Lithium ion battery has typical character of distributed parameter system, and can be described precisely by partial differential equations and multi-physics theory because lithium ion battery is a complicated electro...Lithium ion battery has typical character of distributed parameter system, and can be described precisely by partial differential equations and multi-physics theory because lithium ion battery is a complicated electrochemical energy storage system. A novel failure prediction modeling method of lithium ion battery based on distributed parameter estimation and single particle model is proposed in this work. Lithium ion concentration in the anode of lithium ion battery is an unmeasurable distributed variable. Failure prediction system can estimate lithium ion concentration online, track the failure residual which is the difference between the estimated value and the ideal value. The precaution signal will be triggered when the failure residual is beyond the predefined failure precaution threshold, and the failure countdown prediction module will be activated. The remaining time of the severe failure threshold can be estimated by the failure countdown prediction module according to the changing rate of the failure residual. A simulation example verifies that lithium ion concentration in the anode of lithium ion battery can be estimated exactly and effectively by the failure prediction model. The precaution signal can be triggered reliably, and the remaining time of the severe failure can be forecasted accurately by the failure countdown prediction module.展开更多
Blood test informatics is a field that combines data science,medical informatics,and research to improve management,treatment,and understanding of diseases.This field uses health data,wearable technology,artificial in...Blood test informatics is a field that combines data science,medical informatics,and research to improve management,treatment,and understanding of diseases.This field uses health data,wearable technology,artificial intelligence(AI),and electronic health records(EHRs)to optimize healthcare.EHR informatics focuses on the following:1)Using AI and data analytics to tailor EHR data management for individuals,2)Identifying early signs of complications or predicting blood sugar fluctuations,3)Using continuous glucose monitors(CGMs)and insulin pumps to collect real-time data,4)Assisting doctors and patients with real-time recommendations.In this paper,we will discuss the basic principles of EHR informatics focusing on assisting doctors and patients with accurate recommendations and data management.We will demonstrate a new prediction method that improves accuracy compared to other forecasting technologies.展开更多
Heat exchangers are the core components of energy transfer and conversion and are widely used in the energy,chemical,and other fields.In an actual operational process,load changes lead to variations in the operating c...Heat exchangers are the core components of energy transfer and conversion and are widely used in the energy,chemical,and other fields.In an actual operational process,load changes lead to variations in the operating conditions of the heat exchanger.Evaluating the heat-transfer performance is crucial for the safe and efficient operation of the system.To realize high-precision heat transfer prediction through simulations,instead of using traditional solid equipment,this study proposed a heat transfer prediction modeling method that combines three-dimensional high-precision and one-dimensional real-time dynamic simulations.This method combines the high-precision advantage of three-dimensional simulation with the real-time advantage of one-dimensional simulation.To verify the feasibility of the modeling method,a heat transfer prediction model was constructed based on the heat transfer channel structure of a CO_(2)mixture heat transfer characteristic experimental test system.The steady-state and dynamic heat transfer characteristics of CO_(2)/R32 mixtures were simulated and experimentally tested.Finally,the real-time operational capability of the heat transfer prediction model was verified using a real-time simulator.The results showed that the heat transfer prediction model modeling method proposed in this study could improve the accuracy by 1.75-4.64 times compared with the conventional one-dimensional dynamic model.The established heat transfer prediction model exhibited good accuracy for both dynamic and steady-state processes.The average relative errors with the experimental results were in the range of 0.91%-2.83%under six sets of experimental tests.Thus,the proposed heat transfer prediction model can predict the heat transfer process in real-time under all experimental heat source conditions.展开更多
Objective To explore the clinical characteristics and methods for syndrome differentiation prediction,as well as to construct a predictive model for Qi deficiency and blood stasis syndrome in patients with acute ische...Objective To explore the clinical characteristics and methods for syndrome differentiation prediction,as well as to construct a predictive model for Qi deficiency and blood stasis syndrome in patients with acute ischemic stroke(AIS).Methods This study employed a retrospective case-control design to analyze patients with AIS who received inpatient treatment at the Neurology Department of The First Hospital of Hunan University of Chinese Medicine from January 1,2013 to December 31,2022.AIS patients meeting the diagnostic criteria for Qi deficiency and blood stasis syndrome were stratified into case group,while those without Qi deficiency and blood stasis syndrome were stratified into control group.The demographic characteristics(age and gender),clinical parameters[time from onset to admission,National Institutes of Health Stroke Scale(NIHSS)score,and blood pressure],past medical history,traditional Chinese medicine(TCM)diagnostic characteristics(tongue and pulse),neurological symptoms and signs,imaging findings[magnetic resonance imaging-diffusion weighted imaging(MRI-DWI)],and biochemical indicators of the two groups were collected and compared.The indicators with statistical difference(P<0.05)in univariate analysis were included in multivariate logistic regression analysis to evaluate their predictive value for the diagnosis of Qi deficiency and blood stasis syndrome,and the predictive model was constructed by receiver operating