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
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
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
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: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 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
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
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
Artificial intelligence(AI)is rapidly transforming the landscape of hepatology by enabling automated data interpretation,early disease detection,and individualized treatment strategies.Chronic liver diseases,including...Artificial intelligence(AI)is rapidly transforming the landscape of hepatology by enabling automated data interpretation,early disease detection,and individualized treatment strategies.Chronic liver diseases,including non-alcoholic fatty liver disease,cirrhosis,and hepatocellular carcinoma,often progress silently and pose diagnostic challenges due to reliance on invasive biopsies and operatordependent imaging.This review explores the integration of AI across key domains such as big data analytics,deep learning-based image analysis,histopathological interpretation,biomarker discovery,and clinical prediction modeling.AI algorithms have demonstrated high accuracy in liver fibrosis staging,hepatocellular carcinoma detection,and non-alcoholic fatty liver disease risk stratification,while also enhancing survival prediction and treatment response assessment.For instance,convolutional neural networks trained on portal venous-phase computed tomography have achieved area under the curves up to 0.92 for significant fibrosis(F2-F4)and 0.89 for advanced fibrosis,with magnetic resonance imaging-based models reporting comparable performance.Advanced methodologies such as federated learning preserve patient privacy during cross-center model training,and explainable AI techniques promote transparency and clinician trust.Despite these advancements,clinical adoption remains limited by challenges including data heterogeneity,algorithmic bias,regulatory uncertainty,and lack of real-time integration into electronic health records.Looking forward,the convergence of multi-omics,imaging,and clinical data through interpretable and validated AI frameworks holds great promise for precision liver care.Continued efforts in model standardization,ethical oversight,and clinician-centered deployment will be essential to realize the full potential of AI in hepatopathy diagnosis and treatment.展开更多
Background1 Currently,there is a scarcity of risk prediction models for frailty in hospitalized patients with chronic heart failure(CHF).This study aimed to investigate the frailty status of hospitalized CHF patients,...Background1 Currently,there is a scarcity of risk prediction models for frailty in hospitalized patients with chronic heart failure(CHF).This study aimed to investigate the frailty status of hospitalized CHF patients,identify independent risk factors significantly associated with frailty,and construct an effective risk prediction model.The goal was to provide a reference for clinical strategies in preventing and managing frailty among CHF patients.Methodss Using convenience sampling,we enrolled 184 hospitalized CHF patients from a tertiary hospital between February 2022 and December 2024.General demographic data were collected via questionnaires,alongside frailty screening using the FRAIL scale and assessment of daily functioning with the Activities of Daily Living(ADL)scale.Clinical data were obtained by reviewing medical records.Participants were categorized into a frail group(n=65)and a non-frail group(n=119)based on frailty status.Clinical risk factors were compared between groups.Multivariate logistic regression was used to identify independent risk factors.A prediction model was constructed,and a receiver operating characteristic(ROC)curve was plotted to evaluate its predictive value.Results A total of 184 hospitalized CHF patients were included,with 65(35.33%)exhibiting frailty.Multivariate logistic regression analysis showed that independent risk factors for frailty included:age,ADL score,N-terminal pro-brain natriuretic peptide(NT-pro-BNP),left ventricular ejection fraction(LVEF),New York Heart Association(NYHA)class II/IV,≥3 comorbidities,comorbid diabetes mellitus(DM),comorbid valvular heart disease(VHD),smoking history,hemoglobin(Hb),albumin,high-density lipoprotein cholesterol(HDL-C),low-density lipoprotein cholesterol(LDL-C),creatinine(Cr),and blood urea nitrogen(BUN).The aforementioned factors were incorporated into logistic regression analysis and the prediction model was built.The prediction model showed quite strong predictive performance.Its area under the ROC curve was 0.904(95%CI:0.857-0.951),with a sensitivity of98.5%and a specificity of 85.7%.ConclusionssThe frailty risk prediction model for hospitalized CHF patients demonstrated robust discriminative ability and calibration.It provided substantial reference value for clinical management of CHF,offering a basis for early assessment,risk stratification,and targeted interventions to prevent frailty by identifying high-risk patients.展开更多
