A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorith...A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.展开更多
Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutua...Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.展开更多
Improving the efficiency of athletic performance and reducing the likelihood of overtraining are primarily determined goals that can be achieved by the correct organization of the training process.The nature of adapta...Improving the efficiency of athletic performance and reducing the likelihood of overtraining are primarily determined goals that can be achieved by the correct organization of the training process.The nature of adaptation to physical stress is associated with the specificity,focus,and degree of biochemical and functional changes that occur during muscular work.In this study,we aimed to develop a diagnostic model for predicting metabolic processes in athletes based on standard biochemical blood analysis indicators.The study involved athletes from the track and field athletics team(men,n=42,average age was[22.55±3.68]years).Blood samples were collected in the morning at the beginning and end of the training week during the annual cycle.During the entire period,3625 laboratory parameter tests were conducted.Capillary blood sampling in athletes was conducted from the distal phalanx of the finger after overnight fasting,according to standard diagnostic procedures.To determine the predominance of anabolic or catabolic processes,equations were derived from a linear discriminant function.The discriminant function of predicting metabolic processes in athletes has a high information capacity(92.1%),as confirmed by the biochemical results of neuroendocrine system activity,which characterized the body's stage of adaptive regulatory mechanisms in response to stress factors.The classification matrix used to predict the metabolic processes based on the results of the discriminant function calculation demonstrates the statistical significance of the model(p<0.01).Consequently,an informative mathematical model was developed,which enabled the reliable and timely prediction of the prevalence of one of the metabolic activity phases in the athlete's body.The use of the developed model will also allow us to assess the nature of adaptation to specific muscular work,identify an athlete's weaknesses,forecast the success of their performance,and timely adjust both the training process and the recovery program.展开更多
Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Pat...Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Patients diagnosed with essential hypertension and admitted to Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shang-hai Hospital of Traditional Chinese Medicine,and Shanghai Hospital of Integrated Tradition-al Chinese and Western Medicine from July 6th 2020 to June 16th 2021,and from August 11th 2023 to January 22nd 2024,were enrolled in this retrospective research.The baselines and clinical biochemical indicators of patients were collected.The SMART-I TCM pulse instru-ment was applied to gather pulse diagram parameters.Multivariate logistic regression was adopted to analyze the risk factors for HTH.RStudio was employed to construct the nomo-gram model,receiver operating characteristic(ROC)curve,and calibration curve(bootstrap self-sampling 200 times),and clinical decision curve were drawn to evaluate the model’s dis-crimination and clinical effectiveness.Results A total of 168 hospitalized patients with essential hypertension were selected and di-vided into non-HTH group(n=29)and HTH group(n=139).Compared with non-HTH group,HTH group had a lower body mass index(BMI),and higher proportions of male pa-tients and drinkers(P<0.05).The ventricular wall thickening(VWT)could not be deter-mined.The proportions of left common carotid intima-media wall thickness(LCCIMWT)and serum creatinine(SCR)were higher in HTH group(P<0.05).The pulse diagram parameter As was significantly higher,and H4/H1 and T1/T were lower in HTH group(P<0.05).Gender,al-cohol consumption,serum creatinine,and the pulse diagram parameter H4/H1 were identi-fied as independent risk factors for HTH(P<0.05).The nomogram’s area under the ROC curve(AUC)was 0.795[95%confidence interval(CI):(0.7066,0.8828)],with a specificity of 0.724 and sensitivity of 0.799.After 200 times repeated bootstrap self-samplings,the calibra-tion curve showed that the simulated curve fits well with the actual curve(x^(2)=9.5002,P=0.3019).The clinical decision curve indicated that the nomogram’s applicability was optimal when the threshold for predicting HTH was between 0.38 and 1.00.Conclusion The nomogram model could be valuable for predicting the onset risk of HTH and pulse diagram parameters can facilitate early screening and prevention of HTH.展开更多
Background and aim Recently,long-term outcomes in patients with spontaneous intracerebral haemorrhage(sICH)have gained increasing attention besides acute-phase characteristics.Predictive models for long-term outcomes ...Background and aim Recently,long-term outcomes in patients with spontaneous intracerebral haemorrhage(sICH)have gained increasing attention besides acute-phase characteristics.Predictive models for long-term outcomes are valuable for risk stratification and treatment strategies.This study aimed to develop and validate an explainable model for predicting long-term recurrence and all-cause death in patients with ICH,using clinical and imaging markers of cerebral small vascular diseases from MRI.Method We retrospectively analysed data from a prospectively collected large-scale cohort of patients with acute ICH admitted to the Neurology Department of The Second Affiliated Hospital of Zhejiang University between November 2016 and April 2023.After comprehensive variable selection using least absolute shrinkage and selection operator and stepwise Cox regression,we constructed Cox proportional hazards models to predict recurrence and all-cause death.Model performance was evaluated using the concordance index,integrated Brier score and time-dependent area under the curve.Global and local interpretability were assessed using variable importance calculated as SurvSHAP(t)and SurvLIME methods for the entire training set and individual patients,respectively.Results A total of 842 eligible patients were included.Over a median follow-up of 36 months(IQR:12-51),86 patients(9.1%)died,and 62 patients(6.6%)experienced recurrence of ICH.The concordance indexes for the all-cause death and recurrence models were 0.841(95%CI 0.767 to 0.913)and 0.759(95%CI 0.651 to 0.867),respectively,with integrated Brier scores of 0.079 and 0.063.The interpretability maps highlighted age,aetiology of ICH and low haemoglobin as key predictors of long-term death,while cortical superficial siderosis and previous haemorrhage were crucial for predicting recurrence.Conclusions This model demonstrates high predictive accuracy and emphasises the crucial factors in predicting long-term outcomes of patients with sICH.展开更多