characteristic(ROC)curve analysis.Results The study included 1035 AIS patients,with 404 cases in case group and 631 cases in control group.Compared with control group,patients in case group were significantly older,had extended onset-to-admission time,lower diastolic blood pressure,and lower NIHSS scores(P<0.05).Case group showed lower incidence of hypertension history(P<0.05).Regarding tongue and pulse characteristics,pale and dark tongue colors,white tongue coating,fine pulse,astringent pulse,and sinking pulse were more common in case group.Imaging examinations demonstrated higher proportions of centrum semiovale infarction,cerebral atrophy,and vertebral artery stenosis in case group(P<0.05).Among biochemical indicators,case group showed higher proportions of elevated fasting blood glucose and glycated hemoglobin(HbA1c),while lower proportions of elevated white blood cell count,reduced hemoglobin,and reduced high-density lipoprotein cholesterol(HDL-C)(P<0.05).Multivariate logistic regression analysis identified significant predictors for Qi deficiency and blood stasis syndrome including:fine pulse[odds ratio(OR)=4.38],astringent pulse(OR=3.67),superficial sensory abnormalities(OR=1.86),centrum semiovale infarction(OR=1.57),cerebral atrophy(OR=1.55),vertebral artery stenosis(OR=1.62),and elevated HbA1c(OR=3.52).The ROC curve analysis of the comprehensive prediction model yielded an area under the curve(AUC)of 0.878[95%confidence interval(CI)=0.855-0.900].Conclusion This study finds out that Qi deficiency and blood stasis syndrome represents one of the primary types of AIS.Fine pulse,astringent pulse,superficial sensory abnormalities,centrum semiovale infarction,cerebral atrophy,vertebral artery stenosis,elevated blood glucose,elevated HbA1c,pale and dark tongue colors,and white tongue coating are key objective diagnostic indicators for the syndrome differentiation of AIS with Qi deficiency and blood stasis syndrome.Based on these indicators,a syndrome differentiation prediction model has been developed,offering a more objective basis for clinical diagnosis,and help to rapidly identify this syndrome in clinical practice and reduce misdiagnosis and missed diagnosis.展开更多
Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive cont...Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.展开更多
BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed t...BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed that CRC patients may experience postoperative cognitive dysfunction(POCD).AIM To establish a risk prediction model for POCD in CRC patients and investigate the preventive value of dexmedetomidine(DEX).METHODS A retrospective analysis was conducted on clinical data from 140 CRC patients who underwent surgery at the People’s Hospital of Qian Nan from February 2020 to May 2024.Patients were allocated into a modeling group(n=98)and a validation group(n=42)in a 7:3 ratio.General clinical data were collected.Additionally,in the modeling group,patients who received DEX preoperatively were incorporated into the observation group(n=54),while those who did not were placed in the control group(n=44).The incidence of POCD was recorded for both cohorts.Data analysis was performed using statistical product and service solutions 20.0,with t-tests orχ^(2) tests employed for group comparisons based on the data type.Least absolute shrinkage and selection operator regression was applied to identify influencing factors and reduce the impact of multicollinear predictors among variables.Multivariate analysis was carried out using Logistic regression.Based on the identified risk factors,a risk prediction model for POCD in CRC patients was developed,and the predictive value of these risk factors was evaluated.RESULTS Significant differences were observed between the cognitive dysfunction group and the non-cognitive dysfunction group in diabetes status,alcohol consumption,years of education,anesthesia duration,intraoperative blood loss,intraoperative hypoxemia,use of DEX during surgery,intraoperative use of vasoactive drugs,surgical time,systemic inflammatory response syndrome(SIRS)score(P<0.05).Multivariate Logistic regression analysis identified that diabetes[odds ratio(OR)=4.679,95%confidence interval(CI)=1.382-15.833],alcohol consumption(OR=5.058,95%CI:1.255-20.380),intraoperative hypoxemia(OR=4.697,95%CI:1.380-15.991),no use of DEX during surgery(OR=3.931,95%CI:1.383-11.175),surgery duration≥90 minutes(OR=4.894,95%CI:1.377-17.394),and a SIRS score≥3(OR=4.133,95%CI:1.323-12.907)were independent risk factors for POCD in CRC patients(P<0.05).A risk prediction model for POCD was constructed using diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score as factors.A receiver operator characteristic curve analysis of these factors revealed the model’s predictive sensitivity(88.56%),specificity(70.64%),and area under the curve(AUC)(AUC=0.852,95%CI:0.773-0.919).The model was validated using 42 CRC patients who met the inclusion criteria,demonstrating sensitivity(80.77%),specificity(81.25%),and accuracy(80.95%),and AUC(0.805)in diagnosing cognitive impairment,with a 95%CI:0.635-0.896.CONCLUSION Logistic regression analysis identified that diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score vigorously influenced the occurrence of POCD.The risk prediction model based on these factors demonstrated good predictive performance for POCD in CRC individuals.This study offers valuable insights for clinical practice and contributes to the prevention and management of POCD under CRC circumstances.展开更多
BACKGROUND 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
The study by Huang et al,published in the World Journal of Gastroenterology,advances intrahepatic cholangiocarcinoma(ICC)management by developing a machine-learning model to predict textbook outcomes(TO)based on preop...The study by Huang et al,published in the World Journal of Gastroenterology,advances intrahepatic cholangiocarcinoma(ICC)management by developing a machine-learning model to predict textbook outcomes(TO)based on preoperative factors.By analyzing data from 376 patients across four Chinese medical centers,the researchers identified key variables influencing TO,including Child-Pugh classification,Eastern Cooperative Oncology Group score,hepatitis B status,and tumor size.The model,created using logistic regression and the extreme gradient boosting algorithm,demonstrated high predictive accuracy,with area under the curve values of 0.8825 for internal validation and 0.8346 for external validation.The integration of the Shapley additive explanation technique enhances the interpretability of the model,which is crucial for clinical decision-making.This research highlights the potential of machine learning to improve surgical planning and patient outcomes in ICC,opening possibilities for personalized treatment approaches based on individual patient characteristics and risk factors.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