Objective:To systematically evaluate prediction models for postoperative deep vein thrombosis(DVT)in elderly hip fracture patients and assess their methodological quality and predictive performance.Methods:Following P...Objective:To systematically evaluate prediction models for postoperative deep vein thrombosis(DVT)in elderly hip fracture patients and assess their methodological quality and predictive performance.Methods:Following PRISMA guidelines,we searched eight databases(PubMed,Embase,Cochrane Library,Web of Science,CINAHL,CNKI,Wanfang,VIP)from inception to May 2025.Studies developing or validating DVT prediction models in elderly hip fracture patients were included.Two reviewers independently screened studies,extracted data,and assessed risk of bias and applicability using the PROBAST tool.Results:Eleven studies were included,all conducted in China between 2021 and 2025.Sample sizes ranged from 101 to 504 patients(total n=3,286).Models incorporated 3 to 9 predictors,with D-dimer,age,and time from injury to surgery being most common.All 11 studies(100%)were rated as high risk of bias,primarily due to small sample sizes,lack of validation,and inadequate missing data handling.Applicability concerns were low in 8 studies(72.7%).AUC values ranged from 0.648 to 0.967,with 10 studies(90.9%)reporting AUC>0.7.Meta-analysis identified time from injury to surgery(OR=4.63,95%CI:2.58–6.68),age(OR=1.99),D-dimer(OR=1.51),and Caprini score(OR=1.75)as significant predictors.Conclusion:Current DVT prediction models for elderly hip fracture patients demonstrate acceptable discrimination but are limited by high risk of bias and lack of external validation.Prospective,multicenter studies with rigorous validation are needed to develop clinically applicable models.展开更多
Objectives:This systematic review aimed to assess the properties and feasibility of existing risk prediction models for post-intensive care syndrome outcomes in adult survivors of critical illness.Methods:As of Novemb...Objectives:This systematic review aimed to assess the properties and feasibility of existing risk prediction models for post-intensive care syndrome outcomes in adult survivors of critical illness.Methods:As of November 1,2023,Cochrane Library,PubMed,Embase,CINAHL,Web of Science,PsycInfo,China National Knowledge Infrastructure(CNKI),SinoMed,Wanfang database,and China Science and Technology Journal Database(VIP)were searched.Following the literature screening process,we extracted data encompassing participant sources,post-intensive care syndrome(PICS)outcomes,sample sizes,missing data,predictive factors,model development methodologies,and metrics for model performance and evaluation.We conducted a review and classification of the PICS domains and predictive factors identified in each study.The Prediction Model Risk of Bias Assessment Tool was employed to assess the quality and applicability of the studies.Results:This systematic review included a total of 16 studies,comprising two cognitive impairment studies,four psychological impairment studies,eight physiological impairment studies,and two studies on all three domains.The discriminative ability of prediction models measured by area under the receiver operating characteristic curve was 0.68e0.90.The predictive performance of most models was excellent,but most models were biased and overfitted.All predictive factors tend to encompass age,pre-ICU functional impairment,in-ICU experiences,and early-onset new symptoms.Conclusions:This review identified 16 prediction models and the predictive factors for PICS.Nonetheless,due to the numerous methodological and reporting shortcomings identified in the studies under review,clinicians should exercise caution when interpreting the predictions made by these models.To avert the development of PICS,it is imperative for clinicians to closely monitor prognostic factors,including the in-ICU experience and early-onset new symptoms.展开更多
AIM:To establish a risk prediction model for secondary cataract within 2y after pars plana vitrectomy(PPV)in patients with primary rhegmatogenous retinal detachment(RRD).METHODS:Clinical data of patients with primary ...AIM:To establish a risk prediction model for secondary cataract within 2y after pars plana vitrectomy(PPV)in patients with primary rhegmatogenous retinal detachment(RRD).METHODS:Clinical data of patients with primary RRD treated at the Shenzhen Eye Hospital were retrospectively collected.Twenty-four potential influencing factors,including patient characteristics and surgical factors,were selected for analysis.Independent risk factors for secondary cataract were identified through univariate comparisons and multivariate logistic regression analysis.A risk prediction model was constructed and evaluated using receiver operating characteristic(ROC)curves,area under the ROC curve(AUC),calibration plots,and decision curve analysis(DCA)curves.RESULTS:The 386 cases(389 eyes)of patients who underwent PPV and had complete surgical records were ultimately included.Within a 2-year longitudinal observation,41.39%of patients developed cataract secondary to PPV.Logistic regression results identified a history of hypertension[odds ratio(OR)=1.78,95%CI:1.002–3.163,P=0.049],silicone oil tamponade(OR=3.667,95%CI:2.373–5.667,P=0.000),and lens thickness(OR=1.978,95%CI:1.129–3.464,P=0.017)as independent risk factors for cataract secondary to PPV.The constructed nomogram achieved AUC=0.6974.Calibration plots indicated good agreement between predicted and observed outcomes,while DCA curves demonstrated the model’s clinical utility.CONCLUSION:By incorporating a history of hypertension,vitreous substitute type,and lens thickness,this study constructs a prediction model with moderate discriminative ability.This model offers a valuable tool for clinicians to identify high-risk patients early,potentially allowing for more timely interventions and improved patient outcomes.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°.
