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: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
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
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
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
BACKGROUND Assessment of liver reserve function(LRF)is essential for predicting the prognosis of patients with chronic liver disease(CLD)and determines the extent of liver resection in patients with hepatocellular car...BACKGROUND Assessment of liver reserve function(LRF)is essential for predicting the prognosis of patients with chronic liver disease(CLD)and determines the extent of liver resection in patients with hepatocellular carcinoma.AIM To establish noninvasive models for LRF assessment based on liver stiffness measurement(LSM)and to evaluate their clinical performance.METHODS A total of 360 patients with compensated CLD were retrospectively analyzed as the training cohort.The new predictive models were established through logistic regression analysis and were validated internally in a prospective cohort(132 patients).RESULTS Our study defined indocyanine green retention rate at 15 min(ICGR15)≥10%as mildly impaired LRF and ICGR15≥20%as severely impaired LRF.We constructed predictive models of LRF,named the mLPaM and sLPaM,which involved only LSM,prothrombin time international normalized ratio to albumin ratio(PTAR),age and model for end-stage liver disease(MELD).The area under the curve of the mLPaM model(0.855,0.872,respectively)and sLPaM model(0.869,0.876,respectively)were higher than that of the methods for MELD,albumin bilirubin grade and PTAR in the two cohorts,and their sensitivity and negative predictive value were the highest among these methods in the training cohort.In addition,the new models showed good sensitivity and accuracy for the diagnosis of LRF impairment in the validation cohort.CONCLUSION The new models had a good predictive performance for LRF and could replace the indocyanine green(ICG)clearance test,especially in patients who are unable to undergo ICG testing.展开更多
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
Prediction of weaning success from invasive mechanical ventilation remains a challenge in everyday clinical practice.Several prediction scores have been developed to guide success during spontaneous breathing trials t...Prediction of weaning success from invasive mechanical ventilation remains a challenge in everyday clinical practice.Several prediction scores have been developed to guide success during spontaneous breathing trials to help with weaning decisions.These scores aim to provide a structured framework to support clinical judgment.However,their effectiveness varies across patient populations,and their predictive accuracy remains inconsistent.In this review,we aim to identify the strengths and limitations of commonly used clinical prediction tools in assessing readiness for ventilator liberation.While scores such as the Rapid Shallow Breathing Index and the Integrative Weaning Index are widely adopted,their sensitivity and specificity often fall short in complex clinical settings.Factors such as underlying disease pathophysiology,patient characteristics,and clinician subjectivity impact score performance and reliability.Moreover,disparities in validation across diverse populations limit generalizability.With growing interest in artificial intelligence(AI)and machine learning,there is potential for enhanced prediction models that integrate multidimensional data and adapt to individual patient profiles.However,current AI approaches face challenges related to interpretability,bias,and ethical implementation.This paper underscores the need for more robust,individualized,and transparent prediction systems and advocates for careful integration of emerging technologies into clinical workflows to optimize weaning success and patient outcomes.展开更多
Soil mineralized nitrogen(N)is a vital component of soil N supply capacity and an important N source for rice growth.Unveiling N mineralization(Nm)process characteristics and developing a simple and effective approach...Soil mineralized nitrogen(N)is a vital component of soil N supply capacity and an important N source for rice growth.Unveiling N mineralization(Nm)process characteristics and developing a simple and effective approach to evaluate soil Nm are imperative to guide N fertilizer application and enhance its efficiency in various paddy soils with different physicochemical properties.Soil properties are important driving factors contributing to soil Nm differences and must be considered to achieve effective N management.Nevertheless,discrepancies in Nm capacity and other key influencing factors remain uncertain.To address this knowledge gap,this study collected 52 paddy soil samples from Taihu Lake Basin,China,which possess vastly different physicochemical properties.The samples were subjected to a 112-d submerged anaerobic incubation experiment at a constant temperature to obtain the soil Nm characteristics.Reaction kinetics models,including one-pool exponential model,two-pool exponential model,and effective cumulative temperature model,were employed to compare characteristic differences between Nm potential(Nmp)and short-term accumulated mineralized N(Amn)processes in relation to soil physicochemical properties.Based on these relationships,simplified Nmp prediction methods for paddy soils were established.The results revealed that the Nmp values were 145.18,88.64,and 