Ferroptosis,an iron-dependent form of programmed cell death,has garnered significant attention in tumor research in recent years.Its core characteristics include aberrant accumulation of lipid peroxides and impairment...Ferroptosis,an iron-dependent form of programmed cell death,has garnered significant attention in tumor research in recent years.Its core characteristics include aberrant accumulation of lipid peroxides and impairment of antioxidant defense mechanisms,such as dysfunction of glutathione peroxidase 4.These fea-tures are closely intertwined with the initiation,progression,and therapeutic resistance of hepatocellular carcinoma(HCC).This review presents a systematic overview of the fundamental molecular mechanisms underlying ferroptosis,en-compassing iron metabolism,lipid metabolism,and the antioxidant system.Fur-thermore,it summarizes the potential applications of targeting ferroptosis in liver cancer treatment,including the mechanisms of action of anticancer agents(e.g.,sorafenib)and relevant ferroptosis-related enzymes.Against the backdrop of the growing potential of artificial intelligence(AI)in liver cancer research,various AI-based predictive models for liver cancer are being increasingly developed.On the one hand,this review examines the mechanisms of ferroptosis in HCC to explore novel early detection markers for liver cancer,to provide new insights for the development of AI-based early diagnostic models.On the other hand,it syn-thesizes the current research progress of existing liver cancer predictive models while summarizing key challenges that AI predictive models may encounter in the diagnosis and treatment of HCC.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
Landslide dam failures can cause significant damage to both society and ecosystems.Predicting the failure of these dams in advance enables early preventive measures,thereby minimizing potential harm.This paper aims to...Landslide dam failures can cause significant damage to both society and ecosystems.Predicting the failure of these dams in advance enables early preventive measures,thereby minimizing potential harm.This paper aims to propose a fast and accurate model for predicting the longevity of landslide dams while also addressing the issue of missing data.Given the wide variation in the survival times of landslide dams—from mere minutes to several thousand years—predicting their longevity presents a considerable challenge.The study develops predictive models by considering key factors such as dam geometry,hydrodynamic conditions,materials,and triggering parameters.A dataset of 1045 landslide dam cases is analyzed,categorizing their longevity into three distinct groups:C1(<1 month),C2(1 month to 1 year),and C3(>1 year).Multiple imputation and knearest neighbor algorithms are used to handle missing data on geometric size,hydrodynamic conditions,materials,and triggers.Based on the imputed data,two predictive models are developed:a classification model for dam longevity categories and a regression model for precise longevity predictions.The classification model achieves an accuracy of 88.38%while the regression model outperforms existing models with an R^(2) value of 0.966.Two real-life landslide dam cases are used to validate the models,which show correct classification and small prediction errors.The longevity of landslide dams is jointly influenced by factors such as geometric size,hydrodynamic conditions,materials,and triggering events.Among these,geometric size has the greatest impact,followed by hydrodynamic conditions,materials,and triggers,as confirmed by variable importance in the model development.展开更多
Coal dust explosions are severe safety accidents in coal mine production,posing significant threats to life and property.Predicting the maximum explosion pressure(Pm)of coal dust using deep learning models can effecti...Coal dust explosions are severe safety accidents in coal mine production,posing significant threats to life and property.Predicting the maximum explosion pressure(Pm)of coal dust using deep learning models can effectively assess potential risks and provide a scientific basis for preventing coal dust explosions.In this study,a 20-L explosion sphere apparatus was used to test the maximum explosion pressure of coal dust under seven different particle sizes and ten mass concentrations(Cdust),resulting in a dataset of 70 experimental groups.Through Spearman correlation analysis and random forest feature selection methods,particle size(D_(10),D_(20),D_(50))and mass concentration(Cdust)were identified as critical feature parameters from the ten initial parameters of the coal dust samples.Based on this,a hybrid Long Short-Term Memory(LSTM)network model incorporating a Multi-Head Attention Mechanism and the Sparrow Search Algorithm(SSA)was proposed to predict the maximum explosion pressure of coal dust.The results demonstrate that the SSA-LSTM-Multi-Head Attention model excels in predicting the maximum explosion pressure of coal dust.The four evaluation metrics indicate that the model achieved a coefficient of determination(R^(2)),root mean square error(RMSE),mean absolute percentage error(MAPE),and mean absolute error(MAE)of 0.9841,0.0030,0.0074,and 0.0049,respectively,in the training set.In the testing set,these values were 0.9743,0.0087,0.0108,and 0.0069,respectively.Compared to artificial neural networks(ANN),random forest(RF),support vector machines(SVM),particle swarm optimized-SVM(PSO-SVM)neural networks,and the traditional single-model LSTM,the SSA-LSTM-Multi-Head Attention model demonstrated superior generalization capability and prediction accuracy.The findings of this study not only advance the application of deep learning in coal dust explosion prediction but also provide robust technical support for the prevention and risk assessment of coal dust explosions.展开更多
Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives ...Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.展开更多
In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(M...In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.展开更多
AIM:To explore the relationship between matrix metalloproteinases(MMPs)expression levels in the tumor and the prognosis of uveal melanoma(UM)and to construct prognostic prediction models.METHODS:Transcriptome sequenci...AIM:To explore the relationship between matrix metalloproteinases(MMPs)expression levels in the tumor and the prognosis of uveal melanoma(UM)and to construct prognostic prediction models.METHODS:Transcriptome sequencing data from 17 normal choroid tissues and 53 UM tumor tissues were collected.Based on the differential gene expression levels and their function,MMPs family was selected for establishing risk-score system and prognostic prediction model with machine learning.Tumor microenvironment(TME)analysis was also applied for the impact of immune cell infiltration on prognosis of the disease.RESULTS:Eight MMPs were significantly different expression levels between normal and the tumor tissues.MMP-2 and MMP-28 were selected to construct a risk-score system and divided patients accordingly into high-and low-risk groups.The prediction model based on the risk-score achieved an accuracy of approximately 80%at 1-,3-,and 5-year after diagnosis.Besides,a Nomogram prognostic prediction model which based on risk-score and pathological type(independent prognostic factors after Cox regression analysis)demonstrated good consistency between the predicted outcomes at 1-,3-,and 5-year after diagnosis and the actual prognosis of patients.TME analysis revealed that the high-risk group exhibited higher immune and stromal scores and increased infiltration of tumor-associated macrophages(TAMs)and regulatory T cells compared to the low-risk group.CONCLUSION:Based on MMP-2 and MMP-28 expression levels,our prediction model demonstrates accurate long-term prognosis prediction for UM patients.The aggregation of TAMs and regulatory T cells in the TME of UM may be associated with an unfavorable prognosis.展开更多
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
BACKGROUND Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with ment...BACKGROUND Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with mental disorders induced by rabies vaccination and to construct a risk prediction model to inform strategies for improving patients’mental health.METHODS Patients who received rabies vaccinations at the Department of Infusion Yiwu Central Hospital between August 2024 and July 2025 were included,totaling 384 cases.Data were collected from medical records and included demographic characteristics(age,gender,occupation),lifestyle habits,and details regarding vaccine type,dosage,and injection site.The incidence of psychiatric disorders following vaccination was assessed using standardized anxiety and depression rating scales.Patients were categorized into two groups based on the presence or absence of anxiety and depression symptoms:The psychiatric disorder group and the non-psychiatric disorder group.Differences between the two groups were compared,and high-risk factors were identified using multivariate logistic regression analysis.A predictive model was then developed based on these factors to evaluate its predictive performance.RESULTS Among the 384 patients who received rabies vaccinations,36 cases(9.38%)were diagnosed with anxiety,52 cases(13.54%)with depression,and 88 cases(22.92%)with either condition.Logistic regression analysis identified the following signi ficant risk factors for psychiatric disorders:Education level of primary school or below,exposure site at the head and neck,exposure classified as grade III,family status of divorced/widowed/unmarried/living alone,number of wounds greater than one,and low awareness of rabies prevention and control(P<0.05).The risk prediction model demonstrated good performance,with an area under the receiver operating characteristic curve of 0.859,a specificity of 74.42%,and a sensitivity of 93.02%.CONCLUSION In real-world settings,psychiatric disorders following rabies vaccination are relatively common and are associated with factors such as lower education level,higher exposure severity,vulnerable family status,and limited awareness of rabies prevention and control.The developed risk prediction model may aid in early identification of high-risk individuals and support timely clinical intervention.展开更多
Objective:To construct a clinical prediction model of acupuncture treatment for diminished ovarian reserve(DOR)based on a machine learning algorithm to provide a clinical prediction of acupuncture for ameliorating pre...Objective:To construct a clinical prediction model of acupuncture treatment for diminished ovarian reserve(DOR)based on a machine learning algorithm to provide a clinical prediction of acupuncture for ameliorating pregnancy outcomes in DOR.Methods:We enrolled 377 DOR patients treated with acupuncture and with records of pregnancy outcomes(139 cases of pregnancy and 238 cases failed)exported from the International Patient Registry Platform of Acupuncture-moxibustion(IPRPAM).The predictive variables were determined using Spearman’s correlation analysis and feature engineering methods.The model was constructed by adopting logistic regression,naïve Bayes,random forest,support vector machine,extreme gradient boosting,the knearest neighbor algorithm,linear discriminant analysis,and neural network methods.The models were validated by the area under the curve(AUC),accuracy(ACC),and importance sequencing,and individual pregnancy prediction was conducted for the best-performing model.Results:The key factors determining pregnancy after acupuncture in patients with DOR were age,luteinizing hormone(LH)level after treatment,follicle-stimulating hormone(FSH)level after treatment,the ratio of FSH to LH(FSH/LH)after treatment,and history of acupuncture treatment.Random forest model ACC was 0.95,Fβwas 0.93,Logloss was 0.30,Logloss value was the lowest,the model variables exhibited the highest accuracy and precision.Conclusion:The random forest model for the effects of acupuncture on pregnancy outcomes in patients with DOR,constructed based on the IPRPAM,presents a favorable value for clinical application.展开更多
