Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction mode...Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction model for postoperative recurrence in pancreatic cancer patients, incorporating multiple preoperative, intraoperative, and postoperative factors to assist clinical decision-making. Methods: A retrospective study was conducted on 216 patients who underwent surgical treatment for pancreatic malignancy at the First Affiliated Hospital of Chongqing Medical University between January 2015 and January 2023. An independent external validation cohort of 76 patients from the Second Affiliated Hospital of Chongqing Medical University was used to validate the model. Seven independent risk factors for postoperative recurrence were identified through univariate and multivariate Cox regression analyses. The model’s performance was evaluated using the concordance index (C-index) and ROC curves, and its accuracy and clinical value were assessed using calibration curves and decision curve analysis (DCA). Results: The predictive model demonstrated good discriminatory power, with a C-index of 0.72 in the training cohort and 0.66 in the validation cohort. The ROC curves for predicting recurrence at 3, 6, and 12 months postoperatively showed AUC values ranging from 0.72 to 0.83, indicating strong predictive value. Calibration curves and DCA confirmed the model’s accuracy and clinical utility. Conclusion: This study successfully developed and validated a clinical prediction model that incorporates seven independent risk factors for postoperative recurrence in pancreatic cancer. The model provides a useful tool for predicting recurrence risk, aiding in the identification of high-risk patients, and informing clinical decision-making.展开更多
Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Reg...Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.展开更多
Objectives:This systematic review aimed to assess the properties and feasibility of existing risk prediction models for post-intensive care syndrome outcomes in adult survivors of critical illness.Methods:As of Novemb...Objectives:This systematic review aimed to assess the properties and feasibility of existing risk prediction models for post-intensive care syndrome outcomes in adult survivors of critical illness.Methods:As of November 1,2023,Cochrane Library,PubMed,Embase,CINAHL,Web of Science,PsycInfo,China National Knowledge Infrastructure(CNKI),SinoMed,Wanfang database,and China Science and Technology Journal Database(VIP)were searched.Following the literature screening process,we extracted data encompassing participant sources,post-intensive care syndrome(PICS)outcomes,sample sizes,missing data,predictive factors,model development methodologies,and metrics for model performance and evaluation.We conducted a review and classification of the PICS domains and predictive factors identified in each study.The Prediction Model Risk of Bias Assessment Tool was employed to assess the quality and applicability of the studies.Results:This systematic review included a total of 16 studies,comprising two cognitive impairment studies,four psychological impairment studies,eight physiological impairment studies,and two studies on all three domains.The discriminative ability of prediction models measured by area under the receiver operating characteristic curve was 0.68e0.90.The predictive performance of most models was excellent,but most models were biased and overfitted.All predictive factors tend to encompass age,pre-ICU functional impairment,in-ICU experiences,and early-onset new symptoms.Conclusions:This review identified 16 prediction models and the predictive factors for PICS.Nonetheless,due to the numerous methodological and reporting shortcomings identified in the studies under review,clinicians should exercise caution when interpreting the predictions made by these models.To avert the development of PICS,it is imperative for clinicians to closely monitor prognostic factors,including the in-ICU experience and early-onset new symptoms.展开更多
This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk facto...This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk factors such as chronic obstructive pulmonary disease and hypoalbuminemia,the model demonstrated strong predictive accuracy and offered a pathway to personalized perioperative care.This correspondence highlighted the clinical significance,emphasizing its potential to optimize patient outcomes through tailored inter-ventions.Further prospective validation and application across diverse settings are essential to realize its full potential in advancing esophageal surgery practices.展开更多
BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed t...BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed that CRC patients may experience postoperative cognitive dysfunction(POCD).AIM To establish a risk prediction model for POCD in CRC patients and investigate the preventive value of dexmedetomidine(DEX).METHODS A retrospective analysis was conducted on clinical data from 140 CRC patients who underwent surgery at the People’s Hospital of Qian Nan from February 2020 to May 2024.Patients were allocated into a modeling group(n=98)and a validation group(n=42)in a 7:3 ratio.General clinical data were collected.Additionally,in the modeling group,patients who received DEX preoperatively were incorporated into the observation group(n=54),while those who did not were placed in the control group(n=44).The incidence of POCD was recorded for both cohorts.Data analysis was performed using statistical product and service solutions 20.0,with t-tests orχ2 tests employed for group comparisons based on the data type.Least absolute shrinkage and selection operator regression was applied to identify influencing factors and reduce the impact of multicollinear predictors among variables.Multivariate analysis was carried out using Logistic regression.Based on the identified risk factors,a risk prediction model for POCD in CRC patients was developed,and the predictive value of these risk factors was evaluated.RESULTS Significant differences were observed between the cognitive dysfunction group and the non-cognitive dysfunction group in diabetes status,alcohol consumption,years of education,anesthesia duration,intraoperative blood loss,intraoperative hypoxemia,use of DEX during surgery,intraoperative use of vasoactive drugs,surgical time,systemic inflammatory response syndrome(SIRS)score(P<0.05).Multivariate Logistic regression analysis identified that diabetes[odds ratio(OR)=4.679,95%confidence interval(CI)=1.382-15.833],alcohol consumption(OR=5.058,95%CI:1.255-20.380),intraoperative hypoxemia(OR=4.697,95%CI:1.380-15.991),no use of DEX during surgery(OR=3.931,95%CI:1.383-11.175),surgery duration≥90 minutes(OR=4.894,95%CI:1.377-17.394),and a SIRS score≥3(OR=4.133,95%CI:1.323-12.907)were independent risk factors for POCD in CRC patients(P<0.05).A risk prediction model for POCD was constructed using diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score as factors.A receiver operator characteristic curve analysis of these factors revealed the model’s predictive sensitivity(88.56%),specificity(70.64%),and area under the curve(AUC)(AUC=0.852,95%CI:0.773-0.919).The model was validated using 42 CRC patients who met the inclusion criteria,demonstrating sensitivity(80.77%),specificity(81.25%),and accuracy(80.95%),and AUC(0.805)in diagnosing cognitive impairment,with a 95%CI:0.635-0.896.CONCLUSION Logistic regression analysis identified that diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score vigorously influenced the occurrence of POCD.The risk prediction model based on these factors demonstrated good predictive performance for POCD in CRC individuals.This study offers valuable insights for clinical practice and contributes to the prevention and management of POCD under CRC circumstances.展开更多