BACKGROUND The hepatic venous pressure gradient serves as a crucial parameter for assessing portal hypertension and predicting clinical decompensation in individuals with cirrhosis.However,owing to its invasive nature...BACKGROUND The hepatic venous pressure gradient serves as a crucial parameter for assessing portal hypertension and predicting clinical decompensation in individuals with cirrhosis.However,owing to its invasive nature,there has been growing interest in identifying noninvasive alternatives.Transient elastography offers a promising approach for measuring liver stiffness and spleen stiffness,which can help estimate the likelihood of decompensation in patients with chronic liver disease.AIM To investigate the predictive ability of the liver stiffness measurement(LSM)and spleen stiffness measurement(SSM)in conjunction with other noninvasive indicators for clinical decompensation in patients suffering from compensatory cirrhosis and portal hypertension.METHODS This study was a retrospective analysis of the clinical data of 200 patients who were diagnosed with viral cirrhosis and who received computed tomography,transient elastography,ultrasound,and endoscopic examinations at The Second Affiliated Hospital of Xi’an Jiaotong University between March 2020 and November 2022.Patient classification was performed in accordance with the Baveno VI consensus.The area under the curve was used to evaluate and compare the predictive accuracy across different patient groups.The diagnostic effectiveness of several models,including the liver stiffness-spleen diameter-platelet ratio,variceal risk index,aspartate aminotransferase-alanine aminotransferase ratio,Baveno Ⅵ criteria,and newly developed models,was assessed.Additionally,decision curve analysis was carried out across a range of threshold probabilities to evaluate the clinical utility of these predictive factors.RESULTS Univariate and multivariate analyses demonstrated that SSM,LSM,and the spleen length diameter(SLD)were linked to clinical decompensation in individuals with viral cirrhosis.On the basis of these findings,a predictive model was developed via logistic regression:Ln[P/(1-P)]=-4.969-0.279×SSM+0.348×LSM+0.272×SLD.The model exhibited strong performance,with an area under the curve of 0.944.At a cutoff value of 0.56,the sensitivity,specificity,positive predictive value,and negative predictive value for predicting clinical decompensation were 85.29%,88.89%,87.89%,and 86.47%,respectively.The newly developed model demonstrated enhanced accuracy in forecasting clinical decompensation among patients suffering from viral cirrhosis when compared to four previously established models.CONCLUSION Noninvasive models utilizing SSM,LSM,and SLD are effective in predicting clinical decompensation among patients with viral cirrhosis,thereby reducing the need for unnecessary hepatic venous pressure gradient testing.展开更多
Gross primary production(GPP)is closely associated with processes such as photosynthesis and transpiration within ecosystems,which is a vital component of the global carbon-water-energy cycle.Accurate prediction of GP...Gross primary production(GPP)is closely associated with processes such as photosynthesis and transpiration within ecosystems,which is a vital component of the global carbon-water-energy cycle.Accurate prediction of GPP in terrestrial ecosystems is essential for evaluating terrestrial carbon cycle processes.Machine learning(ML)models provide significant technical support in this domain.Presently,there is a deficiency of high-precision and robust GPP prediction variables and models.Challenges such as unclear contributions of predictive variables,extended model training durations,and limited robustness must be addressed.Solar-induced chlorophyll fluorescence(SIF),optimized multilayer perceptron neural networks,and ensemble learning models show the potential to overcome these challenges.This study aimed to develop an optimized multilayer perceptron neural network model and an ensemble learning model,while objectively assessing the capacity of SIF to predict GPP.Identifying robust models capable of enhancing the accuracy of GPP predictions was the ultimate goal.This study utilized continuous observations of SIF and meteorological data collected from 2020 to 2021 at a designated research observation station within the Populus plantation ecosystem of the Huanghuaihai agricultural protective forest system in Henan Province,China.By optimizing and evaluating the predictive accuracy and robustness of the models across different temporal scales(half-hourly and daily scales),a multi-layer perceptron(MLP)neural network optimization model based on the back propagation(BP)neural network(BPNN)algorithm(BP/MLP)and MLP and random forest(RF)integration(MLP-RF)ensemble models were constructed,utilizing SIF as the primary predictive variable for GPP.Both the BP/MLP(half-hourly scale model R^(2)=0.885,daily scale model R^(2)=0.921)and the MLP-RF(half-hourly scale model R^(2)=0.845,daily scale model R^(2)=0.914)models showed superior accuracy compared to the BPNN(half-hourly scale model R^(2)=0.841,daily scale model R^(2)=0.918)and the traditional RF(half-hourly scale model R^(2)=0.798,daily scale model R^(2)=0.867)models,with the BP/MLP model consistently outperforming the MLP-RF model.The BP/MLP model,which was optimized through particle swarm optimization(PSO),significantly enhanced the robustness of GPP predictions on a half-hourly scale and daily scale.Considering both half-hourly scale and daily scale in the PSO-BP/MLP modeling,the four indicators,light-use efficiency(LUE),photosynthetically active radiation(PAR),absorbed photosynthetically active radiation(APAR),and the variation in SIF with NIRvP(fSIF(NIRvP)),exhibited the potential for enhancing the accuracy of GPP predictions.This study employed a series of model optimization techniques to develop a GPP prediction model with enhanced performance that objectively evaluated the contributions of the predictive variables.This approach provided an innovative and effective method for assessing the carbon cycle in terrestrial ecosystems.展开更多
BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suita...BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.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