21.03 mg kg-1 in paddy soils with pH<6.50,6.50≤pH≤7.50,and pH>7.50,respectively.Significantly,short-term Amn at day 14 showed a good correlation(P<0.01)with Nmp(R2=0.94),indicating that the prevailing short-term incubation experiment is an acceptable marker for Nmp.Moreover,Nmp correlated well with the ultraviolet absorbance value at 260 nm based on NaHCO3 extraction(Na260),further streamlining the Nmp estimation method.The incorporation of easily obtainable soil properties,including pH,total N(TN),and the ratio of total organic carbon to TN(C/N),alongside Na260 for Nmp evaluation allowed the multiple regression model,Nmp=58.62×TN-23.18×pH+13.08×C/N+86.96×Na260,to achieve a high prediction accuracy(R2=0.95).The reliability of this prediction was further validated with published data of paddy soils in the same region and other rice regions,demonstrating the regional applicability and prospects of this model.This study underscored the roles of soil properties in Nm characteristics and mechanisms and established a site-specific prediction model based on rapid extractions and edaphic properties of paddy soils,paving the way for developing rapid and precise Nm prediction models.展开更多
This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et...This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et al developed a robust predictive model demonstrating high accuracy(area under the curve 0.92 in the training cohort)by integrating venous phase radiomic features with alphafetoprotein levels.This noninvasive approach enables early identification of patients unlikely to benefit from transarterial chemoembolization,allowing a timely transition to alternative therapies such as targeted agents or immunotherapy.Such precision strategies may improve clinical outcomes,optimize resource utilization,and increase survival in advanced hepatocellular carcinoma management.Future studies should emphasize external validation and broader clinical adoption.展开更多
Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential S...Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.展开更多
The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There i...The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.展开更多
BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk...BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.展开更多
BACKGROUND Diagnosis of inflammatory bowel disease and assessment of disease activity are fundamentally reliant on endoscopy.Nonetheless,it is costly and invasive,highlighting the necessity for more accessible and non...BACKGROUND Diagnosis of inflammatory bowel disease and assessment of disease activity are fundamentally reliant on endoscopy.Nonetheless,it is costly and invasive,highlighting the necessity for more accessible and non-invasive biomarkers to assist in the diagnosis and evaluation of inflammatory bowel disease.AIM To examine the correlation of biomarkers with endoscopic activity,evaluate their diagnostic significance,and develop models to forecast endo-scopic activity.METHODS We performed a retrospective single-center analysis of 365 patients with ulcerative colitis(UC),319 with Crohn’s disease(CD)and 100 controls at the First Affiliated Hospital of Zhengzhou University from January 2022 to September 2024.The following biomarkers were analyzed:White blood cell,hemoglobin(Hb),platelet(PLT),neutrophil(N),lymphocyte(L),hematocrit(HCT),eosinophil,albumin(ALB),globulin(GLB),C-reactive protein(CRP),erythrocyte sedimentation rate(ESR),ALB/GLB(AGR),CRP/ALB(CAR),CRP/L(CLR),PLT/ALB(PAR),PLT/L(PLR),and N/L(NLR).RESULTS Serum N,PLT,GLB,CRP,ESR,CAR,CLR,PLR,PAR,and NLR levels were significantly elevated(P<0.001 or P<0.05)in the UC and CD groups compared to controls,whereas Hb,HCT,L,ALB,and AGR were reduced(P<0.001 or P<0.05).Aside from L and eosinophil,substantial differences were observed between mild and severe activity in UC and CD(P<0.001 or P<0.05).UC and CD patients who exhibited an endoscopic response after 14 weeks of treatment had elevated CRP,CAR,and CLR levels at baseline compared to endoscopic nonresponders(P<0.01 or P<0.05).The UC nomogram model utilizing ESR,CAR,and PAR,along with the CD nomogram model employing AGR and PAR,demonstrate predictive significance and clinical applicability for assessing endoscopic activity.CONCLUSION White blood cell,Hb,HCT,PLT,N,CRP,ESR,ALB,GLB,AGR,CAR,CLR,PLR,PAR and NLR are significantly correlated with the endoscopic activity of UC and CD.Patients with UC and CD exhibiting elevated CRP,CAR,and CLR levels are more inclined to respond to treatment.Our nomogram models can precisely forecast endoscopic activity.展开更多
The urgent necessity for enhanced risk stratification to improve the efficiency of colonoscopy screening is underscored by the fact that colorectal cancer(CRC)continues to be a primary cause of global cancer mortality...The urgent necessity for enhanced risk stratification to improve the efficiency of colonoscopy screening is underscored by the fact that colorectal cancer(CRC)continues to be a primary cause of global cancer mortality.Conventional models mostly rely on generalized obesity markers including body mass index(BMI),which does not effectively represent oncogenic risk linked with abdominal obesity.Liu et al undertook a large-scale case-control study comprising 6484 firsttime colonoscopy patients at a prominent Chinese hospital between 2020 and 2023 to overcome this restriction.Age,male sex,smoking status,and raised waist-hip ratio(WHR)were found by multivariate logistic