NOAA AVHRR data from the Bay of Biscay between 1988 and 1990 have been examined in order to extract information on the fluctuations of sea surface temperature (SST) at the diurnal time scale. The temporal and spatia...NOAA AVHRR data from the Bay of Biscay between 1988 and 1990 have been examined in order to extract information on the fluctuations of sea surface temperature (SST) at the diurnal time scale. The temporal and spatial distributions of diurnal warming in the area are obtained. The diurnal warming occurs during the summer months. Large diurnal warming in excess of 1℃ is found within 100 km along the west coast of France and within 30 km along the north coast of Spain. In the central Bay of Biscay the diurnal warming is typically about 0.5℃. The diurnal warming up to 6℃ is observed occasionally in the coastal areas where the wind speed is very low. A one-dimensional oceanic mixed-layer model has been used to simulate the diurnal warming. The results demonstrate that the diurnal warming increases with the decrease of the wind speed and the increase of the net heat flux. The comparison shows that the model results are in good agreement with the satellite measurements.展开更多
In the municipal solid waste incineration process,it is difficult to effectively control the gas oxygen content by setting the air flow according to artificial experience.To address this problem,this paper proposes an...In the municipal solid waste incineration process,it is difficult to effectively control the gas oxygen content by setting the air flow according to artificial experience.To address this problem,this paper proposes an optimization control method of gas oxygen content based on model predictive control.First,a stochastic configuration network is utilized to establish a prediction model of gas oxygen content.Second,an improved differential evolution algorithm that is based on parameter adaptive and t-distribution strategy is employed to address the set value of air flow.Finally,model predictive control is combined with the event triggering strategy to reduce the amount of computation and the controller's frequent actions.The experimental results show that the optimization control method proposed in this paper obtains a smaller degree of fluctuation in the air flow set value,which can ensure the tracking control performance of the gas oxygen content while reducing the amount of calculation.展开更多
Considering the actual demand for high-speed operation of induction motors in industrial occasions,the characteristics of induction motors in different regions are analyzed,especially the field weakening characteristi...Considering the actual demand for high-speed operation of induction motors in industrial occasions,the characteristics of induction motors in different regions are analyzed,especially the field weakening characteristics of induction motors in high-speed operation are studied.A field weakening control method of induction motor based on model predictive control(MPC)algorithm is proposed,which can predict the future state of the controlled object,and then obtain the optimal control variables by colling optimization.The simulation results show that the field-weakening control method based on MPC algorithm has faster response speed,stronger robustness and better control performance than the traditional control methods.展开更多
Objective:To investigate the impact of targeted nursing interventions based on frailty prediction models on peri-hospitalization clinical outcomes in middle-aged and elderly patients with upper gastrointestinal bleedi...Objective:To investigate the impact of targeted nursing interventions based on frailty prediction models on peri-hospitalization clinical outcomes in middle-aged and elderly patients with upper gastrointestinal bleeding(UGIB).Methods:A prospective cohort study was conducted,and 126 middle-aged and elderly patients with UGIB admitted from August 2024 to August 2025 were selected as the study subjects.The patients were divided into the intervention group(63 cases)and the control group(63 cases)based on whether they received nursing intervention based on frailty prediction models.The control group received routine care,while the intervention group,on the basis of routine care,used the FRAIL scale combined with laboratory indicators(albumin,hemoglobin,etc.)to establish a predictive model to evaluate patients within 24 hours of admission,and implemented multi-dimensional targeted nursing intervention for pre-frailty or frailty patients screened out.The incidence of frailty,rebleeding rate,average length of stay,hospitalization cost,and nursing satisfaction during hospitalization were compared between the two groups.Results:The incidence of frailty during hospitalization in the intervention group was 11.1%(7 cases/63 cases),significantly lower than 31.7%(20 cases/63 cases)in the control group,and the difference was statistically significant(p<0.05).The rebleeding rate of 4.8%vs 12.7%,the average length of stay of(7.2±1.5)days vs(9.1±2.2)days,and the average hospitalization cost of(23,000±6,000)yuan vs(28,000±7,000)yuan in the intervention group were all lower than those in the control group(all p<0.05).The nursing satisfaction score of the intervention group(93.5±4.2)points was higher than that of the control group(86.3±5.8)points(p<0.05).Conclusion:The frailty prediction model applied to the peri-hospitalization care of middle-aged and elderly patients with UGIB can effectively identify frailty risk.Through early targeted intervention,the incidence of frailty and rebleeding rate can be reduced,the length of hospital stay can be shortened,medical expenses can be reduced,and nursing satisfaction can be improved,which has clinical promotion value.展开更多
Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy...Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.展开更多
Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused ...Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels.展开更多
The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine b...The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine blade,wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation.Furthermore,the fine micro surface structure brings unavoidable manufacturing errors,and the probability prediction contributes to gaining the confidence interval of the results.Therefore,a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency.Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment,the relay-based modeling has been achieved through knowledge transferring,and the confidence interval can be provided by the Gaussian Process Regression(GPR)model.The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma=0.64,0.74,0.84 have been evaluated using the Relay-Based Probabilistic(RBP)model.The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven(SFDD)prediction model,which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.展开更多
Postoperative infection is a major global health concern,affecting 5%-10%of surgical patients and nearly doubling mortality in severe cases[1].Transplant recipients are particularly vulnerable,with 30%-80%developing i...Postoperative infection is a major global health concern,affecting 5%-10%of surgical patients and nearly doubling mortality in severe cases[1].Transplant recipients are particularly vulnerable,with 30%-80%developing infections within 30 days,often from opportunistic pathogens[2,3].Key risk factors include epidemiological exposure,net immunosuppression,age,transplant type,and surgical history[4].Despite known infection risks,current evidence remains transplantation type-specific and neglects behavioral modulators[5].Different types of transplantation may share similar risk factors[6].To identify common factors affecting postoperative infection,this study collected standardized clinical data-including diet,psychological response,medication use,and biochemical indicators-from liver and kidney transplant patients across six hospitals using a unified standard operating procedure(SOP).展开更多
Extreme precipitation is one of the most severe weather of high socio-economic impact.In the warming climate,extreme precipitation has been shown to increase in both intensity and frequency over China and over the wor...Extreme precipitation is one of the most severe weather of high socio-economic impact.In the warming climate,extreme precipitation has been shown to increase in both intensity and frequency over China and over the world[[1],[2],[3]].On 20 July 2021,an extreme precipitation event hit Zhengzhou,a highly urbanized megacity with a dense population of 12.8 million in North China.The hourly rainfall rate was up to 201.9 mm.展开更多
BACKGROUND Post-endoscopic retrograde cholangiopancreatography(ERCP)pancreatitis is a common complication of the procedure.The effective prevention of post-ERCP pancreatitis(PEP)remains a key focus of clinical researc...BACKGROUND Post-endoscopic retrograde cholangiopancreatography(ERCP)pancreatitis is a common complication of the procedure.The effective prevention of post-ERCP pancreatitis(PEP)remains a key focus of clinical research.AIM To develop a prediction model for PEP based on multidimensional clinical indicators and evaluate its clinical application value.METHODS We retrospectively analyzed 183 patients with biliary tract diseases who underwent ERCP at Xuzhou Medical University from January 2020 to June 2023,divided into non-PEP(n=159)and PEP(n=24)groups based on PEP development.Baseline and intraoperative data were compared,and PEP-related factors examined via univariate and multivariate logistic regression.Using R,70%of patients were assigned to training and 30%to testing sets for PEP prediction model development.Model accuracy was evaluated using a calibration curve and receiver operating characteristic(ROC)area under the curve(AUC).RESULTS Age,total cholesterol level,history of pancreatitis,pancreatic ductography,bleeding,and intubation time differed significantly between the two groups when baseline data and intraoperative conditions were compared(P<0.05).Multifactorial logistic regression analysis demonstrated that age[odds ratio(OR)=0.192,95%confidence interval(CI):0.053-0.698],total cholesterol(OR=0.324,95%CI:0.152-0.694),history of pancreatitis(OR=6.159,95%CI:1.770-21.434),pancreatography(OR=3.726,95%CI:1.028-13.507),and bleeding(OR=3.059,95%CI:1.001-9.349)were independently associated with acute pancreatitis after ERCP.The predictive probabilities from the calibration curves had mean errors of 0.021 and 0.030,with ROC AUCs of 0.840 and 0.797 in the training and test sets,respectively.CONCLUSION Age,total cholesterol,pancreatitis history,pancreatic ductography,and bleeding influence the risk of acute PEP.A model incorporating these factors may aid early detection and intervention.展开更多