Purpose:ATLAS is a cross-sectional study aiming to investigate environmental and genetic determinants of athletic performance in healthy Greek competitive athletes(CA).This article presents the study design,investigat...Purpose:ATLAS is a cross-sectional study aiming to investigate environmental and genetic determinants of athletic performance in healthy Greek competitive athletes(CA).This article presents the study design,investigates the muscle strength performance(MSP)of 289 adult and teenage CA,exercisers,and physically inactive individuals(PI),and proposes predictive models of MSP for adults.Methods:Muscle maximal,speed,and explosive strength(MMS/MSS/MES)at unilateral maximal concentric flexion and extension contraction(FC/EC)were evaluated using Biodex System 3 PRO^(TM)at 60°/s,180°/s,and 300°/s,while additional performance markers were assessed through field ergometric testing.Participants were interviewed about their lifestyle,dietary habits,physical activity,injury,and medical history.Body composition was assessed via bioelectrical impedance.gDNA was extracted from biochemical samples and then genotyped.Statistical analysis was conducted using IBM SPSS Statistics v21.0 and R.Results:Age,fitness,and sex impacted correlations of MSP with body composition and anthropometric measurements(p<0.05).Among CA,females outperformed males in accuracy(p<0.001)while,males outperformed females in anaerobic power,MSP,speed,and endurance(p<0.001).Adult CA outperformed exercisers and PI in MMS,MSS,and MES(p<0.05).Multiple linear regression models,with predictors age,FFM,body extremity,training load explained the majority of variation in MMS(R^(2)_(adj):71.4%–88.9%),MSS(R^(2)_(adj):64.8%–78.4%),and MES(R^(2)_(adj):52.7%–68.4%)at EC,FC,and their mean(p<0.001).Conclusions:Muscle-strengthening strategies should be customized according to individual fitness levels,body composition,and anthropometric measurements.The innovative sex-specific regression models assessing MMS,MSS,and MES at EC and FC provide a framework for personalizing rehabilitation and skill-specific training strategies.展开更多
BACKGROUND Laparoscopic distal pancreatectomy(LDP)has emerged as the preferred approach for both benign and malignant lesions located in the pancreatic body and tail.Nevertheless,a notable deficiency persists in the a...BACKGROUND Laparoscopic distal pancreatectomy(LDP)has emerged as the preferred approach for both benign and malignant lesions located in the pancreatic body and tail.Nevertheless,a notable deficiency persists in the absence of a standardized,procedure-specific metric for evaluating and comparing surgical quality.A composite measure termed“textbook outcome(TO)”,which encompasses key short-term endpoints,has been validated in laparoscopic pancreatoduodenectomy but has not yet been established in dedicated LDP cohorts.The definition and prediction of TO in this context could aid in facilitating cross-institutional benchmarking and fostering advancements in quality improvement.AIM To establish procedure-specific criteria for TO and identify independent predictors of TO failure in patients undergoing LDP.METHODS Consecutive patients who underwent LDP at a single high-volume pancreatic center between January 2015 and August 2022 were retrospectively analyzed.TO was defined as the absence of clinically relevant postoperative pancreatic fistula(grade B/C),post-pancreatectomy hemorrhage(grade B/C),severe complications(Clavien-Dindo≥III),readmission within 30 days,and in-hospital or 30-day mortality.Multivariable logistic regression was employed to identify independent predictors of TO failure,and a nomogram was constructed and internally validated.RESULTS Among 405 eligible patients,286(70.6%)attained TO.Multivariable analysis revealed that female sex[odds ratio(OR)=0.62,95%confidence interval(CI):0.39-0.99]conferred a protective effect,while preoperative endoscopic ultrasound-guided fine-needle aspiration(OR=2.66,95%CI:1.05-6.73),pancreatic portal hypertension(OR=2.81,95%CI:1.06-7.45),and cystic-solid(OR=2.51,95%CI:1.34-4.69)or solid lesions(OR=1.91,95%CI:1.06-3.44)were independently associated with TO failure(all P<0.05).The derived nomogram exhibited modest discrimination and calibration when assessed in both the training and validation datasets.CONCLUSION The proposed LDP-specific definition of TO is feasible and discriminative,and the developed nomogram provides an objective tool for individualized risk assessment.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
To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the stre...To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the strength and elastic modulus of mortar and concrete prepared with mechanism aggregates of the corresponding lithology,and the stress-strain curves of concrete were investigated.In this paper,a coarse aggregate and mortar matrix bonding assumption is proposed,and a prediction model for the elastic modulus of mortar is established by considering the lithology of the mechanism sand and the slurry components.An equivalent coarse aggregate elastic modulus model was established by considering factors such as coarse aggregate particle size,volume fraction,and mortar thickness between coarse aggregates.Based on the elastic modulus of the equivalent coarse aggregate and the remaining mortar,a prediction model for the elastic modulus of the two and three components of concrete in series and then in parallel was established,and the predicted values differed from the measured values within 10%.It is proposed that the coarse aggregate elastic modulus in highstrength concrete is the most critical factor affecting the elastic modulus of concrete,and as the coarse aggregate elastic modulus increases by 27.7%,the concrete elastic modulus increases by 19.5%.展开更多
Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential ...Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential predictive tools,hold promise for advancing early diagnosis of mental disorders.This study aims to evaluate the predictive potential of proteomic features and PRS in multiple mental illnesses(depression,schizophrenia,and post-traumatic stress disorder(PTSD)).Using participant data from the UK Biobank-Pharma Proteomics Project,we screen protein associations with mental disorders through least absolute shrinkage and selection operator(LASSO)analysis and construct a Cox regression risk prediction model by integrating the PRS.Additionally,we evaluate predictive performance using 6 machine learning methods and Kaplan-Meier survival curves.Our findings reveal distinct predictive patterns across dis-orders.For depression,integrating plasma proteins with PRS significantly improves prediction beyond the clinical model(C-index=0.6322).For schizophrenia,adding plasma proteins enhances predictive performance,whereas PRS provides no significant improvement.For PTSD,neither plasma proteins nor PRS add substantial predictive value beyond clinical variables.Risk stratification analysis demonstrat that all three mental disorders models can clearly distinguish high-risk from low-risk groups(depression:HR=2.34,P<0.001;schizophrenia:HR=5.47,P<0.001;PTSD:HR=3.02,P<0.001).Al-though it shows good performance in short-term prediction,its long-term prediction ability has decreased,and it needs to be further optimized in the future.This study underscores the differential utility of biomarkers across mental disorders and provides a rationale for disorder-specific predictive modeling in precision psychiatry.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