Based on the hindcasts from five subseasonal-to-seasonal(S2S)models participating in the S2S Prediction Project,this study evaluates the performance of the multimodel ensemble(MME)approach in predicting the subseasona...Based on the hindcasts from five subseasonal-to-seasonal(S2S)models participating in the S2S Prediction Project,this study evaluates the performance of the multimodel ensemble(MME)approach in predicting the subseasonal precipitation anomalies during summer in China and reveals the contributions of possible driving factors.The results suggest that while single-model ensembles(SMEs)exhibit constrained predictive skills within a limited forecast lead time of three pentads,the MME illustrates an enhanced predictive skill at a lead time of up to four pentads,and even six pentads,in southern China.Based on both deterministic and probabilistic verification metrics,the MME consistently outperforms SMEs,with a more evident advantage observed in probabilistic forecasting.The superior performance of the MME is primarily attributable to the increase in ensemble size,and the enhanced model diversity is also a contributing factor.The reliability of probabilistic skill is largely improved due to the increase in ensemble members,while the resolution term does not exhibit consistent improvement.Furthermore,the Madden–Julian Oscillation(MJO)is revealed as the primary driving factor for the successful prediction of summer precipitation in China using the MME.The improvement by the MME is not solely attributable to the enhancement in the inherent predictive capacity of the MJO itself,but derives from its capability in capturing the more realistic relationship between the MJO and subseasonal precipitation anomalies in China.This study establishes a scientific foundation for acknowledging the advantageous predictive capability of the MME approach in subseasonal predictions of summer precipitation in China,and sheds light on further improving S2S predictions.展开更多
Within the SILVARSTAR project,a user-friendly frequency-based hybrid prediction tool has been developed to assess the environmental impact of railway-induced vibration.This tool is integrated in existing noise mapping...Within the SILVARSTAR project,a user-friendly frequency-based hybrid prediction tool has been developed to assess the environmental impact of railway-induced vibration.This tool is integrated in existing noise mapping software.Following modern vibration standards and guidelines,the vibration velocity level in a building in each frequency band is expressed as the sum of a force density(source term),line source transfer mobility(propagation term)and building correction factor(receiver term).A hybrid approach is used that allows for a combination of experimental data and numerical predictions,providing increased flexibility and applicability.The train and track properties can be selected from a database or entered as numerical values.The user can select soil impedance and transfer functions from a database,pre-computed for a wide range of parameters with state-of-the-art models.An experimental database of force densities,transfer functions,free field vibration and input parameters is also provided.The building response is estimated by means of building correction factors.Assumptions within the modelling approach are made to reduce computation time but these can influence prediction accuracy;this is quantified for the case of a nominal intercity train running at different speeds on a ballasted track supported by homogeneous soil of varying stiffness.The paper focuses on the influence of these parameters on the compliance of the track–soil system and the free field response.We also demonstrate the use and discuss the validation of the vibration prediction tool for the case of a high-speed train running on a ballasted track in Lincent(Belgium).展开更多
BACKGROUND Breast cancer(BC)is the second most common malignancy globally.Young and middle-aged patients face more pressures from diagnosis,treatment,costs,and psychological issues like self-image concerns,social barr...BACKGROUND Breast cancer(BC)is the second most common malignancy globally.Young and middle-aged patients face more pressures from diagnosis,treatment,costs,and psychological issues like self-image concerns,social barriers,and professional challenges.Compared to other age groups,they have higher recurrence rates,lower survival rates,and increased risk of depression.Research is lacking on factors influencing depressive symptoms and predictive models for this age group.AIM To analyze factors influencing depressive symptoms in young/middle-aged BC patients and construct a depression risk predictive model.METHODS A total of 360 patients undergoing BC treatment at two tertiary hospitals in Jiangsu Province,China from November 2023 to April 2024 were included in the study.Participants were surveyed using a general information questionnaire,the patient health questionnaire depression scale,the visual analog scale for pain,the revised family support scale,and the long form of the international physical activity questionnaire.Univariate and multivariate analyses were conducted to identify the factors affecting depression in middle-aged and young BC patients,and a predictive model for depression risk was developed based on these findings.RESULTS Among the 360 middle-aged and young BC patients,the incidence rate of depressive symptoms was 38.61%(139/360).Multivariate analysis revealed that tumor grade,patient’s monthly income,pain score,family support score,and physical activity score were factors influencing depression in this patient group(P<0.05).The risk prediction model constructed based on these factors yielded an area under the receiver operating characteristic curve of 0.852,with a maximum Youden index of 0.973,sensitivity of 86.80%,specificity of 89.50%,and a diagnostic odds ratio of 0.552.The Hosmer-Lemeshow test for goodness of fit indicated an adequate model fit(χ^(2)=0.360,P=0.981).CONCLUSION The constructed predictive model demonstrates good predictive performance and can serve as a reference for medical professionals to early identify high-risk patients and implement corresponding preventive measures to decrease the incidence of depressive symptoms in this population.展开更多