Digital modeling and autonomous control of the die forging process are significant challenges in realizing high-quality intelli-gent forging of components.Using the die forging of AA2014 aluminum alloy as a case study...Digital modeling and autonomous control of the die forging process are significant challenges in realizing high-quality intelli-gent forging of components.Using the die forging of AA2014 aluminum alloy as a case study,a machine-learning-assisted method for di-gital modeling of the forging force and autonomous control in response to forging parameter disturbances was proposed.First,finite ele-ment simulations of the forging processes were conducted under varying friction factors,die temperatures,billet temperatures,and for-ging velocities,and the sample data,including process parameters and forging force under different forging strokes,were gathered.Pre-diction models for the forging force were established using the support vector regression algorithm.The prediction error of F_(f),that is,the forging force required to fill the die cavity fully,was as low as 4.1%.To further improve the prediction accuracy of the model for the ac-tual F_(f),two rounds of iterative forging experiments were conducted using the Bayesian optimization algorithm,and the prediction error of F_(f) in the forging experiments was reduced from 6.0%to 1.5%.Finally,the prediction model of F_(f) combined with a genetic algorithm was used to establish an autonomous optimization strategy for the forging velocity at each stage of the forging stroke,when the billet and die temperatures were disturbed,which realized the autonomous control in response to disturbances.In cases of−20 or−40℃ reductions in the die and billet temperatures,forging experiments conducted with the autonomous optimization strategy maintained the measured F_(f) around the target value of 180 t,with the relative error ranging from−1.3%to+3.1%.This work provides a reference for the study of di-gital modeling and autonomous optimization control of quality factors in the forging process.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
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
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: 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 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 Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with ment...BACKGROUND Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with mental disorders induced by rabies vaccination and to construct a risk prediction model to inform strategies for improving patients’mental health.METHODS Patients who received rabies vaccinations at the Department of Infusion Yiwu Central Hospital between August 2024 and July 2025 were included,totaling 384 cases.Data were collected from medical records and included demographic characteristics(age,gender,occupation),lifestyle habits,and details regarding vaccine type,dosage,and injection site.The incidence of psychiatric disorders following vaccination was assessed using standardized anxiety and depression rating scales.Patients were categorized into two groups based on the presence or absence of anxiety and depression symptoms:The psychiatric disorder group and the non-psychiatric disorder group.Differences between the two groups were compared,and high-risk factors were identified using multivariate logistic regression analysis.A predictive model was then developed based on these factors to evaluate its predictive performance.RESULTS Among the 384 patients who received rabies vaccinations,36 cases(9.38%)were diagnosed with anxiety,52 cases(13.54%)with depression,and 88 cases(22.92%)with either condition.Logistic regression analysis identified the following signi ficant risk factors for psychiatric disorders:Education level of primary school or below,exposure site at the head and neck,exposure classified as grade III,family status of divorced/widowed/unmarried/living alone,number of wounds greater than one,and low awareness of rabies prevention and control(P<0.05).The risk prediction model demonstrated good performance,with an area under the receiver operating characteristic curve of 0.859,a specificity of 74.42%,and a sensitivity of 93.02%.CONCLUSION In real-world settings,psychiatric disorders following rabies vaccination are relatively common and are associated with factors such as lower education level,higher exposure severity,vulnerable family status,and limited awareness of rabies prevention and control.The developed risk prediction model may aid in early identification of high-risk individuals and support timely clinical intervention.展开更多
Objective:To construct a clinical prediction model of acupuncture treatment for diminished ovarian reserve(DOR)based on a machine learning algorithm to provide a clinical prediction of acupuncture for ameliorating pre...Objective:To construct a clinical prediction model of acupuncture treatment for diminished ovarian reserve(DOR)based on a machine learning algorithm to provide a clinical prediction of acupuncture for ameliorating pregnancy outcomes in DOR.Methods:We enrolled 377 DOR patients treated with acupuncture and with records of pregnancy outcomes(139 cases of pregnancy and 238 cases failed)exported from the International Patient Registry Platform of Acupuncture-moxibustion(IPRPAM).The predictive variables were determined using Spearman’s correlation analysis and feature engineering methods.The model was constructed by adopting logistic regression,naïve Bayes,random forest,support vector machine,extreme gradient boosting,the knearest neighbor algorithm,linear discriminant analysis,and neural network methods.The models were validated by the area under the curve(AUC),accuracy(ACC),and importance sequencing,and individual pregnancy prediction was conducted for the best-performing model.Results:The key factors determining pregnancy after acupuncture in patients with DOR were age,luteinizing hormone(LH)level after treatment,follicle-stimulating hormone(FSH)level after treatment,the ratio of FSH to LH(FSH/LH)after treatment,and history of acupuncture treatment.Random forest model ACC was 0.95,Fβwas 0.93,Logloss was 0.30,Logloss value was the lowest,the model variables exhibited the highest accuracy and precision.Conclusion:The random forest model for the effects of acupuncture on pregnancy outcomes in patients with DOR,constructed based on the IPRPAM,presents a favorable value for clinical application.展开更多
BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as...BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as current tools may not fully capture the unique risks in this population.This study hypothesizes that a nomogram incorporating specific risk factors will improve hypertension risk prediction in T2DM patients.AIM To develop and validate a nomogram prediction model for hypertension in T2DM patients.METHODS A retrospective observational study was conducted using data from 26850 T2DM patients from the Anhui Provincial Primary Medical and Health Information Management System(2022 to 2024).The study included patients aged 18 and above with available data on key variables.Exclusion criteria were type 1 diabetes,gestational diabetes,insufficient data,secondary hypertension,and abnormal liver and kidney function.The Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression were used to construct the nomogram,which was validated on separate datasets.RESULTS The developed nomogram for T2DM patients incorporated age,low-density lipoprotein,body mass index,diabetes duration,and urine protein levels as key predictive factors.In the training dataset,the model demonstrated a high discriminative power with an area under the receiver operating characteristic curve(AUC)of 0.823,indicating strong predictive accuracy.The validation dataset confirmed these findings with an AUC of 0.812.The calibration curve analysis showed excellent agreement between predicted and observed outcomes,with absolute errors of 0.017 for the training set and 0.031 for the validation set.The Hosmer-Lemeshow test yielded non-significant results for both sets(χ^(2)=7.066,P=0.562 for training;χ^(2)=6.122,P=0.709 for validation),suggesting good model fit.CONCLUSION The nomogram effectively predicts hypertension risk in T2DM patients,offering a valuable tool for personalized risk assessment and guiding targeted interventions.This model provides a significant advancement in the management of T2DM and hypertension comorbidity.展开更多
The comprehensive status of blast furnaces was one of the most important factors affecting their economy,quality,and longev-ity.The blast furnace comprehensive status had the nature of“black box,”and it was“unpredi...The comprehensive status of blast furnaces was one of the most important factors affecting their economy,quality,and longev-ity.The blast furnace comprehensive status had the nature of“black box,”and it was“unpredictable.”In this study,a blast furnace com-prehensive status score and prediction method based on a cascade system and a combined model were proposed to address this issue.A dual cascade evaluation system was developed by integrating subjective and objective weighting methods.The analytic hierarchy process,coefficient of variation,entropy weight method,and impart combinatorial games were jointly employed to determine the optimal weight distribution across indicators.Categorized statuses(raw material,gas flow,furnace body,furnace cylinder,and iron-slag)were evaluated.Based on the five categories of the status data,the second cascade was applied to upgrade the quantitative evaluation of the comprehens-ive status.The weights of the different categories were 0.22,0.15,0.22,0.21,and 0.20,respectively.According to the data analysis,the results of the comprehensive status score closely matched the on-site production logs.Based on the blast furnace smelting period,the maximal information coefficient method was applied to the 100 parameters that were most relevant to the comprehensive status.A com-bined prediction model for a comprehensive status score was designed using bidirectional long short-term memory(BiLSTM)and categorical boosting(CatBoost).The test results indicated that the combined model reduced the mean absolute error by an average of 0.275 and increased the hit rate by an average of 5.65 percentage points compared to BiLSTM or CatBoost alone.When the er-ror range was±2.5,the combined model predicted a hit rate of 91.66%for the next hour’s comprehensive status score,and its high accur-acy was deemed satisfactory for the field.SHapley Additive exPlanations(SHAP)and regression fitting were applied to analyze the lin-ear quantitative relationship between the key variables and the comprehensive status score.When the furnace bottom center temperature was increased by 10℃,the comprehensive status score increased by 0.44.This method contributes to a more precise management and control of the comprehensive status of the blast furnace on-site.展开更多
基金supported by the UK Science and Engineering Research Council under contract! GR3/7612.
文摘NOAA AVHRR data from the Bay of Biscay between 1988 and 1990 have been examined in order to extract information on the fluctuations of sea surface temperature (SST) at the diurnal time scale. The temporal and spatial distributions of diurnal warming in the area are obtained. The diurnal warming occurs during the summer months. Large diurnal warming in excess of 1℃ is found within 100 km along the west coast of France and within 30 km along the north coast of Spain. In the central Bay of Biscay the diurnal warming is typically about 0.5℃. The diurnal warming up to 6℃ is observed occasionally in the coastal areas where the wind speed is very low. A one-dimensional oceanic mixed-layer model has been used to simulate the diurnal warming. The results demonstrate that the diurnal warming increases with the decrease of the wind speed and the increase of the net heat flux. The comparison shows that the model results are in good agreement with the satellite measurements.