BACKGROUND Integrating conventional ultrasound features with 2D shear wave elastography(2D-SWE)can potentially enhance preoperative hepatocellular carcinoma(HCC)predictions.AIM To develop a 2D-SWE-based predictive mod...BACKGROUND Integrating conventional ultrasound features with 2D shear wave elastography(2D-SWE)can potentially enhance preoperative hepatocellular carcinoma(HCC)predictions.AIM To develop a 2D-SWE-based predictive model for preoperative identification of HCC.METHODS A retrospective analysis of 884 patients who underwent liver resection and pathology evaluation from February 2021 to August 2023 was conducted at the Oriental Hepatobiliary Surgery Hospital.The patients were divided into the modeling group(n=720)and the control group(n=164).The study included conventional ultrasound,2D-SWE,and preoperative laboratory tests.Multiple logistic regression was used to identify independent predictive factors for RESULTS In the modeling group analysis,maximal elasticity(Emax)of tumors and their peripheries,platelet count,cirrhosis,and blood flow were independent risk indicators for malignancies.These factors yielded an area under the curve of 0.77(95%confidence interval:0.73-0.81)with 84%sensitivity and 61%specificity.The model demonstrated good calibration in both the construction and validation cohorts,as shown by the calibration graph and Hosmer-Lemeshow test(P=0.683 and P=0.658,respectively).Additionally,the mean elasticity(Emean)of the tumor periphery was identified as a risk factor for microvascular invasion(MVI)in malignant liver tumors(P=0.003).Patients receiving antiviral treatment differed significantly in platelet count(P=0.002),Emax of tumors(P=0.033),Emean of tumors(P=0.042),Emax at tumor periphery(P<0.001),and Emean at tumor periphery(P=0.003).CONCLUSION 2D-SWE’s hardness value serves as a valuable marker for enhancing the preoperative diagnosis of malignant liver lesions,correlating significantly with MVI and antiviral treatment efficacy.展开更多
Soils were collected from 2-year (2-y) and 3-year (3-y) old red-pine seedling plots in two tree nurseries, Hayward in the north and Wilson in the southwestern part of Wisconsin State respectively, and equilibrated wit...Soils were collected from 2-year (2-y) and 3-year (3-y) old red-pine seedling plots in two tree nurseries, Hayward in the north and Wilson in the southwestern part of Wisconsin State respectively, and equilibrated with 0.01 M Ca(NO3)2 for soil solution Zn and Mn (solu-Zn and Mn), and with 0.01 M Ca(NO3)2+0.005 M EDTA for soil adsorbed Zn and Mn (ad-Zn and Mn). Buffering capacity of soil Zn and Mn (b-Zn and Mn) was obtained from the ratio of ad-Zn and Mn to the solu-Zn and Mn. The concerned traces in pine seedling needles (ndls), stems(sts) and roots (rts) were simultaneously measured. The results obtained show that:About 60% of solu- and ad- Zn ranged from 0.2 to 0.4 and from 1 to 2μ/g soil respectively. About 70% of b-Zn was within) 3-10.The highest content of solu-Zn compared with the lowest showed a discrepancy of more than 10-fold. The two forms of soil Zn were commonly higher in Wilson than in Hayward Nursery.About 80% of solu-, ad- and b-Mn were within 3-10, 5-5.8 μg/ g soil and 1-2 respectively. Influence of low buffering capacity on solu-Zn and Mn was about 20 times stronger than that of the high.The E-value, a ratio of accumulated Zn and Mn in needles to those in the soil solution, is proved to be: E-Zn > E-Mn;E-sts> E-ndls or E-rts; and E-2y > E-3y.Curvilinear and/ or linear correlations between soil solu-, ad- and b-Zn and Mn and ndls-, sts-, rts-Zn and Mn were at very significant or significant levels.For predicting ndls-Zn and Mn, two realizable and simple models from two regression equations were established through the selection of related parameters and dependent variables. Binary regression analysis basically eliminated the influence of soil pH on the prediction of Zn and Mn in needles. Soil pH was thus thought to be excluded from the model.展开更多
BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for...BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for clinical stage II/III rectal cancer.However,few patients achieve a complete pathological response,and most patients require surgical resection and adjuvant therapy.Therefore,identifying risk factors and developing accurate models to predict the prognosis of LARC patients are of great clinical significance.AIM To establish effective prognostic nomograms and risk score prediction models to predict overall survival(OS)and disease-free survival(DFS)for LARC treated with NT.METHODS Nomograms and risk factor score prediction models were based on patients who received NT at the Cancer Hospital from 2015 to 2017.The least absolute shrinkage and selection operator regression model were utilized to screen for prognostic risk factors,which were validated by the Cox regression method.Assessment of the performance of the two prediction models was conducted using receiver operating characteristic curves,and that of the two nomograms was conducted by calculating the concordance index(C-index)and calibration curves.The results were validated in a cohort of 65 patients from 2015 to 2017.RESULTS Seven features were significantly associated with OS and were included in the OS prediction nomogram and prediction model:Vascular_tumors_bolt,cancer nodules,yN,body mass index,matchmouth distance from the edge,nerve aggression and postoperative carcinoembryonic antigen.The nomogram showed good predictive value for OS,with a C-index of 0.91(95%CI:0.85,0.97)and good calibration.In the validation cohort,the C-index was 0.69(95%CI:0.53,0.84).The risk factor prediction model showed good predictive value.The areas under the curve for 3-and 5-year survival were 0.811 and 0.782.The nomogram for predicting DFS included ypTNM and nerve aggression and showed good calibration and a C-index of 0.77(95%CI:0.69,0.85).In the validation cohort,the C-index was 0.71(95%CI:0.61,0.81).The prediction model for DFS also had good predictive value,with an AUC for 3-year survival of 0.784 and an AUC for 5-year survival of 0.754.CONCLUSION We established accurate nomograms and prediction models for predicting OS and DFS in patients with LARC after undergoing NT.展开更多
文摘A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.