regression as independent predictors of advanced colorectal neoplasia(ACN).In a validation cohort of 1891 individuals,a new 7-point risk scoring model was created and stratified into low-(5.0%)ACN prevalence,moderate-(10.3%)and high-risk(17.6%).With C-statistic=0.66 the model showed better discriminating ability than the Asia-Pacific Colorectal Screening(APCS)score(C-statistic=0.63)and the BMI-modified APCS model.These results fit newly published data showing central obesity as a major carcinogenic driver via pro-inflammatory visceral adipokine channels.With the use of WHR,patient risk classification is greatly improved,providing a practical tool to make the most of screening resources in the face of rising CRC incidence rates.Finally,multi-ethnic validation is necessary for the WHR-based scoring model to be considered for integration into global CRC preventive frameworks,since it improves the accuracy of ACN risk prediction.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
BACKGROUND Female depression is a prevalent and increasingly recognized mental health issue.Due to cultural and social factors,many female patients still face challenges in diagnosis and treatment,and traditional asse...BACKGROUND Female depression is a prevalent and increasingly recognized mental health issue.Due to cultural and social factors,many female patients still face challenges in diagnosis and treatment,and traditional assessment methods often fail to identify high-risk individuals accurately.This highlights the necessity of developing more precise predictive tools.Utilizing machine learning(ML)algorithms to construct predictive models may overcome the limitations of traditional methods,providing more comprehensive support for women’s mental health.AIM To construct an ML-nomogram hybrid model that translates multivariate risk predictors of female depressive symptoms into actionable clinical scoring thresholds,optimizing predictive accuracy and interpretability for healthcare applications.METHODS We analyzed data from 7609 female participants aged 18 to 85 years from the Guangdong Provincial Sleep and Psychosomatic Health Survey.Sixteen variables,including anxiety symptoms,insomnia,chronic diseases,exercise habits,and age,were selected based on prior literature and comprehensively incorporated into ML models to maximize predictive information utilization.Three ML algorithms,extreme gradient boosting,support vector machine,and light gradient boosting machine,were employed to construct predictive models.Model performance was evaluated using accuracy,precision,recall,F1 score,and area under the curve(AUC).Feature importance was interpreted using SHapley Additive exPlanations(SHAP),with ablation studies validating the impact of the top five SHAPderived features on predictive performance,and a nomogram was constructed based on these prioritized predictors.Clinical utility was assessed through decision curve analysis.RESULTS The prevalence of depressive symptoms was 6.8%among the sample.The evaluation of predictive models revealed that light gradient boosting machine achieved a top-performing AUC of 0.867,placing it ahead of extreme gradient boosting(AUC=0.862)and support vector machine(AUC=0.849).SHAP analysis identified insomnia,anxiety symptoms,age,chronic disease,and exercise as the top five predictors.The nomogram based on these features demonstrated excellent discrimination(AUC=0.910)and calibration,with significant net benefits in decision curve analysis compared to baseline strategies.The model effectively stratifies depressive symptoms risk,facilitating personalized and quantitative assessments in clinical settings.We also developed an interactive digital version of the nomogram to facilitate its application in clinical practice.CONCLUSION The ML-based model effectively predicts depressive symptoms in women,identifying insomnia,anxiety symptoms,age,chronic diseases,and exercise as key predictors,offering a practical tool for early detection and intervention.展开更多
Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and sub...Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.展开更多
In this article,we comment on the article published by Yu et al.By employing LASSO regression and Cox proportional hazard models,the article identified nine significant variables affecting survival,including body mass...In this article,we comment on the article published by Yu et al.By employing LASSO regression and Cox proportional hazard models,the article identified nine significant variables affecting survival,including body mass index,Karnofsky performance status,and tumor-node-metastasis staging.We firmly concur with Yu et al regarding the vital significance of clinical prediction models(CPMs),including logistic regression and Cox regression for assessment in esophageal carcinoma(EC).However,the nomogram's limitations and the complexities of integrating genetic factors pose challenges.The integration of immunological data with advanced statistics offers new research directions.High-throughput sequencing and big data,facilitated by machine learning,have revolutionized cancer research but require substantial computational resources.The future of CPMs in EC depends on leveraging these technologies to improve predictive accuracy and clinical application,addressing the need for larger datasets,patientreported outcomes,and regular updates for clinical relevance.展开更多
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 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.