Machine learning models can predict material properties quickly and accurately at a low computational cost.This study generated novel hybridized nanocomposites with unsaturated polyester resin as the matrix and Areca ...Machine learning models can predict material properties quickly and accurately at a low computational cost.This study generated novel hybridized nanocomposites with unsaturated polyester resin as the matrix and Areca fruit husk fiber(AFHF),tamarind fruit fiber(TFF),and nano-sized coconut shell powder(NCSP).It is challenging to determine the optimal proportion of raw materials in this composite to achieve maximum mechanical properties.This task was accomplished with the help of ML techniques in this study.The tensile strength of the hybridized nanocomposite was increased by 134.06% compared to the neat unsaturated polyester resin at a 10:5:2 wt.% ratio,AFHF:TFF:NCSP.The stiffness and impact behavior of hybridized nanocomposites were similar.The scanning electron microscope showed homogeneous reinforcement and nanofiller distribution in the matrix.However,the hybridized nanocomposite with a 20:5:0 wt.% combination ratio had the highest strain at break of 5.98%,AFHF:TFF:NCSP.The effectiveness of recurrent neural networks and recurrent neural networks with Levenberg’s algorithm was assessed using R2,mean absolute errors,and minimum squared errors.Tensile and impact strength of hybridized nanocomposites were well predicted by the recurrent neural network with Levenberg’s model with 2 and 3 hidden layers,80 neurons and 80 neurons,respectively.A recurrent neural network model with 4 hidden layers,60 neurons,and 2 hidden layers,100 neurons predicted hybridized nanocomposites’Young’s modulus and elongation at break with maximum R2 values.The mean absolute errors and minimum squared errors were evaluated to ensure the reliability of the machine learning algorithms.The models optimize hybridized nanocomposites’mechanical properties,saving time and money during experimental characterization.展开更多
基金This work was supported by the Fundamental Research Funds for the Central Universities （No.2017JBM003）, the National Natural Science Foundation of China （No.61575053, No.61504008）, and the Specialized Research Fund for the Doctoral Program of Higher Education of China （No.20130009120042）.
文摘Lithium ion battery has typical character of distributed parameter system, and can be described precisely by partial differential equations and multi-physics theory because lithium ion battery is a complicated electrochemical energy storage system. A novel failure prediction modeling method of lithium ion battery based on distributed parameter estimation and single particle model is proposed in this work. Lithium ion concentration in the anode of lithium ion battery is an unmeasurable distributed variable. Failure prediction system can estimate lithium ion concentration online, track the failure residual which is the difference between the estimated value and the ideal value. The precaution signal will be triggered when the failure residual is beyond the predefined failure precaution threshold, and the failure countdown prediction module will be activated. The remaining time of the severe failure threshold can be estimated by the failure countdown prediction module according to the changing rate of the failure residual. A simulation example verifies that lithium ion concentration in the anode of lithium ion battery can be estimated exactly and effectively by the failure prediction model. The precaution signal can be triggered reliably, and the remaining time of the severe failure can be forecasted accurately by the failure countdown prediction module.
文摘Blood test informatics is a field that combines data science,medical informatics,and research to improve management,treatment,and understanding of diseases.This field uses health data,wearable technology,artificial intelligence(AI),and electronic health records(EHRs)to optimize healthcare.EHR informatics focuses on the following:1)Using AI and data analytics to tailor EHR data management for individuals,2)Identifying early signs of complications or predicting blood sugar fluctuations,3)Using continuous glucose monitors(CGMs)and insulin pumps to collect real-time data,4)Assisting doctors and patients with real-time recommendations.In this paper,we will discuss the basic principles of EHR informatics focusing on assisting doctors and patients with accurate recommendations and data management.We will demonstrate a new prediction method that improves accuracy compared to other forecasting technologies.
基金supported by the National Key R&D Program of China(Grant No.2022YFE0100100)。
文摘Heat exchangers are the core components of energy transfer and conversion and are widely used in the energy,chemical,and other fields.In an actual operational process,load changes lead to variations in the operating conditions of the heat exchanger.Evaluating the heat-transfer performance is crucial for the safe and efficient operation of the system.To realize high-precision heat transfer prediction through simulations,instead of using traditional solid equipment,this study proposed a heat transfer prediction modeling method that combines three-dimensional high-precision and one-dimensional real-time dynamic simulations.This method combines the high-precision advantage of three-dimensional simulation with the real-time advantage of one-dimensional simulation.To verify the feasibility of the modeling method,a heat transfer prediction model was constructed based on the heat transfer channel structure of a CO_(2)mixture heat transfer characteristic experimental test system.The steady-state and dynamic heat transfer characteristics of CO_(2)/R32 mixtures were simulated and experimentally tested.Finally,the real-time operational capability of the heat transfer prediction model was verified using a real-time simulator.The results showed that the heat transfer prediction model modeling method proposed in this study could improve the accuracy by 1.75-4.64 times compared with the conventional one-dimensional dynamic model.The established heat transfer prediction model exhibited good accuracy for both dynamic and steady-state processes.The average relative errors with the experimental results were in the range of 0.91%-2.83%under six sets of experimental tests.Thus,the proposed heat transfer prediction model can predict the heat transfer process in real-time under all experimental heat source conditions.