基金supported by the National Natural Science Foundation of China(62373017,62073006)and the Beijing Natural Science Foundation of China(4212032)。
文摘In the municipal solid waste incineration process,it is difficult to effectively control the gas oxygen content by setting the air flow according to artificial experience.To address this problem,this paper proposes an optimization control method of gas oxygen content based on model predictive control.First,a stochastic configuration network is utilized to establish a prediction model of gas oxygen content.Second,an improved differential evolution algorithm that is based on parameter adaptive and t-distribution strategy is employed to address the set value of air flow.Finally,model predictive control is combined with the event triggering strategy to reduce the amount of computation and the controller's frequent actions.The experimental results show that the optimization control method proposed in this paper obtains a smaller degree of fluctuation in the air flow set value,which can ensure the tracking control performance of the gas oxygen content while reducing the amount of calculation.
基金National Natural Science Foundation of China(No.61663022)Changjiang Scholars and Innovaton Team Develpment Plan(No.Rt_16R36)。
文摘Considering the actual demand for high-speed operation of induction motors in industrial occasions,the characteristics of induction motors in different regions are analyzed,especially the field weakening characteristics of induction motors in high-speed operation are studied.A field weakening control method of induction motor based on model predictive control(MPC)algorithm is proposed,which can predict the future state of the controlled object,and then obtain the optimal control variables by colling optimization.The simulation results show that the field-weakening control method based on MPC algorithm has faster response speed,stronger robustness and better control performance than the traditional control methods.
基金Construction and Application of Frailty Trajectory Prediction Model for Middle-aged and Elderly Patients with Upper Gastrointestinal Bleeding,Project Source:Sichuan Vocational College of Nursing(Project No.:2024ZRY25)。
文摘Objective:To investigate the impact of targeted nursing interventions based on frailty prediction models on peri-hospitalization clinical outcomes in middle-aged and elderly patients with upper gastrointestinal bleeding(UGIB).Methods:A prospective cohort study was conducted,and 126 middle-aged and elderly patients with UGIB admitted from August 2024 to August 2025 were selected as the study subjects.The patients were divided into the intervention group(63 cases)and the control group(63 cases)based on whether they received nursing intervention based on frailty prediction models.The control group received routine care,while the intervention group,on the basis of routine care,used the FRAIL scale combined with laboratory indicators(albumin,hemoglobin,etc.)to establish a predictive model to evaluate patients within 24 hours of admission,and implemented multi-dimensional targeted nursing intervention for pre-frailty or frailty patients screened out.The incidence of frailty,rebleeding rate,average length of stay,hospitalization cost,and nursing satisfaction during hospitalization were compared between the two groups.Results:The incidence of frailty during hospitalization in the intervention group was 11.1%(7 cases/63 cases),significantly lower than 31.7%(20 cases/63 cases)in the control group,and the difference was statistically significant(p<0.05).The rebleeding rate of 4.8%vs 12.7%,the average length of stay of(7.2±1.5)days vs(9.1±2.2)days,and the average hospitalization cost of(23,000±6,000)yuan vs(28,000±7,000)yuan in the intervention group were all lower than those in the control group(all p<0.05).The nursing satisfaction score of the intervention group(93.5±4.2)points was higher than that of the control group(86.3±5.8)points(p<0.05).Conclusion:The frailty prediction model applied to the peri-hospitalization care of middle-aged and elderly patients with UGIB can effectively identify frailty risk.Through early targeted intervention,the incidence of frailty and rebleeding rate can be reduced,the length of hospital stay can be shortened,medical expenses can be reduced,and nursing satisfaction can be improved,which has clinical promotion value.
基金financially supported by the National Key Research and Development Program of China (No. 2023YFB3812601)the National Natural Science Foundation of China (No. 51925401)the Young Elite Scientists Sponsorship Program by CAST, China (No. 2022QNRC001)。
文摘Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.
基金financially supported by the National Natural Science Foundation of China(Nos.42577209 and U22A20239)the Key R&D Program of Hunan Province(No.2024WK2004)the Key Technologies for Accurate Diagnosis and Intelligent Prevention and Control of Slope Hazards in Open pit Mines,181 Major R&D projects of Metallurgical Corporation of China Ltd。
文摘Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels.