A multicenter study recently published introduced a novel prognostic model for predicting esophagogastric variceal rebleeding after endoscopic treatment in patients with cirrhosis.The model incorporated six readily av...A multicenter study recently published introduced a novel prognostic model for predicting esophagogastric variceal rebleeding after endoscopic treatment in patients with cirrhosis.The model incorporated six readily available clinical variables—albumin level,aspartate aminotransferase level,white blood cell count,ascites,portal vein thrombosis,and bleeding signs—and demonstrated promising predictive performance.However,limitations,including the retrospective design and exclusion of patients with hepatocellular carcinoma,may affect the generaliz-ability of the model.Additionally,further improvement is needed in the model’s discrimination between intermediate-and high-risk groups in external.Prospec-tive validation and inclusion of additional variables are recommended to enhan-ce predictive accuracy across diverse clinical scenarios.展开更多
A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary ...A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary micro-variables evolution at different temperatures and their interaction.The dislocation density was incorporated into the model to capture the effect of creep deformation on precipitation.Quantitative transmission electron microscopy and experimental data obtained from a previous study were used to calibrate the model.Subsequently,the developed constitutive model was implemented in the finite element(FE)software ABAQUS via the user subroutines for TSCA process simulation and the springback prediction of an integral panel.A TSCA test was performed.The result shows that the maximum radius deviation between the formed plate and the simulation results is less than 0.4 mm,thus validating the effectiveness of the developed constitutive model and FE model.展开更多
Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currentl...Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.展开更多
BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages ...BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.展开更多
The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was p...The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.展开更多
BACKGROUND Major depressive disorder is a common mental illness among adolescents and is the largest disease burden in this age group.Most adolescent patients with depression have suicidal ideation(SI);however,few stu...BACKGROUND Major depressive disorder is a common mental illness among adolescents and is the largest disease burden in this age group.Most adolescent patients with depression have suicidal ideation(SI);however,few studies have focused on the factors related to SI,and effective predictive models are lacking.AIM To construct a risk prediction model for SI in adolescent depression and provide a reference assessment tool for prevention.METHODS The data of 150 adolescent patients with depression at the First People's Hospital of Lianyungang from June 2020 to December 2022 were retrospectively analyzed.Based on whether or not they had SI,they were divided into a SI group(n=91)and a non-SI group(n=59).The general data and laboratory indices of the two groups were compared.Logistic regression was used to analyze the factors influencing SI in adolescent patients with depression,a nomogram prediction model was constructed based on the analysis results,and internal evaluation was performed.Receiver operating characteristic and calibration curves were used to evaluate the model’s efficacy,and the clinical application value was evaluated using decision curve analysis(DCA).RESULTS There were differences in trauma history,triggers,serum ferritin levels(SF),highsensitivity C-reactive protein levels(hs-CRP),and high-density lipoprotein(HDLC)levels between the two groups(P<0.05).Logistic regression analysis showed that trauma history,predisposing factors,SF,hs-CRP,and HDL-C were factors influencing SI in adolescent patients with depression.The area under the curve of the nomogram prediction model was 0.831(95%CI:0.763–0.899),sensitivity was 0.912,and specificity was 0.678.The higher net benefit of the DCA and the average absolute error of the calibration curve were 0.043,indicating that the model had a good fit.CONCLUSION The nomogram prediction model based on trauma history,triggers,ferritin,serum hs-CRP,and HDL-C levels can effectively predict the risk of SI in adolescent patients with depression.展开更多
BACKGROUND Models for predicting hepatitis B e antigen(HBeAg)seroconversion in patients with HBeAg-positive chronic hepatitis B(CHB)after nucleos(t)ide analog treatment are rare.AIM To establish a simple scoring model...BACKGROUND Models for predicting hepatitis B e antigen(HBeAg)seroconversion in patients with HBeAg-positive chronic hepatitis B(CHB)after nucleos(t)ide analog treatment are rare.AIM To establish a simple scoring model based on a response-guided therapy(RGT)strategy for predicting HBeAg seroconversion and hepatitis B surface antigen(HBsAg)clearance.METHODS In this study,75 previously treated patients with HBeAg-positive CHB underwent a 52-week peginterferon-alfa(PEG-IFNα)treatment and a 24-wk follow-up.Logistic regression analysis was used to assess parameters at baseline,week 12,and week 24 to predict HBeAg seroconversion at 24 wk post-treatment.The two best predictors at each time point were used to establish a prediction model for PEG-IFNαtherapy efficacy.Parameters at each time point that met the corresponding optimal cutoff thresholds were scored as 1 or 0.RESULTS The two most meaningful predictors were HBsAg≤1000 IU/mL and HBeAg≤3 S/CO at baseline,HBsAg≤600 IU/mL and HBeAg≤3 S/CO at week 12,and HBsAg≤300 IU/mL and HBeAg≤2 S/CO at week 24.With a total score of 0 vs 2 at baseline,week 12,and week 24,the response rates were 23.8%,15.2%,and 11.1%vs 81.8%,80.0%,and 82.4%,respectively,and the HBsAg clearance rates were 2.4%,3.0%,and 0.0%,vs 54.5%,40.0%,and 41.2%,respectively.CONCLUSION We successfully established a predictive model and diagnosis-treatment process using the RGT strategy to predict HBeAg and HBsAg seroconversion in patients with HBeAg-positive CHB undergoing PEG-IFNαtherapy.展开更多
Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential S...Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.展开更多