基金National Natural Science Foundation of China(U22A20377)Natural Science Foundation of Hunan Province of China(23C0168).
文摘Objective To explore the clinical characteristics and methods for syndrome differentiation prediction,as well as to construct a predictive model for Qi deficiency and blood stasis syndrome in patients with acute ischemic stroke(AIS).Methods This study employed a retrospective case-control design to analyze patients with AIS who received inpatient treatment at the Neurology Department of The First Hospital of Hunan University of Chinese Medicine from January 1,2013 to December 31,2022.AIS patients meeting the diagnostic criteria for Qi deficiency and blood stasis syndrome were stratified into case group,while those without Qi deficiency and blood stasis syndrome were stratified into control group.The demographic characteristics(age and gender),clinical parameters[time from onset to admission,National Institutes of Health Stroke Scale(NIHSS)score,and blood pressure],past medical history,traditional Chinese medicine(TCM)diagnostic characteristics(tongue and pulse),neurological symptoms and signs,imaging findings[magnetic resonance imaging-diffusion weighted imaging(MRI-DWI)],and biochemical indicators of the two groups were collected and compared.The indicators with statistical difference(P<0.05)in univariate analysis were included in multivariate logistic regression analysis to evaluate their predictive value for the diagnosis of Qi deficiency and blood stasis syndrome,and the predictive model was constructed by receiver operating characteristic(ROC)curve analysis.Results The study included 1035 AIS patients,with 404 cases in case group and 631 cases in control group.Compared with control group,patients in case group were significantly older,had extended onset-to-admission time,lower diastolic blood pressure,and lower NIHSS scores(P<0.05).Case group showed lower incidence of hypertension history(P<0.05).Regarding tongue and pulse characteristics,pale and dark tongue colors,white tongue coating,fine pulse,astringent pulse,and sinking pulse were more common in case group.Imaging examinations demonstrated higher proportions of centrum semiovale infarction,cerebral atrophy,and vertebral artery stenosis in case group(P<0.05).Among biochemical indicators,case group showed higher proportions of elevated fasting blood glucose and glycated hemoglobin(HbA1c),while lower proportions of elevated white blood cell count,reduced hemoglobin,and reduced high-density lipoprotein cholesterol(HDL-C)(P<0.05).Multivariate logistic regression analysis identified significant predictors for Qi deficiency and blood stasis syndrome including:fine pulse[odds ratio(OR)=4.38],astringent pulse(OR=3.67),superficial sensory abnormalities(OR=1.86),centrum semiovale infarction(OR=1.57),cerebral atrophy(OR=1.55),vertebral artery stenosis(OR=1.62),and elevated HbA1c(OR=3.52).The ROC curve analysis of the comprehensive prediction model yielded an area under the curve(AUC)of 0.878[95%confidence interval(CI)=0.855-0.900].Conclusion This study finds out that Qi deficiency and blood stasis syndrome represents one of the primary types of AIS.Fine pulse,astringent pulse,superficial sensory abnormalities,centrum semiovale infarction,cerebral atrophy,vertebral artery stenosis,elevated blood glucose,elevated HbA1c,pale and dark tongue colors,and white tongue coating are key objective diagnostic indicators for the syndrome differentiation of AIS with Qi deficiency and blood stasis syndrome.Based on these indicators,a syndrome differentiation prediction model has been developed,offering a more objective basis for clinical diagnosis,and help to rapidly identify this syndrome in clinical practice and reduce misdiagnosis and missed diagnosis.
文摘Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.
基金Supported by the Research Fund of Qiannan Medical College for Nationalities,No.Qnyz202222.
