables were identified through LASSO regression analysis,and score-based prediction models were developed based on Cox proportional hazards model.Model performance including discrimination and calibration was evaluated,and compared to existing prediction models and screening strategies.Results:After a median follow-up of 3.69 and 5.42 years,147 and 45 HCC cases were identified in the derivation and validation cohorts,respectively.The female HCC(HCCF)model incorporating five independent variables:age,α-fetoprotein(AFP),albumin,alanine aminotransferase,and platelet,showed excellent performance with an area under the receiver operating characteristic curve(AUC)of 0.82(95%CI:0.78-0.86).The HCCF-Enhanced model which included cirrhosis,achieved an AUC of 0.85(95%CI:0.81-0.89).Both models demonstrated superior predictive performance than existing models,with strong predictive accuracy in the validation cohort:AUCs of 0.83(95%CI:0.77-0.89)and 0.88(95%CI:0.83-0.92),respectively.The HCCF model,at a score threshold of 7,achieved the largest Youden’s index and identified 32.80%of high-risk individuals.When combined with ultrasonography(US),the model detected 37 additional cases,significantly improved screening sensitivity and accuracy compared to the traditional AFP plus US strategy.Conclusions:The developed HCCF models with good performance for HCC prediction in HBsAg-positive females significantly improve screening efficiency and provide an effective tool for surveillance,ultimately helping to optimize prevention and management strategies for HCC.
文摘BACKGROUND In cases of colorectal cancer(CRC)with obstruction,patients experience local tissue edema due to intestinal obstruction.This condition stimulates the accumulation of inflammatory factors,activates cancer cells,and increases the risk of tumor recurrence.At present,analyses and evaluation tools for factors influencing early postoperative recurrence in patients with CRC and obstruction are limited.AIM To explore the influencing factors and construct a predictive model of the early postoperative recurrence of CRC with obstruction.METHODS Data from 181 patients with CRC and obstruction who underwent surgery in the Department of Gastrointestinal Surgery,Suzhou Ninth Hospital Affiliated to Soochow University,between January 2017 and May 2023 were retrospectively collected.Patients with CRC and obstruction were divided into a recurrence group and a non-recurrence group based on whether recurrence occurred during the 2-year follow-up after surgery.Datasets from the two groups were compared.Subsequently,multiple logistic regression was employed to analyze the influencing factors of the early postoperative recurrence of CRC with obstruction.The nomogram prediction model was drawn using R software,and its performance was evaluated by the goodness of fit test and receiver operating characteristic(ROC)curve analysis.The clinical benefit rate of the model was evaluated by decision curves.RESULTS Among the 181 patients with CRC and obstruction,52(28.73%)experienced tumor recurrence within 2 years after surgery.Significant differences were observed in preoperative carcinoembryonic antigen(CEA),preoperative systemic immuneinflammation index(SII),tumor,node,and metastasis(TNM)stage,differentiation degree,nerve infiltration,and Ki-67 expression between the recurrence and non-recurrence groups(P<0.05).Multivariate logistic regression analysis showed that high preoperative CEA(OR=2.094,P=0.008),high preoperative SII(OR=2.795,P<0.001),TNM stage III(OR=1.644,P=0.027),poor differentiation(OR=1.861,P=0.035),and high Ki-67 expression(OR=2.467,P=0.001)were all influencing factors for early postoperative recurrence of CRC with obstruction.The area under the ROC curve of the nomograph model constructed based on this was 0.890,the goodness of fit deviation test was conducted(χ^(2)=3.903,P=0.866),and the decision curve display model demonstrated practical value in clinical practice.CONCLUSION The early recurrence rate of CRC with obstruction is high.CEA,SII,TNM staging,differentiation degree,and Ki-67 expression are factors related to early postoperative recurrence.A nomogram prediction model incorporating these factors can effectively evaluate the risk of early postoperative recurrence in patients with CRC.