BACKGROUND Gallbladder cancer(GBC)is the most common malignant tumor of the biliary system,and is often undetected until advanced stages,making curative surgery unfeasible for many patients.Curative surgery remains th...BACKGROUND Gallbladder cancer(GBC)is the most common malignant tumor of the biliary system,and is often undetected until advanced stages,making curative surgery unfeasible for many patients.Curative surgery remains the only option for long-term survival.Accurate postsurgical prognosis is crucial for effective treatment planning.tumor-node-metastasis staging,which focuses on tumor infiltration,lymph node metastasis,and distant metastasis,limits the accuracy of prognosis.Nomograms offer a more comprehensive and personalized approach by visually analyzing a broader range of prognostic factors,enhancing the precision of treatment planning for patients with GBC.AIM A retrospective study analyzed the clinical and pathological data of 93 patients who underwent radical surgery for GBC at Peking University People's Hospital from January 2015 to December 2020.Kaplan-Meier analysis was used to calculate the 1-,2-and 3-year survival rates.The log-rank test was used to evaluate factors impacting prognosis,with survival curves plotted for significant variables.Single-factor analysis revealed statistically significant differences,and multivariate Cox regression identified independent prognostic factors.A nomogram was developed and validated with receiver operating characteristic curves and calibration curves.Among 93 patients who underwent radical surgery for GBC,30 patients survived,accounting for 32.26%of the sample,with a median survival time of 38 months.The 1-year,2-year,and 3-year survival rates were 83.87%,68.82%,and 53.57%,respectively.Univariate analysis revealed that carbohydrate antigen 19-9 expre-ssion,T stage,lymph node metastasis,histological differentiation,surgical margins,and invasion of the liver,ex-trahepatic bile duct,nerves,and vessels(P≤0.001)significantly impacted patient prognosis after curative surgery.Multivariate Cox regression identified lymph node metastasis(P=0.03),histological differentiation(P<0.05),nerve invasion(P=0.036),and extrahepatic bile duct invasion(P=0.014)as independent risk factors.A nomogram model with a concordance index of 0.838 was developed.Internal validation confirmed the model's consistency in predicting the 1-year,2-year,and 3-year survival rates.CONCLUSION Lymph node metastasis,tumor differentiation,extrahepatic bile duct invasion,and perineural invasion are independent risk factors.A nomogram based on these factors can be used to personalize and improve treatment strategies.展开更多
The rapid prediction of aerodynamic performance is critical in the conceptual and preliminary design of hypersonic vehicles. This study focused on axisymmetric body configurations commonly used in such vehicles and pr...The rapid prediction of aerodynamic performance is critical in the conceptual and preliminary design of hypersonic vehicles. This study focused on axisymmetric body configurations commonly used in such vehicles and proposed a multi-fidelity neural network (MFNN) framework to fuse aerodynamic data of varying quality. A data-driven prediction model was constructed using a pointwise modeling method based on generating lines to input geometric features into the network. The MFNN framework combined low-fidelity and high-fidelity networks, trained on aerodynamic performance data from engineering rapid computation methods and CFD, respectively, using spherically blunted cones as examples. The results showed that the MFNN effectively integrated multi-fidelity data, achieving prediction accuracy close to CFD results in most regions, with errors under 5% in key stagnation areas. The model demonstrated strong generalization capabilities for varying cone dimensions and flight conditions. Furthermore, it significantly reduced dependence on high-fidelity data, enabling efficient aerodynamic performance predictions with limited datasets. This study provides a novel methodology for rapid aerodynamic performance prediction, offering both accuracy and efficiency, and contributes to the design of hypersonic vehicles.展开更多
BACKGROUND Sepsis is a severe complication in hospitalized patients with diabetic foot(DF),often associated with high morbidity and mortality.Despite its clinical significance,limited tools exist for early risk predic...BACKGROUND Sepsis is a severe complication in hospitalized patients with diabetic foot(DF),often associated with high morbidity and mortality.Despite its clinical significance,limited tools exist for early risk prediction.AIM To identify key risk factors and evaluate the predictive value of a nomogram model for sepsis in this population.METHODS This retrospective study included 216 patients with DF admitted from January 2022 to June 2024.Patients were classified into sepsis(n=31)and non-sepsis(n=185)groups.Baseline characteristics,clinical parameters,and laboratory data were analyzed.Independent risk factors were identified through multivariable logistic regression,and a nomogram model was developed and validated.The model's performance was assessed by its discrimination(AUC),calibration(Hosmer-Lemeshow test,calibration plots),and clinical utility[decision curve analysis(DCA)].RESULTS The multivariable analysis identified six independent predictors of sepsis:Diabetes duration,DF Texas grade,white blood cell count,glycated hemoglobin,Creactive protein,and albumin.A nomogram integrating these factors achieved excellent diagnostic performance,with an AUC of 0.908(95%CI:0.865-0.956)and robust internal validation(AUC:0.906).Calibration results showed strong agreement between predicted and observed probabilities(Hosmer-Lemeshow P=0.926).DCA demonstrated superior net benefit compared to extreme intervention scenarios,highlighting its clinical utility.CONCLUSION The nomogram prediction model,based on six key risk factors,demonstrates strong predictive value,calibration,and clinical utility for sepsis in patients with DF.This tool offers a practical approach for early risk stratification,enabling timely interventions and improved clinical management in this high-risk population.展开更多
Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learnin...Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.展开更多
文摘Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction model for postoperative recurrence in pancreatic cancer patients, incorporating multiple preoperative, intraoperative, and postoperative factors to assist clinical decision-making. Methods: A retrospective study was conducted on 216 patients who underwent surgical treatment for pancreatic malignancy at the First Affiliated Hospital of Chongqing Medical University between January 2015 and January 2023. An independent external validation cohort of 76 patients from the Second Affiliated Hospital of Chongqing Medical University was used to validate the model. Seven independent risk factors for postoperative recurrence were identified through univariate and multivariate Cox regression analyses. The model’s performance was evaluated using the concordance index (C-index) and ROC curves, and its accuracy and clinical value were assessed using calibration curves and decision curve analysis (DCA). Results: The predictive model demonstrated good discriminatory power, with a C-index of 0.72 in the training cohort and 0.66 in the validation cohort. The ROC curves for predicting recurrence at 3, 6, and 12 months postoperatively showed AUC values ranging from 0.72 to 0.83, indicating strong predictive value. Calibration curves and DCA confirmed the model’s accuracy and clinical utility. Conclusion: This study successfully developed and validated a clinical prediction model that incorporates seven independent risk factors for postoperative recurrence in pancreatic cancer. The model provides a useful tool for predicting recurrence risk, aiding in the identification of high-risk patients, and informing clinical decision-making.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd..(Grant No.H20230317)。
文摘Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.