基金supported by the National Natural Science Foundation of China(No.12301672)the Shanghai Science and Technology Innovation Action Plan(Yangfan Special Project),China(No.23YF1401300)。
文摘The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine blade,wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation.Furthermore,the fine micro surface structure brings unavoidable manufacturing errors,and the probability prediction contributes to gaining the confidence interval of the results.Therefore,a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency.Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment,the relay-based modeling has been achieved through knowledge transferring,and the confidence interval can be provided by the Gaussian Process Regression(GPR)model.The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma=0.64,0.74,0.84 have been evaluated using the Relay-Based Probabilistic(RBP)model.The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven(SFDD)prediction model,which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.
基金the MOST Key Research and Development Program of China(grant number 2022YFC2304703)the Natural Science Foundation of China(grant number 32422004)+5 种基金The Medicine and Engineering Interdisciplinary Research Fund of Shanghai Jiao Tong University(grant number 24X010301328)the Natural Science Foundation of China(grant number 32270202)the Computational Biology Program of Science and Technology Commission of Shanghai Municipality(STCSM)(grant number 25JS2810200)the MOST Key Research and Development Program of China(grant number 2020YFA0907200)Program of Shanghai Academic Research Leader(grant number 23XD1422300)Innovative research team of high-level local universities in Shanghai.All funding sources are attributed to N.N.L.
文摘Postoperative infection is a major global health concern,affecting 5%-10%of surgical patients and nearly doubling mortality in severe cases[1].Transplant recipients are particularly vulnerable,with 30%-80%developing infections within 30 days,often from opportunistic pathogens[2,3].Key risk factors include epidemiological exposure,net immunosuppression,age,transplant type,and surgical history[4].Despite known infection risks,current evidence remains transplantation type-specific and neglects behavioral modulators[5].Different types of transplantation may share similar risk factors[6].To identify common factors affecting postoperative infection,this study collected standardized clinical data-including diet,psychological response,medication use,and biochemical indicators-from liver and kidney transplant patients across six hospitals using a unified standard operating procedure(SOP).
基金supported by the National Key Research and Development Program of China(2023YFC3007502)the National Natural Science Foundation of China(42122036)the Fundamental Research Funds for the Central Universities(14380222).
文摘Extreme precipitation is one of the most severe weather of high socio-economic impact.In the warming climate,extreme precipitation has been shown to increase in both intensity and frequency over China and over the world[[1],[2],[3]].On 20 July 2021,an extreme precipitation event hit Zhengzhou,a highly urbanized megacity with a dense population of 12.8 million in North China.The hourly rainfall rate was up to 201.9 mm.
文摘BACKGROUND Post-endoscopic retrograde cholangiopancreatography(ERCP)pancreatitis is a common complication of the procedure.The effective prevention of post-ERCP pancreatitis(PEP)remains a key focus of clinical research.AIM To develop a prediction model for PEP based on multidimensional clinical indicators and evaluate its clinical application value.METHODS We retrospectively analyzed 183 patients with biliary tract diseases who underwent ERCP at Xuzhou Medical University from January 2020 to June 2023,divided into non-PEP(n=159)and PEP(n=24)groups based on PEP development.Baseline and intraoperative data were compared,and PEP-related factors examined via univariate and multivariate logistic regression.Using R,70%of patients were assigned to training and 30%to testing sets for PEP prediction model development.Model accuracy was evaluated using a calibration curve and receiver operating characteristic(ROC)area under the curve(AUC).RESULTS Age,total cholesterol level,history of pancreatitis,pancreatic ductography,bleeding,and intubation time differed significantly between the two groups when baseline data and intraoperative conditions were compared(P<0.05).Multifactorial logistic regression analysis demonstrated that age[odds ratio(OR)=0.192,95%confidence interval(CI):0.053-0.698],total cholesterol(OR=0.324,95%CI:0.152-0.694),history of pancreatitis(OR=6.159,95%CI:1.770-21.434),pancreatography(OR=3.726,95%CI:1.028-13.507),and bleeding(OR=3.059,95%CI:1.001-9.349)were independently associated with acute pancreatitis after ERCP.The predictive probabilities from the calibration curves had mean errors of 0.021 and 0.030,with ROC AUCs of 0.840 and 0.797 in the training and test sets,respectively.CONCLUSION Age,total cholesterol,pancreatitis history,pancreatic ductography,and bleeding influence the risk of acute PEP.A model incorporating these factors may aid early detection and intervention.