文摘Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.
基金financed by the Ministry of Science and Higher Education of the Russian Federation within the framework of state support for the creation and development of World-Class Research Centers‘Digital Biodesign and Personalized Healthcare’No 75-15-2022-305.
文摘Improving the efficiency of athletic performance and reducing the likelihood of overtraining are primarily determined goals that can be achieved by the correct organization of the training process.The nature of adaptation to physical stress is associated with the specificity,focus,and degree of biochemical and functional changes that occur during muscular work.In this study,we aimed to develop a diagnostic model for predicting metabolic processes in athletes based on standard biochemical blood analysis indicators.The study involved athletes from the track and field athletics team(men,n=42,average age was[22.55±3.68]years).Blood samples were collected in the morning at the beginning and end of the training week during the annual cycle.During the entire period,3625 laboratory parameter tests were conducted.Capillary blood sampling in athletes was conducted from the distal phalanx of the finger after overnight fasting,according to standard diagnostic procedures.To determine the predominance of anabolic or catabolic processes,equations were derived from a linear discriminant function.The discriminant function of predicting metabolic processes in athletes has a high information capacity(92.1%),as confirmed by the biochemical results of neuroendocrine system activity,which characterized the body's stage of adaptive regulatory mechanisms in response to stress factors.The classification matrix used to predict the metabolic processes based on the results of the discriminant function calculation demonstrates the statistical significance of the model(p<0.01).Consequently,an informative mathematical model was developed,which enabled the reliable and timely prediction of the prevalence of one of the metabolic activity phases in the athlete's body.The use of the developed model will also allow us to assess the nature of adaptation to specific muscular work,identify an athlete's weaknesses,forecast the success of their performance,and timely adjust both the training process and the recovery program.
基金National Natural Science Foundation of China (81973749 and 8143594)State Administration of Traditional Chinese Medicine High-level Chinese Medicine Key Discipline Construction Project (zyyzdxk-2023069)。
文摘Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Patients diagnosed with essential hypertension and admitted to Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shang-hai Hospital of Traditional Chinese Medicine,and Shanghai Hospital of Integrated Tradition-al Chinese and Western Medicine from July 6th 2020 to June 16th 2021,and from August 11th 2023 to January 22nd 2024,were enrolled in this retrospective research.The baselines and clinical biochemical indicators of patients were collected.The SMART-I TCM pulse instru-ment was applied to gather pulse diagram parameters.Multivariate logistic regression was adopted to analyze the risk factors for HTH.RStudio was employed to construct the nomo-gram model,receiver operating characteristic(ROC)curve,and calibration curve(bootstrap self-sampling 200 times),and clinical decision curve were drawn to evaluate the model’s dis-crimination and clinical effectiveness.Results A total of 168 hospitalized patients with essential hypertension were selected and di-vided into non-HTH group(n=29)and HTH group(n=139).Compared with non-HTH group,HTH group had a lower body mass index(BMI),and higher proportions of male pa-tients and drinkers(P<0.05).The ventricular wall thickening(VWT)could not be deter-mined.The proportions of left common carotid intima-media wall thickness(LCCIMWT)and serum creatinine(SCR)were higher in HTH group(P<0.05).The pulse diagram parameter As was significantly higher,and H4/H1 and T1/T were lower in HTH group(P<0.05).Gender,al-cohol consumption,serum creatinine,and the pulse diagram parameter H4/H1 were identi-fied as independent risk factors for HTH(P<0.05).The nomogram’s area under the ROC curve(AUC)was 0.795[95%confidence interval(CI):(0.7066,0.8828)],with a specificity of 0.724 and sensitivity of 0.799.After 200 times repeated bootstrap self-samplings,the calibra-tion curve showed that the simulated curve fits well with the actual curve(x^(2)=9.5002,P=0.3019).The clinical decision curve indicated that the nomogram’s applicability was optimal when the threshold for predicting HTH was between 0.38 and 1.00.Conclusion The nomogram model could be valuable for predicting the onset risk of HTH and pulse diagram parameters can facilitate early screening and prevention of HTH.
基金supported by the Zhejiang Provincial Medical and Health Science and Technology Project(2022KY174)the‘Pioneer’R&D Program of Zhejiang(2024C03006).
文摘Background and aim Recently,long-term outcomes in patients with spontaneous intracerebral haemorrhage(sICH)have gained increasing attention besides acute-phase characteristics.Predictive models for long-term outcomes are valuable for risk stratification and treatment strategies.This study aimed to develop and validate an explainable model for predicting long-term recurrence and all-cause death in patients with ICH,using clinical and imaging markers of cerebral small vascular diseases from MRI.Method We retrospectively analysed data from a prospectively collected large-scale cohort of patients with acute ICH admitted to the Neurology Department of The Second Affiliated Hospital of Zhejiang University between November 2016 and April 2023.After comprehensive variable selection using least absolute shrinkage and selection operator and stepwise Cox regression,we constructed Cox proportional hazards models to predict recurrence and all-cause death.Model performance was evaluated using the concordance index,integrated Brier score and time-dependent area under the curve.Global and local interpretability were assessed using variable importance calculated as SurvSHAP(t)and SurvLIME methods for the entire training set and individual patients,respectively.Results A total of 842 eligible patients were included.Over a median follow-up of 36 months(IQR:12-51),86 patients(9.1%)died,and 62 patients(6.6%)experienced recurrence of ICH.The concordance indexes for the all-cause death and recurrence models were 0.841(95%CI 0.767 to 0.913)and 0.759(95%CI 0.651 to 0.867),respectively,with integrated Brier scores of 0.079 and 0.063.The interpretability maps highlighted age,aetiology of ICH and low haemoglobin as key predictors of long-term death,while cortical superficial siderosis and previous haemorrhage were crucial for predicting recurrence.Conclusions This model demonstrates high predictive accuracy and emphasises the crucial factors in predicting long-term outcomes of patients with sICH.