基金supported by the Scientific Research Project of Shanghai Municipal Health Commission(202140047)the Characteristic Research Project of Shanghai General Hospital(CCTR-2022N03)the Technology Standardization Management and Promotion Project of Shanghai Shenkang Hospital Development Center(SHDC22022219)and the funding organization has played no roles in the survey's design,implementation,and analysis.
文摘Objectives:This systematic review aimed to assess the properties and feasibility of existing risk prediction models for post-intensive care syndrome outcomes in adult survivors of critical illness.Methods:As of November 1,2023,Cochrane Library,PubMed,Embase,CINAHL,Web of Science,PsycInfo,China National Knowledge Infrastructure(CNKI),SinoMed,Wanfang database,and China Science and Technology Journal Database(VIP)were searched.Following the literature screening process,we extracted data encompassing participant sources,post-intensive care syndrome(PICS)outcomes,sample sizes,missing data,predictive factors,model development methodologies,and metrics for model performance and evaluation.We conducted a review and classification of the PICS domains and predictive factors identified in each study.The Prediction Model Risk of Bias Assessment Tool was employed to assess the quality and applicability of the studies.Results:This systematic review included a total of 16 studies,comprising two cognitive impairment studies,four psychological impairment studies,eight physiological impairment studies,and two studies on all three domains.The discriminative ability of prediction models measured by area under the receiver operating characteristic curve was 0.68e0.90.The predictive performance of most models was excellent,but most models were biased and overfitted.All predictive factors tend to encompass age,pre-ICU functional impairment,in-ICU experiences,and early-onset new symptoms.Conclusions:This review identified 16 prediction models and the predictive factors for PICS.Nonetheless,due to the numerous methodological and reporting shortcomings identified in the studies under review,clinicians should exercise caution when interpreting the predictions made by these models.To avert the development of PICS,it is imperative for clinicians to closely monitor prognostic factors,including the in-ICU experience and early-onset new symptoms.
文摘This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk factors such as chronic obstructive pulmonary disease and hypoalbuminemia,the model demonstrated strong predictive accuracy and offered a pathway to personalized perioperative care.This correspondence highlighted the clinical significance,emphasizing its potential to optimize patient outcomes through tailored inter-ventions.Further prospective validation and application across diverse settings are essential to realize its full potential in advancing esophageal surgery practices.
基金Supported by the Research Fund of Qiannan Medical College for Nationalities,No.Qnyz202222.
文摘BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed that CRC patients may experience postoperative cognitive dysfunction(POCD).AIM To establish a risk prediction model for POCD in CRC patients and investigate the preventive value of dexmedetomidine(DEX).METHODS A retrospective analysis was conducted on clinical data from 140 CRC patients who underwent surgery at the People’s Hospital of Qian Nan from February 2020 to May 2024.Patients were allocated into a modeling group(n=98)and a validation group(n=42)in a 7:3 ratio.General clinical data were collected.Additionally,in the modeling group,patients who received DEX preoperatively were incorporated into the observation group(n=54),while those who did not were placed in the control group(n=44).The incidence of POCD was recorded for both cohorts.Data analysis was performed using statistical product and service solutions 20.0,with t-tests orχ2 tests employed for group comparisons based on the data type.Least absolute shrinkage and selection operator regression was applied to identify influencing factors and reduce the impact of multicollinear predictors among variables.Multivariate analysis was carried out using Logistic regression.Based on the identified risk factors,a risk prediction model for POCD in CRC patients was developed,and the predictive value of these risk factors was evaluated.RESULTS Significant differences were observed between the cognitive dysfunction group and the non-cognitive dysfunction group in diabetes status,alcohol consumption,years of education,anesthesia duration,intraoperative blood loss,intraoperative hypoxemia,use of DEX during surgery,intraoperative use of vasoactive drugs,surgical time,systemic inflammatory response syndrome(SIRS)score(P<0.05).Multivariate Logistic regression analysis identified that diabetes[odds ratio(OR)=4.679,95%confidence interval(CI)=1.382-15.833],alcohol consumption(OR=5.058,95%CI:1.255-20.380),intraoperative hypoxemia(OR=4.697,95%CI:1.380-15.991),no use of DEX during surgery(OR=3.931,95%CI:1.383-11.175),surgery duration≥90 minutes(OR=4.894,95%CI:1.377-17.394),and a SIRS score≥3(OR=4.133,95%CI:1.323-12.907)were independent risk factors for POCD in CRC patients(P<0.05).A risk prediction model for POCD was constructed using diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score as factors.A receiver operator characteristic curve analysis of these factors revealed the model’s predictive sensitivity(88.56%),specificity(70.64%),and area under the curve(AUC)(AUC=0.852,95%CI:0.773-0.919).The model was validated using 42 CRC patients who met the inclusion criteria,demonstrating sensitivity(80.77%),specificity(81.25%),and accuracy(80.95%),and AUC(0.805)in diagnosing cognitive impairment,with a 95%CI:0.635-0.896.CONCLUSION Logistic regression analysis identified that diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score vigorously influenced the occurrence of POCD.The risk prediction model based on these factors demonstrated good predictive performance for POCD in CRC individuals.This study offers valuable insights for clinical practice and contributes to the prevention and management of POCD under CRC circumstances.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.42175052 and U2442206)the Joint Research Project for Meteorological Capacity Improvement(Grant No.23NLTSQ007,23NLTSZ003)+2 种基金the Innovative Development Special Project of the China Meteorological Administration(Grant No.CXFZ2023J002)the National Key R&D Program of China(Grant No.2023YFC3007700,2024YFC3013100)the China Meteorological Administration Youth Innovation Team(Grant No.CMA2024QN06)。