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.
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 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.
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.
基金Supported by the Science Planning Project of Liaoning Province,No.2019JH2/10300031-05the National Natural Science Foundation of China,No.12171074.
文摘Artificial intelligence(AI)is rapidly transforming the landscape of hepatology by enabling automated data interpretation,early disease detection,and individualized treatment strategies.Chronic liver diseases,including non-alcoholic fatty liver disease,cirrhosis,and hepatocellular carcinoma,often progress silently and pose diagnostic challenges due to reliance on invasive biopsies and operatordependent imaging.This review explores the integration of AI across key domains such as big data analytics,deep learning-based image analysis,histopathological interpretation,biomarker discovery,and clinical prediction modeling.AI algorithms have demonstrated high accuracy in liver fibrosis staging,hepatocellular carcinoma detection,and non-alcoholic fatty liver disease risk stratification,while also enhancing survival prediction and treatment response assessment.For instance,convolutional neural networks trained on portal venous-phase computed tomography have achieved area under the curves up to 0.92 for significant fibrosis(F2-F4)and 0.89 for advanced fibrosis,with magnetic resonance imaging-based models reporting comparable performance.Advanced methodologies such as federated learning preserve patient privacy during cross-center model training,and explainable AI techniques promote transparency and clinician trust.Despite these advancements,clinical adoption remains limited by challenges including data heterogeneity,algorithmic bias,regulatory uncertainty,and lack of real-time integration into electronic health records.Looking forward,the convergence of multi-omics,imaging,and clinical data through interpretable and validated AI frameworks holds great promise for precision liver care.Continued efforts in model standardization,ethical oversight,and clinician-centered deployment will be essential to realize the full potential of AI in hepatopathy diagnosis and treatment.
基金supported by Guangdong Medical Science and Technology Research Fund Project(No.A2022458)Guangdong Provincial People's Medical Climbing Plan(Nursing Research Project)(No.DFJH2020011)。
文摘Background1 Currently,there is a scarcity of risk prediction models for frailty in hospitalized patients with chronic heart failure(CHF).This study aimed to investigate the frailty status of hospitalized CHF patients,identify independent risk factors significantly associated with frailty,and construct an effective risk prediction model.The goal was to provide a reference for clinical strategies in preventing and managing frailty among CHF patients.Methodss Using convenience sampling,we enrolled 184 hospitalized CHF patients from a tertiary hospital between February 2022 and December 2024.General demographic data were collected via questionnaires,alongside frailty screening using the FRAIL scale and assessment of daily functioning with the Activities of Daily Living(ADL)scale.Clinical data were obtained by reviewing medical records.Participants were categorized into a frail group(n=65)and a non-frail group(n=119)based on frailty status.Clinical risk factors were compared between groups.Multivariate logistic regression was used to identify independent risk factors.A prediction model was constructed,and a receiver operating characteristic(ROC)curve was plotted to evaluate its predictive value.Results A total of 184 hospitalized CHF patients were included,with 65(35.33%)exhibiting frailty.Multivariate logistic regression analysis showed that independent risk factors for frailty included:age,ADL score,N-terminal pro-brain natriuretic peptide(NT-pro-BNP),left ventricular ejection fraction(LVEF),New York Heart Association(NYHA)class II/IV,≥3 comorbidities,comorbid diabetes mellitus(DM),comorbid valvular heart disease(VHD),smoking history,hemoglobin(Hb),albumin,high-density lipoprotein cholesterol(HDL-C),low-density lipoprotein cholesterol(LDL-C),creatinine(Cr),and blood urea nitrogen(BUN).The aforementioned factors were incorporated into logistic regression analysis and the prediction model was built.The prediction model showed quite strong predictive performance.Its area under the ROC curve was 0.904(95%CI:0.857-0.951),with a sensitivity of98.5%and a specificity of 85.7%.ConclusionssThe frailty risk prediction model for hospitalized CHF patients demonstrated robust discriminative ability and calibration.It provided substantial reference value for clinical management of CHF,offering a basis for early assessment,risk stratification,and targeted interventions to prevent frailty by identifying high-risk patients.