基金Supported by Xi’an Science and Technology Plan,No.23YXYJ0172.
文摘BACKGROUND The hepatic venous pressure gradient serves as a crucial parameter for assessing portal hypertension and predicting clinical decompensation in individuals with cirrhosis.However,owing to its invasive nature,there has been growing interest in identifying noninvasive alternatives.Transient elastography offers a promising approach for measuring liver stiffness and spleen stiffness,which can help estimate the likelihood of decompensation in patients with chronic liver disease.AIM To investigate the predictive ability of the liver stiffness measurement(LSM)and spleen stiffness measurement(SSM)in conjunction with other noninvasive indicators for clinical decompensation in patients suffering from compensatory cirrhosis and portal hypertension.METHODS This study was a retrospective analysis of the clinical data of 200 patients who were diagnosed with viral cirrhosis and who received computed tomography,transient elastography,ultrasound,and endoscopic examinations at The Second Affiliated Hospital of Xi’an Jiaotong University between March 2020 and November 2022.Patient classification was performed in accordance with the Baveno VI consensus.The area under the curve was used to evaluate and compare the predictive accuracy across different patient groups.The diagnostic effectiveness of several models,including the liver stiffness-spleen diameter-platelet ratio,variceal risk index,aspartate aminotransferase-alanine aminotransferase ratio,Baveno Ⅵ criteria,and newly developed models,was assessed.Additionally,decision curve analysis was carried out across a range of threshold probabilities to evaluate the clinical utility of these predictive factors.RESULTS Univariate and multivariate analyses demonstrated that SSM,LSM,and the spleen length diameter(SLD)were linked to clinical decompensation in individuals with viral cirrhosis.On the basis of these findings,a predictive model was developed via logistic regression:Ln[P/(1-P)]=-4.969-0.279×SSM+0.348×LSM+0.272×SLD.The model exhibited strong performance,with an area under the curve of 0.944.At a cutoff value of 0.56,the sensitivity,specificity,positive predictive value,and negative predictive value for predicting clinical decompensation were 85.29%,88.89%,87.89%,and 86.47%,respectively.The newly developed model demonstrated enhanced accuracy in forecasting clinical decompensation among patients suffering from viral cirrhosis when compared to four previously established models.CONCLUSION Noninvasive models utilizing SSM,LSM,and SLD are effective in predicting clinical decompensation among patients with viral cirrhosis,thereby reducing the need for unnecessary hepatic venous pressure gradient testing.
基金supported by the National Key R&D Program of China(No.2023YFD2200400-01)the Fundamental Scientific Research Operation of Central-level Public Welfare Scientific Research Institutes(No.CAFYBB2023MA001).
文摘Gross primary production(GPP)is closely associated with processes such as photosynthesis and transpiration within ecosystems,which is a vital component of the global carbon-water-energy cycle.Accurate prediction of GPP in terrestrial ecosystems is essential for evaluating terrestrial carbon cycle processes.Machine learning(ML)models provide significant technical support in this domain.Presently,there is a deficiency of high-precision and robust GPP prediction variables and models.Challenges such as unclear contributions of predictive variables,extended model training durations,and limited robustness must be addressed.Solar-induced chlorophyll fluorescence(SIF),optimized multilayer perceptron neural networks,and ensemble learning models show the potential to overcome these challenges.This study aimed to develop an optimized multilayer perceptron neural network model and an ensemble learning model,while objectively assessing the capacity of SIF to predict GPP.Identifying robust models capable of enhancing the accuracy of GPP predictions was the ultimate goal.This study utilized continuous observations of SIF and meteorological data collected from 2020 to 2021 at a designated research observation station within the Populus plantation ecosystem of the Huanghuaihai agricultural protective forest system in Henan Province,China.By optimizing and evaluating the predictive accuracy and robustness of the models across different temporal scales(half-hourly and daily scales),a multi-layer perceptron(MLP)neural network optimization model based on the back propagation(BP)neural network(BPNN)algorithm(BP/MLP)and MLP and random forest(RF)integration(MLP-RF)ensemble models were constructed,utilizing SIF as the primary predictive variable for GPP.Both the BP/MLP(half-hourly scale model R^(2)=0.885,daily scale model R^(2)=0.921)and the MLP-RF(half-hourly scale model R^(2)=0.845,daily scale model R^(2)=0.914)models showed superior accuracy compared to the BPNN(half-hourly scale model R^(2)=0.841,daily scale model R^(2)=0.918)and the traditional RF(half-hourly scale model R^(2)=0.798,daily scale model R^(2)=0.867)models,with the BP/MLP model consistently outperforming the MLP-RF model.The BP/MLP model,which was optimized through particle swarm optimization(PSO),significantly enhanced the robustness of GPP predictions on a half-hourly scale and daily scale.Considering both half-hourly scale and daily scale in the PSO-BP/MLP modeling,the four indicators,light-use efficiency(LUE),photosynthetically active radiation(PAR),absorbed photosynthetically active radiation(APAR),and the variation in SIF with NIRvP(fSIF(NIRvP)),exhibited the potential for enhancing the accuracy of GPP predictions.This study employed a series of model optimization techniques to develop a GPP prediction model with enhanced performance that objectively evaluated the contributions of the predictive variables.This approach provided an innovative and effective method for assessing the carbon cycle in terrestrial ecosystems.
基金supported by the special fund of the National Clinical Key Specialty Construction Program[(2022)301-2305].
文摘BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.
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).