文摘Based on the hindcasts from five subseasonal-to-seasonal(S2S)models participating in the S2S Prediction Project,this study evaluates the performance of the multimodel ensemble(MME)approach in predicting the subseasonal precipitation anomalies during summer in China and reveals the contributions of possible driving factors.The results suggest that while single-model ensembles(SMEs)exhibit constrained predictive skills within a limited forecast lead time of three pentads,the MME illustrates an enhanced predictive skill at a lead time of up to four pentads,and even six pentads,in southern China.Based on both deterministic and probabilistic verification metrics,the MME consistently outperforms SMEs,with a more evident advantage observed in probabilistic forecasting.The superior performance of the MME is primarily attributable to the increase in ensemble size,and the enhanced model diversity is also a contributing factor.The reliability of probabilistic skill is largely improved due to the increase in ensemble members,while the resolution term does not exhibit consistent improvement.Furthermore,the Madden–Julian Oscillation(MJO)is revealed as the primary driving factor for the successful prediction of summer precipitation in China using the MME.The improvement by the MME is not solely attributable to the enhancement in the inherent predictive capacity of the MJO itself,but derives from its capability in capturing the more realistic relationship between the MJO and subseasonal precipitation anomalies in China.This study establishes a scientific foundation for acknowledging the advantageous predictive capability of the MME approach in subseasonal predictions of summer precipitation in China,and sheds light on further improving S2S predictions.
基金the project SILVARSTAR funded from the Shift2Rail Joint Undertaking under the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement 101015442。
文摘Within the SILVARSTAR project,a user-friendly frequency-based hybrid prediction tool has been developed to assess the environmental impact of railway-induced vibration.This tool is integrated in existing noise mapping software.Following modern vibration standards and guidelines,the vibration velocity level in a building in each frequency band is expressed as the sum of a force density(source term),line source transfer mobility(propagation term)and building correction factor(receiver term).A hybrid approach is used that allows for a combination of experimental data and numerical predictions,providing increased flexibility and applicability.The train and track properties can be selected from a database or entered as numerical values.The user can select soil impedance and transfer functions from a database,pre-computed for a wide range of parameters with state-of-the-art models.An experimental database of force densities,transfer functions,free field vibration and input parameters is also provided.The building response is estimated by means of building correction factors.Assumptions within the modelling approach are made to reduce computation time but these can influence prediction accuracy;this is quantified for the case of a nominal intercity train running at different speeds on a ballasted track supported by homogeneous soil of varying stiffness.The paper focuses on the influence of these parameters on the compliance of the track–soil system and the free field response.We also demonstrate the use and discuss the validation of the vibration prediction tool for the case of a high-speed train running on a ballasted track in Lincent(Belgium).
基金Supported by Jiangsu Provincial Cadre Healthcare Scientific Research Grant Project,No.BJ23019Jiangsu Provincial Association of Maternal and Child Healthcare Scientific Research Grant Project,No.FYX202350+2 种基金Special Fund for the Project of Enhancing Academic Capability of Integrative Nursing,No.ZXYJHHL-K-2023-M20Jiangsu Provincial Graduate Student Practice and Innovation Program Project,No.SJCX24_0833the Training Project for Backbone Talents in Traditional Chinese Medicine Nursing in Nanjing Region,No.Ningwei Zhongyi[2023]No.8.
文摘BACKGROUND Breast cancer(BC)is the second most common malignancy globally.Young and middle-aged patients face more pressures from diagnosis,treatment,costs,and psychological issues like self-image concerns,social barriers,and professional challenges.Compared to other age groups,they have higher recurrence rates,lower survival rates,and increased risk of depression.Research is lacking on factors influencing depressive symptoms and predictive models for this age group.AIM To analyze factors influencing depressive symptoms in young/middle-aged BC patients and construct a depression risk predictive model.METHODS A total of 360 patients undergoing BC treatment at two tertiary hospitals in Jiangsu Province,China from November 2023 to April 2024 were included in the study.Participants were surveyed using a general information questionnaire,the patient health questionnaire depression scale,the visual analog scale for pain,the revised family support scale,and the long form of the international physical activity questionnaire.Univariate and multivariate analyses were conducted to identify the factors affecting depression in middle-aged and young BC patients,and a predictive model for depression risk was developed based on these findings.RESULTS Among the 360 middle-aged and young BC patients,the incidence rate of depressive symptoms was 38.61%(139/360).Multivariate analysis revealed that tumor grade,patient’s monthly income,pain score,family support score,and physical activity score were factors influencing depression in this patient group(P<0.05).The risk prediction model constructed based on these factors yielded an area under the receiver operating characteristic curve of 0.852,with a maximum Youden index of 0.973,sensitivity of 86.80%,specificity of 89.50%,and a diagnostic odds ratio of 0.552.The Hosmer-Lemeshow test for goodness of fit indicated an adequate model fit(χ^(2)=0.360,P=0.981).CONCLUSION The constructed predictive model demonstrates good predictive performance and can serve as a reference for medical professionals to early identify high-risk patients and implement corresponding preventive measures to decrease the incidence of depressive symptoms in this population.