基金Supported by the National Natural Science Foundation of China(No.82220108017,No.82141128,No.82101180)Beijing Natural Science Foundation(No.Z220012)+3 种基金The Capital Health Research and Development of Special(No.2020-1-2052)Science&Technology Project of Beijing Municipal Science&Technology Commission(No.Z201100005520045)Sanming Project of Medicine in Shenzhen(No.SZSM202311018)Beijing Science&Technology Development of TCM(No.BJZYYB-2023-17).
文摘AIM:To explore the relationship between matrix metalloproteinases(MMPs)expression levels in the tumor and the prognosis of uveal melanoma(UM)and to construct prognostic prediction models.METHODS:Transcriptome sequencing data from 17 normal choroid tissues and 53 UM tumor tissues were collected.Based on the differential gene expression levels and their function,MMPs family was selected for establishing risk-score system and prognostic prediction model with machine learning.Tumor microenvironment(TME)analysis was also applied for the impact of immune cell infiltration on prognosis of the disease.RESULTS:Eight MMPs were significantly different expression levels between normal and the tumor tissues.MMP-2 and MMP-28 were selected to construct a risk-score system and divided patients accordingly into high-and low-risk groups.The prediction model based on the risk-score achieved an accuracy of approximately 80%at 1-,3-,and 5-year after diagnosis.Besides,a Nomogram prognostic prediction model which based on risk-score and pathological type(independent prognostic factors after Cox regression analysis)demonstrated good consistency between the predicted outcomes at 1-,3-,and 5-year after diagnosis and the actual prognosis of patients.TME analysis revealed that the high-risk group exhibited higher immune and stromal scores and increased infiltration of tumor-associated macrophages(TAMs)and regulatory T cells compared to the low-risk group.CONCLUSION:Based on MMP-2 and MMP-28 expression levels,our prediction model demonstrates accurate long-term prognosis prediction for UM patients.The aggregation of TAMs and regulatory T cells in the TME of UM may be associated with an unfavorable prognosis.
基金Supported by the National Key Research and Development Program of China,No.2022YFC2305002Beijing Natural Science Foundation,No.7232079+1 种基金Middle-aged and Young Talent Incubation Programs(Clinical Research)of Beijing Youan Hospital,No.BJYAYY-YN2022-12,No.BJYAYY-YN2022-13,and No.BJYAYY-YN2022-01the China Postdoctoral Science Foundation,No.2023M732410 and No.2024T170595.
文摘BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high.AIM To compare the clinical characteristics of patients with cirrhosis,cirrhosis combined with intrapulmonary vascular dilatation(IPVD),and HPS,and to establish predictive models for IPVD and HPS.METHODS Patients with cirrhosis were prospectively screened at a liver-specialized university teaching hospital.Clinical information and blood samples were collected,and biomarker levels in blood samples were measured.Patients with cirrhosis were divided into three groups:Those with pure cirrhosis,those with combined IPVD,and those with HPS based on contrast-enhanced transthoracic echocardiography results and the pulmonary alveolar-arterial oxygen gradient values.Univariate logistic regression and Least Absolute Shrinkage and Selection Operator(LASSO)regression methods were utilized to identify risk factors for IPVD and HPS,and nomograms were constructed to predict IPVD and HPS.RESULTS A total of 320 patients were analyzed,with 101 diagnosed with IPVD,of whom 54 were diagnosed with HPS.There were statistically significant differences in clinical parameters among these three groups of patients.Among the tested biomarkers,sphingosine 1 phosphate,angiopoietin-2,and platelet-derived growth factor BB were significantly associated with IPVD and HPS in patients with cirrhosis.Following LASSO logistic regression screening,prediction models for IPVD and HPS were established.The area under the receiver operating characteristic curve for IPVD prediction was 0.792(95%confidence interval[CI]:0.737-0.847),and for HPS prediction was 0.891(95%CI:0.848-0.934).CONCLUSION This study systematically compared the clinical characteristics of patients with cirrhosis,IPVD,and HPS,and constructed predictive models for IPVD and HPS based on clinical parameters and laboratory indicators.These models showed good predictive value for IPVD and HPS in patients with cirrhosis.They can assist clinicians in the early prognosis assessment of patients with cirrhosis,ultimately benefiting the patients.