文摘Purpose:ATLAS is a cross-sectional study aiming to investigate environmental and genetic determinants of athletic performance in healthy Greek competitive athletes(CA).This article presents the study design,investigates the muscle strength performance(MSP)of 289 adult and teenage CA,exercisers,and physically inactive individuals(PI),and proposes predictive models of MSP for adults.Methods:Muscle maximal,speed,and explosive strength(MMS/MSS/MES)at unilateral maximal concentric flexion and extension contraction(FC/EC)were evaluated using Biodex System 3 PRO^(TM)at 60°/s,180°/s,and 300°/s,while additional performance markers were assessed through field ergometric testing.Participants were interviewed about their lifestyle,dietary habits,physical activity,injury,and medical history.Body composition was assessed via bioelectrical impedance.gDNA was extracted from biochemical samples and then genotyped.Statistical analysis was conducted using IBM SPSS Statistics v21.0 and R.Results:Age,fitness,and sex impacted correlations of MSP with body composition and anthropometric measurements(p<0.05).Among CA,females outperformed males in accuracy(p<0.001)while,males outperformed females in anaerobic power,MSP,speed,and endurance(p<0.001).Adult CA outperformed exercisers and PI in MMS,MSS,and MES(p<0.05).Multiple linear regression models,with predictors age,FFM,body extremity,training load explained the majority of variation in MMS(R^(2)_(adj):71.4%–88.9%),MSS(R^(2)_(adj):64.8%–78.4%),and MES(R^(2)_(adj):52.7%–68.4%)at EC,FC,and their mean(p<0.001).Conclusions:Muscle-strengthening strategies should be customized according to individual fitness levels,body composition,and anthropometric measurements.The innovative sex-specific regression models assessing MMS,MSS,and MES at EC and FC provide a framework for personalizing rehabilitation and skill-specific training strategies.
文摘BACKGROUND Laparoscopic distal pancreatectomy(LDP)has emerged as the preferred approach for both benign and malignant lesions located in the pancreatic body and tail.Nevertheless,a notable deficiency persists in the absence of a standardized,procedure-specific metric for evaluating and comparing surgical quality.A composite measure termed“textbook outcome(TO)”,which encompasses key short-term endpoints,has been validated in laparoscopic pancreatoduodenectomy but has not yet been established in dedicated LDP cohorts.The definition and prediction of TO in this context could aid in facilitating cross-institutional benchmarking and fostering advancements in quality improvement.AIM To establish procedure-specific criteria for TO and identify independent predictors of TO failure in patients undergoing LDP.METHODS Consecutive patients who underwent LDP at a single high-volume pancreatic center between January 2015 and August 2022 were retrospectively analyzed.TO was defined as the absence of clinically relevant postoperative pancreatic fistula(grade B/C),post-pancreatectomy hemorrhage(grade B/C),severe complications(Clavien-Dindo≥III),readmission within 30 days,and in-hospital or 30-day mortality.Multivariable logistic regression was employed to identify independent predictors of TO failure,and a nomogram was constructed and internally validated.RESULTS Among 405 eligible patients,286(70.6%)attained TO.Multivariable analysis revealed that female sex[odds ratio(OR)=0.62,95%confidence interval(CI):0.39-0.99]conferred a protective effect,while preoperative endoscopic ultrasound-guided fine-needle aspiration(OR=2.66,95%CI:1.05-6.73),pancreatic portal hypertension(OR=2.81,95%CI:1.06-7.45),and cystic-solid(OR=2.51,95%CI:1.34-4.69)or solid lesions(OR=1.91,95%CI:1.06-3.44)were independently associated with TO failure(all P<0.05).The derived nomogram exhibited modest discrimination and calibration when assessed in both the training and validation datasets.CONCLUSION The proposed LDP-specific definition of TO is feasible and discriminative,and the developed nomogram provides an objective tool for individualized risk assessment.
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Funded by State Railway Administration Research Project(No.2023JS007)National Natural Science Foundation of China(No.52438002)+1 种基金Research and Development Programs for Science and Technology of China Railways Corporation(No.J2023G003)New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the strength and elastic modulus of mortar and concrete prepared with mechanism aggregates of the corresponding lithology,and the stress-strain curves of concrete were investigated.In this paper,a coarse aggregate and mortar matrix bonding assumption is proposed,and a prediction model for the elastic modulus of mortar is established by considering the lithology of the mechanism sand and the slurry components.An equivalent coarse aggregate elastic modulus model was established by considering factors such as coarse aggregate particle size,volume fraction,and mortar thickness between coarse aggregates.Based on the elastic modulus of the equivalent coarse aggregate and the remaining mortar,a prediction model for the elastic modulus of the two and three components of concrete in series and then in parallel was established,and the predicted values differed from the measured values within 10%.It is proposed that the coarse aggregate elastic modulus in highstrength concrete is the most critical factor affecting the elastic modulus of concrete,and as the coarse aggregate elastic modulus increases by 27.7%,the concrete elastic modulus increases by 19.5%.
基金The National Natural Science Foundation of China-Regional Science“Identification of novel drug targets for lung cancer via Mendelian randomization analysis based on blood proteomics”(62362062)The 2025 Xinjiang University Excellent Graduate Innovation Project“Research on identification of therapeutic targets and predictive factors for mental disorders based on proteomics”(XJDX2025YJS151)。
文摘Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential predictive tools,hold promise for advancing early diagnosis of mental disorders.This study aims to evaluate the predictive potential of proteomic features and PRS in multiple mental illnesses(depression,schizophrenia,and post-traumatic stress disorder(PTSD)).Using participant data from the UK Biobank-Pharma Proteomics Project,we screen protein associations with mental disorders through least absolute shrinkage and selection operator(LASSO)analysis and construct a Cox regression risk prediction model by integrating the PRS.Additionally,we evaluate predictive performance using 6 machine learning methods and Kaplan-Meier survival curves.Our findings reveal distinct predictive patterns across dis-orders.For depression,integrating plasma proteins with PRS significantly improves prediction beyond the clinical model(C-index=0.6322).For schizophrenia,adding plasma proteins enhances predictive performance,whereas PRS provides no significant improvement.For PTSD,neither plasma proteins nor PRS add substantial predictive value beyond clinical variables.Risk stratification analysis demonstrat that all three mental disorders models can clearly distinguish high-risk from low-risk groups(depression:HR=2.34,P<0.001;schizophrenia:HR=5.47,P<0.001;PTSD:HR=3.02,P<0.001).Al-though it shows good performance in short-term prediction,its long-term prediction ability has decreased,and it needs to be further optimized in the future.This study underscores the differential utility of biomarkers across mental disorders and provides a rationale for disorder-specific predictive modeling in precision psychiatry.