文摘A multicenter study recently published introduced a novel prognostic model for predicting esophagogastric variceal rebleeding after endoscopic treatment in patients with cirrhosis.The model incorporated six readily available clinical variables—albumin level,aspartate aminotransferase level,white blood cell count,ascites,portal vein thrombosis,and bleeding signs—and demonstrated promising predictive performance.However,limitations,including the retrospective design and exclusion of patients with hepatocellular carcinoma,may affect the generaliz-ability of the model.Additionally,further improvement is needed in the model’s discrimination between intermediate-and high-risk groups in external.Prospec-tive validation and inclusion of additional variables are recommended to enhan-ce predictive accuracy across diverse clinical scenarios.
基金supported by the National Key R&D Program of China(No.2021YFB3400900)the National Natural Science Foundation of China(Nos.52175373,52205435)+1 种基金Natural Science Foundation of Hunan Province,China(No.2022JJ40621)the Innovation Fund of National Commercial Aircraft Manufacturing Engineering Technology Center,China(No.COMACSFGS-2022-1875)。
文摘A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary micro-variables evolution at different temperatures and their interaction.The dislocation density was incorporated into the model to capture the effect of creep deformation on precipitation.Quantitative transmission electron microscopy and experimental data obtained from a previous study were used to calibrate the model.Subsequently,the developed constitutive model was implemented in the finite element(FE)software ABAQUS via the user subroutines for TSCA process simulation and the springback prediction of an integral panel.A TSCA test was performed.The result shows that the maximum radius deviation between the formed plate and the simulation results is less than 0.4 mm,thus validating the effectiveness of the developed constitutive model and FE model.
基金supported by National Natural Science Foundation of China,China(No.42004016)HuBei Natural Science Fund,China(No.2020CFB329)+1 种基金HuNan Natural Science Fund,China(No.2023JJ60559,2023JJ60560)the State Key Laboratory of Geodesy and Earth’s Dynamics self-deployment project,China(No.S21L6101)。
文摘Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.
文摘BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.
基金financially supported by the National Key Research and Development Program of China(2022YFB3706800,2020YFB1710100)the National Natural Science Foundation of China(51821001,52090042,52074183)。
文摘The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.
文摘BACKGROUND Major depressive disorder is a common mental illness among adolescents and is the largest disease burden in this age group.Most adolescent patients with depression have suicidal ideation(SI);however,few studies have focused on the factors related to SI,and effective predictive models are lacking.AIM To construct a risk prediction model for SI in adolescent depression and provide a reference assessment tool for prevention.METHODS The data of 150 adolescent patients with depression at the First People's Hospital of Lianyungang from June 2020 to December 2022 were retrospectively analyzed.Based on whether or not they had SI,they were divided into a SI group(n=91)and a non-SI group(n=59).The general data and laboratory indices of the two groups were compared.Logistic regression was used to analyze the factors influencing SI in adolescent patients with depression,a nomogram prediction model was constructed based on the analysis results,and internal evaluation was performed.Receiver operating characteristic and calibration curves were used to evaluate the model’s efficacy,and the clinical application value was evaluated using decision curve analysis(DCA).RESULTS There were differences in trauma history,triggers,serum ferritin levels(SF),highsensitivity C-reactive protein levels(hs-CRP),and high-density lipoprotein(HDLC)levels between the two groups(P<0.05).Logistic regression analysis showed that trauma history,predisposing factors,SF,hs-CRP,and HDL-C were factors influencing SI in adolescent patients with depression.The area under the curve of the nomogram prediction model was 0.831(95%CI:0.763–0.899),sensitivity was 0.912,and specificity was 0.678.The higher net benefit of the DCA and the average absolute error of the calibration curve were 0.043,indicating that the model had a good fit.CONCLUSION The nomogram prediction model based on trauma history,triggers,ferritin,serum hs-CRP,and HDL-C levels can effectively predict the risk of SI in adolescent patients with depression.
基金Supported by the Anhui Provincial Natural Science Foundation,No.2108085MH298the Scientific Research Project of the Second Affiliated Hospital of Anhui Medical University,No.2019GMFY02 and 2021lcxk027the Scientific Research Project of Colleges and Universities in Anhui Province,No.KJ2021A0323.
文摘BACKGROUND Models for predicting hepatitis B e antigen(HBeAg)seroconversion in patients with HBeAg-positive chronic hepatitis B(CHB)after nucleos(t)ide analog treatment are rare.AIM To establish a simple scoring model based on a response-guided therapy(RGT)strategy for predicting HBeAg seroconversion and hepatitis B surface antigen(HBsAg)clearance.METHODS In this study,75 previously treated patients with HBeAg-positive CHB underwent a 52-week peginterferon-alfa(PEG-IFNα)treatment and a 24-wk follow-up.Logistic regression analysis was used to assess parameters at baseline,week 12,and week 24 to predict HBeAg seroconversion at 24 wk post-treatment.The two best predictors at each time point were used to establish a prediction model for PEG-IFNαtherapy efficacy.Parameters at each time point that met the corresponding optimal cutoff thresholds were scored as 1 or 0.RESULTS The two most meaningful predictors were HBsAg≤1000 IU/mL and HBeAg≤3 S/CO at baseline,HBsAg≤600 IU/mL and HBeAg≤3 S/CO at week 12,and HBsAg≤300 IU/mL and HBeAg≤2 S/CO at week 24.With a total score of 0 vs 2 at baseline,week 12,and week 24,the response rates were 23.8%,15.2%,and 11.1%vs 81.8%,80.0%,and 82.4%,respectively,and the HBsAg clearance rates were 2.4%,3.0%,and 0.0%,vs 54.5%,40.0%,and 41.2%,respectively.CONCLUSION We successfully established a predictive model and diagnosis-treatment process using the RGT strategy to predict HBeAg and HBsAg seroconversion in patients with HBeAg-positive CHB undergoing PEG-IFNαtherapy.