基金financially supported by the National Key Research and Development Program of China(No.2022YFB3706901)the National Natural Science Foundation of China(No.52090041)the Young Elite Scientists Sponsorship Program by CAST(No.2022QNRC 001).
文摘Digital modeling and autonomous control of the die forging process are significant challenges in realizing high-quality intelli-gent forging of components.Using the die forging of AA2014 aluminum alloy as a case study,a machine-learning-assisted method for di-gital modeling of the forging force and autonomous control in response to forging parameter disturbances was proposed.First,finite ele-ment simulations of the forging processes were conducted under varying friction factors,die temperatures,billet temperatures,and for-ging velocities,and the sample data,including process parameters and forging force under different forging strokes,were gathered.Pre-diction models for the forging force were established using the support vector regression algorithm.The prediction error of F_(f),that is,the forging force required to fill the die cavity fully,was as low as 4.1%.To further improve the prediction accuracy of the model for the ac-tual F_(f),two rounds of iterative forging experiments were conducted using the Bayesian optimization algorithm,and the prediction error of F_(f) in the forging experiments was reduced from 6.0%to 1.5%.Finally,the prediction model of F_(f) combined with a genetic algorithm was used to establish an autonomous optimization strategy for the forging velocity at each stage of the forging stroke,when the billet and die temperatures were disturbed,which realized the autonomous control in response to disturbances.In cases of−20 or−40℃ reductions in the die and billet temperatures,forging experiments conducted with the autonomous optimization strategy maintained the measured F_(f) around the target value of 180 t,with the relative error ranging from−1.3%to+3.1%.This work provides a reference for the study of di-gital modeling and autonomous optimization control of quality factors in the forging process.
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.
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 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.
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.
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.
基金Supported by the 2024 Yiwu City Research Plan Project,No.24-3-102.
文摘BACKGROUND Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with mental disorders induced by rabies vaccination and to construct a risk prediction model to inform strategies for improving patients’mental health.METHODS Patients who received rabies vaccinations at the Department of Infusion Yiwu Central Hospital between August 2024 and July 2025 were included,totaling 384 cases.Data were collected from medical records and included demographic characteristics(age,gender,occupation),lifestyle habits,and details regarding vaccine type,dosage,and injection site.The incidence of psychiatric disorders following vaccination was assessed using standardized anxiety and depression rating scales.Patients were categorized into two groups based on the presence or absence of anxiety and depression symptoms:The psychiatric disorder group and the non-psychiatric disorder group.Differences between the two groups were compared,and high-risk factors were identified using multivariate logistic regression analysis.A predictive model was then developed based on these factors to evaluate its predictive performance.RESULTS Among the 384 patients who received rabies vaccinations,36 cases(9.38%)were diagnosed with anxiety,52 cases(13.54%)with depression,and 88 cases(22.92%)with either condition.Logistic regression analysis identified the following signi ficant risk factors for psychiatric disorders:Education level of primary school or below,exposure site at the head and neck,exposure classified as grade III,family status of divorced/widowed/unmarried/living alone,number of wounds greater than one,and low awareness of rabies prevention and control(P<0.05).The risk prediction model demonstrated good performance,with an area under the receiver operating characteristic curve of 0.859,a specificity of 74.42%,and a sensitivity of 93.02%.CONCLUSION In real-world settings,psychiatric disorders following rabies vaccination are relatively common and are associated with factors such as lower education level,higher exposure severity,vulnerable family status,and limited awareness of rabies prevention and control.The developed risk prediction model may aid in early identification of high-risk individuals and support timely clinical intervention.
基金Supported by the Qihuang Scholars Program in 202114th Five-Year National Key R&D Program Project:2022YFC3500504。
文摘Objective:To construct a clinical prediction model of acupuncture treatment for diminished ovarian reserve(DOR)based on a machine learning algorithm to provide a clinical prediction of acupuncture for ameliorating pregnancy outcomes in DOR.Methods:We enrolled 377 DOR patients treated with acupuncture and with records of pregnancy outcomes(139 cases of pregnancy and 238 cases failed)exported from the International Patient Registry Platform of Acupuncture-moxibustion(IPRPAM).The predictive variables were determined using Spearman’s correlation analysis and feature engineering methods.The model was constructed by adopting logistic regression,naïve Bayes,random forest,support vector machine,extreme gradient boosting,the knearest neighbor algorithm,linear discriminant analysis,and neural network methods.The models were validated by the area under the curve(AUC),accuracy(ACC),and importance sequencing,and individual pregnancy prediction was conducted for the best-performing model.Results:The key factors determining pregnancy after acupuncture in patients with DOR were age,luteinizing hormone(LH)level after treatment,follicle-stimulating hormone(FSH)level after treatment,the ratio of FSH to LH(FSH/LH)after treatment,and history of acupuncture treatment.Random forest model ACC was 0.95,Fβwas 0.93,Logloss was 0.30,Logloss value was the lowest,the model variables exhibited the highest accuracy and precision.Conclusion:The random forest model for the effects of acupuncture on pregnancy outcomes in patients with DOR,constructed based on the IPRPAM,presents a favorable value for clinical application.