文摘Objective:To systematically evaluate prediction models for postoperative deep vein thrombosis(DVT)in elderly hip fracture patients and assess their methodological quality and predictive performance.Methods:Following PRISMA guidelines,we searched eight databases(PubMed,Embase,Cochrane Library,Web of Science,CINAHL,CNKI,Wanfang,VIP)from inception to May 2025.Studies developing or validating DVT prediction models in elderly hip fracture patients were included.Two reviewers independently screened studies,extracted data,and assessed risk of bias and applicability using the PROBAST tool.Results:Eleven studies were included,all conducted in China between 2021 and 2025.Sample sizes ranged from 101 to 504 patients(total n=3,286).Models incorporated 3 to 9 predictors,with D-dimer,age,and time from injury to surgery being most common.All 11 studies(100%)were rated as high risk of bias,primarily due to small sample sizes,lack of validation,and inadequate missing data handling.Applicability concerns were low in 8 studies(72.7%).AUC values ranged from 0.648 to 0.967,with 10 studies(90.9%)reporting AUC>0.7.Meta-analysis identified time from injury to surgery(OR=4.63,95%CI:2.58–6.68),age(OR=1.99),D-dimer(OR=1.51),and Caprini score(OR=1.75)as significant predictors.Conclusion:Current DVT prediction models for elderly hip fracture patients demonstrate acceptable discrimination but are limited by high risk of bias and lack of external validation.Prospective,multicenter studies with rigorous validation are needed to develop clinically applicable models.
基金supported by the Scientific Research Project of Shanghai Municipal Health Commission(202140047)the Characteristic Research Project of Shanghai General Hospital(CCTR-2022N03)the Technology Standardization Management and Promotion Project of Shanghai Shenkang Hospital Development Center(SHDC22022219)and the funding organization has played no roles in the survey's design,implementation,and analysis.
文摘Objectives:This systematic review aimed to assess the properties and feasibility of existing risk prediction models for post-intensive care syndrome outcomes in adult survivors of critical illness.Methods:As of November 1,2023,Cochrane Library,PubMed,Embase,CINAHL,Web of Science,PsycInfo,China National Knowledge Infrastructure(CNKI),SinoMed,Wanfang database,and China Science and Technology Journal Database(VIP)were searched.Following the literature screening process,we extracted data encompassing participant sources,post-intensive care syndrome(PICS)outcomes,sample sizes,missing data,predictive factors,model development methodologies,and metrics for model performance and evaluation.We conducted a review and classification of the PICS domains and predictive factors identified in each study.The Prediction Model Risk of Bias Assessment Tool was employed to assess the quality and applicability of the studies.Results:This systematic review included a total of 16 studies,comprising two cognitive impairment studies,four psychological impairment studies,eight physiological impairment studies,and two studies on all three domains.The discriminative ability of prediction models measured by area under the receiver operating characteristic curve was 0.68e0.90.The predictive performance of most models was excellent,but most models were biased and overfitted.All predictive factors tend to encompass age,pre-ICU functional impairment,in-ICU experiences,and early-onset new symptoms.Conclusions:This review identified 16 prediction models and the predictive factors for PICS.Nonetheless,due to the numerous methodological and reporting shortcomings identified in the studies under review,clinicians should exercise caution when interpreting the predictions made by these models.To avert the development of PICS,it is imperative for clinicians to closely monitor prognostic factors,including the in-ICU experience and early-onset new symptoms.
基金Supported by the Shenzhen Science and Technology Program(No.JCYJ20220818103207015)the SanMing Project of Medicine in Shenzhen(No.SZSM202311012).
文摘AIM:To establish a risk prediction model for secondary cataract within 2y after pars plana vitrectomy(PPV)in patients with primary rhegmatogenous retinal detachment(RRD).METHODS:Clinical data of patients with primary RRD treated at the Shenzhen Eye Hospital were retrospectively collected.Twenty-four potential influencing factors,including patient characteristics and surgical factors,were selected for analysis.Independent risk factors for secondary cataract were identified through univariate comparisons and multivariate logistic regression analysis.A risk prediction model was constructed and evaluated using receiver operating characteristic(ROC)curves,area under the ROC curve(AUC),calibration plots,and decision curve analysis(DCA)curves.RESULTS:The 386 cases(389 eyes)of patients who underwent PPV and had complete surgical records were ultimately included.Within a 2-year longitudinal observation,41.39%of patients developed cataract secondary to PPV.Logistic regression results identified a history of hypertension[odds ratio(OR)=1.78,95%CI:1.002–3.163,P=0.049],silicone oil tamponade(OR=3.667,95%CI:2.373–5.667,P=0.000),and lens thickness(OR=1.978,95%CI:1.129–3.464,P=0.017)as independent risk factors for cataract secondary to PPV.The constructed nomogram achieved AUC=0.6974.Calibration plots indicated good agreement between predicted and observed outcomes,while DCA curves demonstrated the model’s clinical utility.CONCLUSION:By incorporating a history of hypertension,vitreous substitute type,and lens thickness,this study constructs a prediction model with moderate discriminative ability.This model offers a valuable tool for clinicians to identify high-risk patients early,potentially allowing for more timely interventions and improved patient outcomes.