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Supported by the National Natural Science Foundation of China Youth Training Project,No.2021GZR003and Medical-engineering Interdisciplinary Research Youth Training Project,No.2022YGJC001.
文摘BACKGROUND Integrating conventional ultrasound features with 2D shear wave elastography(2D-SWE)can potentially enhance preoperative hepatocellular carcinoma(HCC)predictions.AIM To develop a 2D-SWE-based predictive model for preoperative identification of HCC.METHODS A retrospective analysis of 884 patients who underwent liver resection and pathology evaluation from February 2021 to August 2023 was conducted at the Oriental Hepatobiliary Surgery Hospital.The patients were divided into the modeling group(n=720)and the control group(n=164).The study included conventional ultrasound,2D-SWE,and preoperative laboratory tests.Multiple logistic regression was used to identify independent predictive factors for RESULTS In the modeling group analysis,maximal elasticity(Emax)of tumors and their peripheries,platelet count,cirrhosis,and blood flow were independent risk indicators for malignancies.These factors yielded an area under the curve of 0.77(95%confidence interval:0.73-0.81)with 84%sensitivity and 61%specificity.The model demonstrated good calibration in both the construction and validation cohorts,as shown by the calibration graph and Hosmer-Lemeshow test(P=0.683 and P=0.658,respectively).Additionally,the mean elasticity(Emean)of the tumor periphery was identified as a risk factor for microvascular invasion(MVI)in malignant liver tumors(P=0.003).Patients receiving antiviral treatment differed significantly in platelet count(P=0.002),Emax of tumors(P=0.033),Emean of tumors(P=0.042),Emax at tumor periphery(P<0.001),and Emean at tumor periphery(P=0.003).CONCLUSION 2D-SWE’s hardness value serves as a valuable marker for enhancing the preoperative diagnosis of malignant liver lesions,correlating significantly with MVI and antiviral treatment efficacy.
文摘Soils were collected from 2-year (2-y) and 3-year (3-y) old red-pine seedling plots in two tree nurseries, Hayward in the north and Wilson in the southwestern part of Wisconsin State respectively, and equilibrated with 0.01 M Ca(NO3)2 for soil solution Zn and Mn (solu-Zn and Mn), and with 0.01 M Ca(NO3)2+0.005 M EDTA for soil adsorbed Zn and Mn (ad-Zn and Mn). Buffering capacity of soil Zn and Mn (b-Zn and Mn) was obtained from the ratio of ad-Zn and Mn to the solu-Zn and Mn. The concerned traces in pine seedling needles (ndls), stems(sts) and roots (rts) were simultaneously measured. The results obtained show that:About 60% of solu- and ad- Zn ranged from 0.2 to 0.4 and from 1 to 2μ/g soil respectively. About 70% of b-Zn was within) 3-10.The highest content of solu-Zn compared with the lowest showed a discrepancy of more than 10-fold. The two forms of soil Zn were commonly higher in Wilson than in Hayward Nursery.About 80% of solu-, ad- and b-Mn were within 3-10, 5-5.8 μg/ g soil and 1-2 respectively. Influence of low buffering capacity on solu-Zn and Mn was about 20 times stronger than that of the high.The E-value, a ratio of accumulated Zn and Mn in needles to those in the soil solution, is proved to be: E-Zn > E-Mn;E-sts> E-ndls or E-rts; and E-2y > E-3y.Curvilinear and/ or linear correlations between soil solu-, ad- and b-Zn and Mn and ndls-, sts-, rts-Zn and Mn were at very significant or significant levels.For predicting ndls-Zn and Mn, two realizable and simple models from two regression equations were established through the selection of related parameters and dependent variables. Binary regression analysis basically eliminated the influence of soil pH on the prediction of Zn and Mn in needles. Soil pH was thus thought to be excluded from the model.
文摘BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for clinical stage II/III rectal cancer.However,few patients achieve a complete pathological response,and most patients require surgical resection and adjuvant therapy.Therefore,identifying risk factors and developing accurate models to predict the prognosis of LARC patients are of great clinical significance.AIM To establish effective prognostic nomograms and risk score prediction models to predict overall survival(OS)and disease-free survival(DFS)for LARC treated with NT.METHODS Nomograms and risk factor score prediction models were based on patients who received NT at the Cancer Hospital from 2015 to 2017.The least absolute shrinkage and selection operator regression model were utilized to screen for prognostic risk factors,which were validated by the Cox regression method.Assessment of the performance of the two prediction models was conducted using receiver operating characteristic curves,and that of the two nomograms was conducted by calculating the concordance index(C-index)and calibration curves.The results were validated in a cohort of 65 patients from 2015 to 2017.RESULTS Seven features were significantly associated with OS and were included in the OS prediction nomogram and prediction model:Vascular_tumors_bolt,cancer nodules,yN,body mass index,matchmouth distance from the edge,nerve aggression and postoperative carcinoembryonic antigen.The nomogram showed good predictive value for OS,with a C-index of 0.91(95%CI:0.85,0.97)and good calibration.In the validation cohort,the C-index was 0.69(95%CI:0.53,0.84).The risk factor prediction model showed good predictive value.The areas under the curve for 3-and 5-year survival were 0.811 and 0.782.The nomogram for predicting DFS included ypTNM and nerve aggression and showed good calibration and a C-index of 0.77(95%CI:0.69,0.85).In the validation cohort,the C-index was 0.71(95%CI:0.61,0.81).The prediction model for DFS also had good predictive value,with an AUC for 3-year survival of 0.784 and an AUC for 5-year survival of 0.754.CONCLUSION We established accurate nomograms and prediction models for predicting OS and DFS in patients with LARC after undergoing NT.