文摘BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as current tools may not fully capture the unique risks in this population.This study hypothesizes that a nomogram incorporating specific risk factors will improve hypertension risk prediction in T2DM patients.AIM To develop and validate a nomogram prediction model for hypertension in T2DM patients.METHODS A retrospective observational study was conducted using data from 26850 T2DM patients from the Anhui Provincial Primary Medical and Health Information Management System(2022 to 2024).The study included patients aged 18 and above with available data on key variables.Exclusion criteria were type 1 diabetes,gestational diabetes,insufficient data,secondary hypertension,and abnormal liver and kidney function.The Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression were used to construct the nomogram,which was validated on separate datasets.RESULTS The developed nomogram for T2DM patients incorporated age,low-density lipoprotein,body mass index,diabetes duration,and urine protein levels as key predictive factors.In the training dataset,the model demonstrated a high discriminative power with an area under the receiver operating characteristic curve(AUC)of 0.823,indicating strong predictive accuracy.The validation dataset confirmed these findings with an AUC of 0.812.The calibration curve analysis showed excellent agreement between predicted and observed outcomes,with absolute errors of 0.017 for the training set and 0.031 for the validation set.The Hosmer-Lemeshow test yielded non-significant results for both sets(χ^(2)=7.066,P=0.562 for training;χ^(2)=6.122,P=0.709 for validation),suggesting good model fit.CONCLUSION The nomogram effectively predicts hypertension risk in T2DM patients,offering a valuable tool for personalized risk assessment and guiding targeted interventions.This model provides a significant advancement in the management of T2DM and hypertension comorbidity.
基金supported by the Youth Program of National Natural Science Foundation of China(No.52404343)the General Program of National Natural Science Foundation of China(No.52274326)+2 种基金the Fundamental Research Funds for the Central Universities,China(No.N2425031)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553)the Liaoning Province Science and Technology Plan Joint Program,China(Key Research and Development Program Project)(No.2023JH2/101800058).
文摘The comprehensive status of blast furnaces was one of the most important factors affecting their economy,quality,and longev-ity.The blast furnace comprehensive status had the nature of“black box,”and it was“unpredictable.”In this study,a blast furnace com-prehensive status score and prediction method based on a cascade system and a combined model were proposed to address this issue.A dual cascade evaluation system was developed by integrating subjective and objective weighting methods.The analytic hierarchy process,coefficient of variation,entropy weight method,and impart combinatorial games were jointly employed to determine the optimal weight distribution across indicators.Categorized statuses(raw material,gas flow,furnace body,furnace cylinder,and iron-slag)were evaluated.Based on the five categories of the status data,the second cascade was applied to upgrade the quantitative evaluation of the comprehens-ive status.The weights of the different categories were 0.22,0.15,0.22,0.21,and 0.20,respectively.According to the data analysis,the results of the comprehensive status score closely matched the on-site production logs.Based on the blast furnace smelting period,the maximal information coefficient method was applied to the 100 parameters that were most relevant to the comprehensive status.A com-bined prediction model for a comprehensive status score was designed using bidirectional long short-term memory(BiLSTM)and categorical boosting(CatBoost).The test results indicated that the combined model reduced the mean absolute error by an average of 0.275 and increased the hit rate by an average of 5.65 percentage points compared to BiLSTM or CatBoost alone.When the er-ror range was±2.5,the combined model predicted a hit rate of 91.66%for the next hour’s comprehensive status score,and its high accur-acy was deemed satisfactory for the field.SHapley Additive exPlanations(SHAP)and regression fitting were applied to analyze the lin-ear quantitative relationship between the key variables and the comprehensive status score.When the furnace bottom center temperature was increased by 10℃,the comprehensive status score increased by 0.44.This method contributes to a more precise management and control of the comprehensive status of the blast furnace on-site.