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.
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.
基金Startup Fund for Scientific Research of Fujian Medical University,No.2018QH1052Fujian Health Research Talents Training Program,No.2019-1-42.
文摘BACKGROUND Assessment of liver reserve function(LRF)is essential for predicting the prognosis of patients with chronic liver disease(CLD)and determines the extent of liver resection in patients with hepatocellular carcinoma.AIM To establish noninvasive models for LRF assessment based on liver stiffness measurement(LSM)and to evaluate their clinical performance.METHODS A total of 360 patients with compensated CLD were retrospectively analyzed as the training cohort.The new predictive models were established through logistic regression analysis and were validated internally in a prospective cohort(132 patients).RESULTS Our study defined indocyanine green retention rate at 15 min(ICGR15)≥10%as mildly impaired LRF and ICGR15≥20%as severely impaired LRF.We constructed predictive models of LRF,named the mLPaM and sLPaM,which involved only LSM,prothrombin time international normalized ratio to albumin ratio(PTAR),age and model for end-stage liver disease(MELD).The area under the curve of the mLPaM model(0.855,0.872,respectively)and sLPaM model(0.869,0.876,respectively)were higher than that of the methods for MELD,albumin bilirubin grade and PTAR in the two cohorts,and their sensitivity and negative predictive value were the highest among these methods in the training cohort.In addition,the new models showed good sensitivity and accuracy for the diagnosis of LRF impairment in the validation cohort.CONCLUSION The new models had a good predictive performance for LRF and could replace the indocyanine green(ICG)clearance test,especially in patients who are unable to undergo ICG testing.
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
文摘Prediction of weaning success from invasive mechanical ventilation remains a challenge in everyday clinical practice.Several prediction scores have been developed to guide success during spontaneous breathing trials to help with weaning decisions.These scores aim to provide a structured framework to support clinical judgment.However,their effectiveness varies across patient populations,and their predictive accuracy remains inconsistent.In this review,we aim to identify the strengths and limitations of commonly used clinical prediction tools in assessing readiness for ventilator liberation.While scores such as the Rapid Shallow Breathing Index and the Integrative Weaning Index are widely adopted,their sensitivity and specificity often fall short in complex clinical settings.Factors such as underlying disease pathophysiology,patient characteristics,and clinician subjectivity impact score performance and reliability.Moreover,disparities in validation across diverse populations limit generalizability.With growing interest in artificial intelligence(AI)and machine learning,there is potential for enhanced prediction models that integrate multidimensional data and adapt to individual patient profiles.However,current AI approaches face challenges related to interpretability,bias,and ethical implementation.This paper underscores the need for more robust,individualized,and transparent prediction systems and advocates for careful integration of emerging technologies into clinical workflows to optimize weaning success and patient outcomes.
基金supported by the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.Y201956)the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(No.2023QNRC001)the National Key Research and Development Program of China(No.2017YFD200104).
文摘Soil mineralized nitrogen(N)is a vital component of soil N supply capacity and an important N source for rice growth.Unveiling N mineralization(Nm)process characteristics and developing a simple and effective approach to evaluate soil Nm are imperative to guide N fertilizer application and enhance its efficiency in various paddy soils with different physicochemical properties.Soil properties are important driving factors contributing to soil Nm differences and must be considered to achieve effective N management.Nevertheless,discrepancies in Nm capacity and other key influencing factors remain uncertain.To address this knowledge gap,this study collected 52 paddy soil samples from Taihu Lake Basin,China,which possess vastly different physicochemical properties.The samples were subjected to a 112-d submerged anaerobic incubation experiment at a constant temperature to obtain the soil Nm characteristics.Reaction kinetics models,including one-pool exponential model,two-pool exponential model,and effective cumulative temperature model,were employed to compare characteristic differences between Nm potential(Nmp)and short-term accumulated mineralized N(Amn)processes in relation to soil physicochemical properties.Based on these relationships,simplified Nmp prediction methods for paddy soils were established.The results revealed that the Nmp values were 145.18,88.64,and 21.03 mg kg-1 in paddy soils with pH<6.50,6.50≤pH≤7.50,and pH>7.50,respectively.Significantly,short-term Amn at day 14 showed a good correlation(P<0.01)with Nmp(R2=0.94),indicating that the prevailing short-term incubation experiment is an acceptable marker for Nmp.Moreover,Nmp correlated well with the ultraviolet absorbance value at 260 nm based on NaHCO3 extraction(Na260),further streamlining the Nmp estimation method.The incorporation of easily obtainable soil properties,including pH,total N(TN),and the ratio of total organic carbon to TN(C/N),alongside Na260 for Nmp evaluation allowed the multiple regression model,Nmp=58.62×TN-23.18×pH+13.08×C/N+86.96×Na260,to achieve a high prediction accuracy(R2=0.95).The reliability of this prediction was further validated with published data of paddy soils in the same region and other rice regions,demonstrating the regional applicability and prospects of this model.This study underscored the roles of soil properties in Nm characteristics and mechanisms and established a site-specific prediction model based on rapid extractions and edaphic properties of paddy soils,paving the way for developing rapid and precise Nm prediction models.