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Supported by Xiao-Ping Chen Foundation for The Development of Science and Technology of Hubei Province,No.CXPJJH122002-061.
文摘BACKGROUND Gallbladder cancer(GBC)is the most common malignant tumor of the biliary system,and is often undetected until advanced stages,making curative surgery unfeasible for many patients.Curative surgery remains the only option for long-term survival.Accurate postsurgical prognosis is crucial for effective treatment planning.tumor-node-metastasis staging,which focuses on tumor infiltration,lymph node metastasis,and distant metastasis,limits the accuracy of prognosis.Nomograms offer a more comprehensive and personalized approach by visually analyzing a broader range of prognostic factors,enhancing the precision of treatment planning for patients with GBC.AIM A retrospective study analyzed the clinical and pathological data of 93 patients who underwent radical surgery for GBC at Peking University People's Hospital from January 2015 to December 2020.Kaplan-Meier analysis was used to calculate the 1-,2-and 3-year survival rates.The log-rank test was used to evaluate factors impacting prognosis,with survival curves plotted for significant variables.Single-factor analysis revealed statistically significant differences,and multivariate Cox regression identified independent prognostic factors.A nomogram was developed and validated with receiver operating characteristic curves and calibration curves.Among 93 patients who underwent radical surgery for GBC,30 patients survived,accounting for 32.26%of the sample,with a median survival time of 38 months.The 1-year,2-year,and 3-year survival rates were 83.87%,68.82%,and 53.57%,respectively.Univariate analysis revealed that carbohydrate antigen 19-9 expre-ssion,T stage,lymph node metastasis,histological differentiation,surgical margins,and invasion of the liver,ex-trahepatic bile duct,nerves,and vessels(P≤0.001)significantly impacted patient prognosis after curative surgery.Multivariate Cox regression identified lymph node metastasis(P=0.03),histological differentiation(P<0.05),nerve invasion(P=0.036),and extrahepatic bile duct invasion(P=0.014)as independent risk factors.A nomogram model with a concordance index of 0.838 was developed.Internal validation confirmed the model's consistency in predicting the 1-year,2-year,and 3-year survival rates.CONCLUSION Lymph node metastasis,tumor differentiation,extrahepatic bile duct invasion,and perineural invasion are independent risk factors.A nomogram based on these factors can be used to personalize and improve treatment strategies.
文摘The rapid prediction of aerodynamic performance is critical in the conceptual and preliminary design of hypersonic vehicles. This study focused on axisymmetric body configurations commonly used in such vehicles and proposed a multi-fidelity neural network (MFNN) framework to fuse aerodynamic data of varying quality. A data-driven prediction model was constructed using a pointwise modeling method based on generating lines to input geometric features into the network. The MFNN framework combined low-fidelity and high-fidelity networks, trained on aerodynamic performance data from engineering rapid computation methods and CFD, respectively, using spherically blunted cones as examples. The results showed that the MFNN effectively integrated multi-fidelity data, achieving prediction accuracy close to CFD results in most regions, with errors under 5% in key stagnation areas. The model demonstrated strong generalization capabilities for varying cone dimensions and flight conditions. Furthermore, it significantly reduced dependence on high-fidelity data, enabling efficient aerodynamic performance predictions with limited datasets. This study provides a novel methodology for rapid aerodynamic performance prediction, offering both accuracy and efficiency, and contributes to the design of hypersonic vehicles.
文摘BACKGROUND Sepsis is a severe complication in hospitalized patients with diabetic foot(DF),often associated with high morbidity and mortality.Despite its clinical significance,limited tools exist for early risk prediction.AIM To identify key risk factors and evaluate the predictive value of a nomogram model for sepsis in this population.METHODS This retrospective study included 216 patients with DF admitted from January 2022 to June 2024.Patients were classified into sepsis(n=31)and non-sepsis(n=185)groups.Baseline characteristics,clinical parameters,and laboratory data were analyzed.Independent risk factors were identified through multivariable logistic regression,and a nomogram model was developed and validated.The model's performance was assessed by its discrimination(AUC),calibration(Hosmer-Lemeshow test,calibration plots),and clinical utility[decision curve analysis(DCA)].RESULTS The multivariable analysis identified six independent predictors of sepsis:Diabetes duration,DF Texas grade,white blood cell count,glycated hemoglobin,Creactive protein,and albumin.A nomogram integrating these factors achieved excellent diagnostic performance,with an AUC of 0.908(95%CI:0.865-0.956)and robust internal validation(AUC:0.906).Calibration results showed strong agreement between predicted and observed probabilities(Hosmer-Lemeshow P=0.926).DCA demonstrated superior net benefit compared to extreme intervention scenarios,highlighting its clinical utility.CONCLUSION The nomogram prediction model,based on six key risk factors,demonstrates strong predictive value,calibration,and clinical utility for sepsis in patients with DF.This tool offers a practical approach for early risk stratification,enabling timely interventions and improved clinical management in this high-risk population.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)Postdoctoral Fellowship Program of CPSF(GZC20232598)+1 种基金China Postdoctoral Science Foundation(2024M753168)National Key Scientific and Technological Infrastructure Project“Earth System Numerical Simulation Facility”(EarthLab)。
文摘Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.