文摘BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed that CRC patients may experience postoperative cognitive dysfunction(POCD).AIM To establish a risk prediction model for POCD in CRC patients and investigate the preventive value of dexmedetomidine(DEX).METHODS A retrospective analysis was conducted on clinical data from 140 CRC patients who underwent surgery at the People’s Hospital of Qian Nan from February 2020 to May 2024.Patients were allocated into a modeling group(n=98)and a validation group(n=42)in a 7:3 ratio.General clinical data were collected.Additionally,in the modeling group,patients who received DEX preoperatively were incorporated into the observation group(n=54),while those who did not were placed in the control group(n=44).The incidence of POCD was recorded for both cohorts.Data analysis was performed using statistical product and service solutions 20.0,with t-tests orχ^(2) tests employed for group comparisons based on the data type.Least absolute shrinkage and selection operator regression was applied to identify influencing factors and reduce the impact of multicollinear predictors among variables.Multivariate analysis was carried out using Logistic regression.Based on the identified risk factors,a risk prediction model for POCD in CRC patients was developed,and the predictive value of these risk factors was evaluated.RESULTS Significant differences were observed between the cognitive dysfunction group and the non-cognitive dysfunction group in diabetes status,alcohol consumption,years of education,anesthesia duration,intraoperative blood loss,intraoperative hypoxemia,use of DEX during surgery,intraoperative use of vasoactive drugs,surgical time,systemic inflammatory response syndrome(SIRS)score(P<0.05).Multivariate Logistic regression analysis identified that diabetes[odds ratio(OR)=4.679,95%confidence interval(CI)=1.382-15.833],alcohol consumption(OR=5.058,95%CI:1.255-20.380),intraoperative hypoxemia(OR=4.697,95%CI:1.380-15.991),no use of DEX during surgery(OR=3.931,95%CI:1.383-11.175),surgery duration≥90 minutes(OR=4.894,95%CI:1.377-17.394),and a SIRS score≥3(OR=4.133,95%CI:1.323-12.907)were independent risk factors for POCD in CRC patients(P<0.05).A risk prediction model for POCD was constructed using diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score as factors.A receiver operator characteristic curve analysis of these factors revealed the model’s predictive sensitivity(88.56%),specificity(70.64%),and area under the curve(AUC)(AUC=0.852,95%CI:0.773-0.919).The model was validated using 42 CRC patients who met the inclusion criteria,demonstrating sensitivity(80.77%),specificity(81.25%),and accuracy(80.95%),and AUC(0.805)in diagnosing cognitive impairment,with a 95%CI:0.635-0.896.CONCLUSION Logistic regression analysis identified that diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score vigorously influenced the occurrence of POCD.The risk prediction model based on these factors demonstrated good predictive performance for POCD in CRC individuals.This study offers valuable insights for clinical practice and contributes to the prevention and management of POCD under CRC circumstances.
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.
文摘The study by Huang et al,published in the World Journal of Gastroenterology,advances intrahepatic cholangiocarcinoma(ICC)management by developing a machine-learning model to predict textbook outcomes(TO)based on preoperative factors.By analyzing data from 376 patients across four Chinese medical centers,the researchers identified key variables influencing TO,including Child-Pugh classification,Eastern Cooperative Oncology Group score,hepatitis B status,and tumor size.The model,created using logistic regression and the extreme gradient boosting algorithm,demonstrated high predictive accuracy,with area under the curve values of 0.8825 for internal validation and 0.8346 for external validation.The integration of the Shapley additive explanation technique enhances the interpretability of the model,which is crucial for clinical decision-making.This research highlights the potential of machine learning to improve surgical planning and patient outcomes in ICC,opening possibilities for personalized treatment approaches based on individual patient characteristics and risk factors.
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 Henan Provincial Science and Technology Research Project,No.252102311168 and No.242102310066the Medical Education Research Project in Henan Province,No.WJLX2024153.
文摘Ferroptosis,an iron-dependent form of programmed cell death,has garnered significant attention in tumor research in recent years.Its core characteristics include aberrant accumulation of lipid peroxides and impairment of antioxidant defense mechanisms,such as dysfunction of glutathione peroxidase 4.These fea-tures are closely intertwined with the initiation,progression,and therapeutic resistance of hepatocellular carcinoma(HCC).This review presents a systematic overview of the fundamental molecular mechanisms underlying ferroptosis,en-compassing iron metabolism,lipid metabolism,and the antioxidant system.Fur-thermore,it summarizes the potential applications of targeting ferroptosis in liver cancer treatment,including the mechanisms of action of anticancer agents(e.g.,sorafenib)and relevant ferroptosis-related enzymes.Against the backdrop of the growing potential of artificial intelligence(AI)in liver cancer research,various AI-based predictive models for liver cancer are being increasingly developed.On the one hand,this review examines the mechanisms of ferroptosis in HCC to explore novel early detection markers for liver cancer,to provide new insights for the development of AI-based early diagnostic models.On the other hand,it syn-thesizes the current research progress of existing liver cancer predictive models while summarizing key challenges that AI predictive models may encounter in the diagnosis and treatment of HCC.
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.
基金support of the National Natural Science Foundation of China(U42107189,20A20111).
文摘Landslide dam failures can cause significant damage to both society and ecosystems.Predicting the failure of these dams in advance enables early preventive measures,thereby minimizing potential harm.This paper aims to propose a fast and accurate model for predicting the longevity of landslide dams while also addressing the issue of missing data.Given the wide variation in the survival times of landslide dams—from mere minutes to several thousand years—predicting their longevity presents a considerable challenge.The study develops predictive models by considering key factors such as dam geometry,hydrodynamic conditions,materials,and triggering parameters.A dataset of 1045 landslide dam cases is analyzed,categorizing their longevity into three distinct groups:C1(<1 month),C2(1 month to 1 year),and C3(>1 year).Multiple imputation and knearest neighbor algorithms are used to handle missing data on geometric size,hydrodynamic conditions,materials,and triggers.Based on the imputed data,two predictive models are developed:a classification model for dam longevity categories and a regression model for precise longevity predictions.The classification model achieves an accuracy of 88.38%while the regression model outperforms existing models with an R^(2) value of 0.966.Two real-life landslide dam cases are used to validate the models,which show correct classification and small prediction errors.The longevity of landslide dams is jointly influenced by factors such as geometric size,hydrodynamic conditions,materials,and triggering events.Among these,geometric size has the greatest impact,followed by hydrodynamic conditions,materials,and triggers,as confirmed by variable importance in the model development.