文摘This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et al developed a robust predictive model demonstrating high accuracy(area under the curve 0.92 in the training cohort)by integrating venous phase radiomic features with alphafetoprotein levels.This noninvasive approach enables early identification of patients unlikely to benefit from transarterial chemoembolization,allowing a timely transition to alternative therapies such as targeted agents or immunotherapy.Such precision strategies may improve clinical outcomes,optimize resource utilization,and increase survival in advanced hepatocellular carcinoma management.Future studies should emphasize external validation and broader clinical adoption.
文摘Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.
基金funding enabled and organized by CAUL and its Member Institutions.
文摘The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.
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 Henan Natural Science Foundation,No.232300421181.
文摘BACKGROUND Diagnosis of inflammatory bowel disease and assessment of disease activity are fundamentally reliant on endoscopy.Nonetheless,it is costly and invasive,highlighting the necessity for more accessible and non-invasive biomarkers to assist in the diagnosis and evaluation of inflammatory bowel disease.AIM To examine the correlation of biomarkers with endoscopic activity,evaluate their diagnostic significance,and develop models to forecast endo-scopic activity.METHODS We performed a retrospective single-center analysis of 365 patients with ulcerative colitis(UC),319 with Crohn’s disease(CD)and 100 controls at the First Affiliated Hospital of Zhengzhou University from January 2022 to September 2024.The following biomarkers were analyzed:White blood cell,hemoglobin(Hb),platelet(PLT),neutrophil(N),lymphocyte(L),hematocrit(HCT),eosinophil,albumin(ALB),globulin(GLB),C-reactive protein(CRP),erythrocyte sedimentation rate(ESR),ALB/GLB(AGR),CRP/ALB(CAR),CRP/L(CLR),PLT/ALB(PAR),PLT/L(PLR),and N/L(NLR).RESULTS Serum N,PLT,GLB,CRP,ESR,CAR,CLR,PLR,PAR,and NLR levels were significantly elevated(P<0.001 or P<0.05)in the UC and CD groups compared to controls,whereas Hb,HCT,L,ALB,and AGR were reduced(P<0.001 or P<0.05).Aside from L and eosinophil,substantial differences were observed between mild and severe activity in UC and CD(P<0.001 or P<0.05).UC and CD patients who exhibited an endoscopic response after 14 weeks of treatment had elevated CRP,CAR,and CLR levels at baseline compared to endoscopic nonresponders(P<0.01 or P<0.05).The UC nomogram model utilizing ESR,CAR,and PAR,along with the CD nomogram model employing AGR and PAR,demonstrate predictive significance and clinical applicability for assessing endoscopic activity.CONCLUSION White blood cell,Hb,HCT,PLT,N,CRP,ESR,ALB,GLB,AGR,CAR,CLR,PLR,PAR and NLR are significantly correlated with the endoscopic activity of UC and CD.Patients with UC and CD exhibiting elevated CRP,CAR,and CLR levels are more inclined to respond to treatment.Our nomogram models can precisely forecast endoscopic activity.
文摘The urgent necessity for enhanced risk stratification to improve the efficiency of colonoscopy screening is underscored by the fact that colorectal cancer(CRC)continues to be a primary cause of global cancer mortality.Conventional models mostly rely on generalized obesity markers including body mass index(BMI),which does not effectively represent oncogenic risk linked with abdominal obesity.Liu et al undertook a large-scale case-control study comprising 6484 firsttime colonoscopy patients at a prominent Chinese hospital between 2020 and 2023 to overcome this restriction.Age,male sex,smoking status,and raised waist-hip ratio(WHR)were found by multivariate logistic regression as independent predictors of advanced colorectal neoplasia(ACN).In a validation cohort of 1891 individuals,a new 7-point risk scoring model was created and stratified into low-(5.0%)ACN prevalence,moderate-(10.3%)and high-risk(17.6%).With C-statistic=0.66 the model showed better discriminating ability than the Asia-Pacific Colorectal Screening(APCS)score(C-statistic=0.63)and the BMI-modified APCS model.These results fit newly published data showing central obesity as a major carcinogenic driver via pro-inflammatory visceral adipokine channels.With the use of WHR,patient risk classification is greatly improved,providing a practical tool to make the most of screening resources in the face of rising CRC incidence rates.Finally,multi-ethnic validation is necessary for the WHR-based scoring model to be considered for integration into global CRC preventive frameworks,since it improves the accuracy of ACN risk prediction.
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Supported by Longyan City Science and Technology Plan Project,No.2024 LYF17067.
文摘BACKGROUND Female depression is a prevalent and increasingly recognized mental health issue.Due to cultural and social factors,many female patients still face challenges in diagnosis and treatment,and traditional assessment methods often fail to identify high-risk individuals accurately.This highlights the necessity of developing more precise predictive tools.Utilizing machine learning(ML)algorithms to construct predictive models may overcome the limitations of traditional methods,providing more comprehensive support for women’s mental health.AIM To construct an ML-nomogram hybrid model that translates multivariate risk predictors of female depressive symptoms into actionable clinical scoring thresholds,optimizing predictive accuracy and interpretability for healthcare applications.METHODS We analyzed data from 7609 female participants aged 18 to 85 years from the Guangdong Provincial Sleep and Psychosomatic Health Survey.Sixteen variables,including anxiety symptoms,insomnia,chronic diseases,exercise habits,and age,were selected based on prior literature and comprehensively incorporated into ML models to maximize predictive information utilization.Three ML algorithms,extreme gradient boosting,support vector machine,and light gradient boosting machine,were employed to construct predictive models.Model performance was evaluated using accuracy,precision,recall,F1 score,and area under the curve(AUC).Feature importance was interpreted using SHapley Additive exPlanations(SHAP),with ablation studies validating the impact of the top five SHAPderived features on predictive performance,and a nomogram was constructed based on these prioritized predictors.Clinical utility was assessed through decision curve analysis.RESULTS The prevalence of depressive symptoms was 6.8%among the sample.The evaluation of predictive models revealed that light gradient boosting machine achieved a top-performing AUC of 0.867,placing it ahead of extreme gradient boosting(AUC=0.862)and support vector machine(AUC=0.849).SHAP analysis identified insomnia,anxiety symptoms,age,chronic disease,and exercise as the top five predictors.The nomogram based on these features demonstrated excellent discrimination(AUC=0.910)and calibration,with significant net benefits in decision curve analysis compared to baseline strategies.The model effectively stratifies depressive symptoms risk,facilitating personalized and quantitative assessments in clinical settings.We also developed an interactive digital version of the nomogram to facilitate its application in clinical practice.CONCLUSION The ML-based model effectively predicts depressive symptoms in women,identifying insomnia,anxiety symptoms,age,chronic diseases,and exercise as key predictors,offering a practical tool for early detection and intervention.
基金supported by the National Key Research and Development Program of China(2022YFB3605902)the National Natural Science Foundation of China(52375411,52293402)。
文摘Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.
基金Supported by the Scientific Research Program of Hunan Provincial Health Commission,No.B202313018450.
文摘In this article,we comment on the article published by Yu et al.By employing LASSO regression and Cox proportional hazard models,the article identified nine significant variables affecting survival,including body mass index,Karnofsky performance status,and tumor-node-metastasis staging.We firmly concur with Yu et al regarding the vital significance of clinical prediction models(CPMs),including logistic regression and Cox regression for assessment in esophageal carcinoma(EC).However,the nomogram's limitations and the complexities of integrating genetic factors pose challenges.The integration of immunological data with advanced statistics offers new research directions.High-throughput sequencing and big data,facilitated by machine learning,have revolutionized cancer research but require substantial computational resources.The future of CPMs in EC depends on leveraging these technologies to improve predictive accuracy and clinical application,addressing the need for larger datasets,patientreported outcomes,and regular updates for clinical relevance.