基金funded by the Research on Intelligent Mining Geological Model and Ventilation Model for Extremely Thin Coal Seam in Heilongjiang Province,China(2021ZXJ02A03)the Demonstration of Intelligent Mining for Comprehensive Mining Face in Extremely Thin Coal Seam in Heilongjiang Province,China(2021ZXJ02A04)the Natural Science Foundation of Heilongjiang Province,China(LH2024E112).
文摘Coal dust explosions are severe safety accidents in coal mine production,posing significant threats to life and property.Predicting the maximum explosion pressure(Pm)of coal dust using deep learning models can effectively assess potential risks and provide a scientific basis for preventing coal dust explosions.In this study,a 20-L explosion sphere apparatus was used to test the maximum explosion pressure of coal dust under seven different particle sizes and ten mass concentrations(Cdust),resulting in a dataset of 70 experimental groups.Through Spearman correlation analysis and random forest feature selection methods,particle size(D_(10),D_(20),D_(50))and mass concentration(Cdust)were identified as critical feature parameters from the ten initial parameters of the coal dust samples.Based on this,a hybrid Long Short-Term Memory(LSTM)network model incorporating a Multi-Head Attention Mechanism and the Sparrow Search Algorithm(SSA)was proposed to predict the maximum explosion pressure of coal dust.The results demonstrate that the SSA-LSTM-Multi-Head Attention model excels in predicting the maximum explosion pressure of coal dust.The four evaluation metrics indicate that the model achieved a coefficient of determination(R^(2)),root mean square error(RMSE),mean absolute percentage error(MAPE),and mean absolute error(MAE)of 0.9841,0.0030,0.0074,and 0.0049,respectively,in the training set.In the testing set,these values were 0.9743,0.0087,0.0108,and 0.0069,respectively.Compared to artificial neural networks(ANN),random forest(RF),support vector machines(SVM),particle swarm optimized-SVM(PSO-SVM)neural networks,and the traditional single-model LSTM,the SSA-LSTM-Multi-Head Attention model demonstrated superior generalization capability and prediction accuracy.The findings of this study not only advance the application of deep learning in coal dust explosion prediction but also provide robust technical support for the prevention and risk assessment of coal dust explosions.
基金supported by the National Natural Science Foundation of China(Grant No.U2342208)support from NSF/Climate Dynamics Award#2025057。
文摘Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.
文摘In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.
基金Supported by the National Natural Science Foundation of China(No.82220108017,No.82141128,No.82101180)Beijing Natural Science Foundation(No.Z220012)+3 种基金The Capital Health Research and Development of Special(No.2020-1-2052)Science&Technology Project of Beijing Municipal Science&Technology Commission(No.Z201100005520045)Sanming Project of Medicine in Shenzhen(No.SZSM202311018)Beijing Science&Technology Development of TCM(No.BJZYYB-2023-17).
文摘AIM:To explore the relationship between matrix metalloproteinases(MMPs)expression levels in the tumor and the prognosis of uveal melanoma(UM)and to construct prognostic prediction models.METHODS:Transcriptome sequencing data from 17 normal choroid tissues and 53 UM tumor tissues were collected.Based on the differential gene expression levels and their function,MMPs family was selected for establishing risk-score system and prognostic prediction model with machine learning.Tumor microenvironment(TME)analysis was also applied for the impact of immune cell infiltration on prognosis of the disease.RESULTS:Eight MMPs were significantly different expression levels between normal and the tumor tissues.MMP-2 and MMP-28 were selected to construct a risk-score system and divided patients accordingly into high-and low-risk groups.The prediction model based on the risk-score achieved an accuracy of approximately 80%at 1-,3-,and 5-year after diagnosis.Besides,a Nomogram prognostic prediction model which based on risk-score and pathological type(independent prognostic factors after Cox regression analysis)demonstrated good consistency between the predicted outcomes at 1-,3-,and 5-year after diagnosis and the actual prognosis of patients.TME analysis revealed that the high-risk group exhibited higher immune and stromal scores and increased infiltration of tumor-associated macrophages(TAMs)and regulatory T cells compared to the low-risk group.CONCLUSION:Based on MMP-2 and MMP-28 expression levels,our prediction model demonstrates accurate long-term prognosis prediction for UM patients.The aggregation of TAMs and regulatory T cells in the TME of UM may be associated with an unfavorable prognosis.
文摘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 2024 Yiwu City Research Plan Project,No.24-3-102.
文摘BACKGROUND Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with mental disorders induced by rabies vaccination and to construct a risk prediction model to inform strategies for improving patients’mental health.METHODS Patients who received rabies vaccinations at the Department of Infusion Yiwu Central Hospital between August 2024 and July 2025 were included,totaling 384 cases.Data were collected from medical records and included demographic characteristics(age,gender,occupation),lifestyle habits,and details regarding vaccine type,dosage,and injection site.The incidence of psychiatric disorders following vaccination was assessed using standardized anxiety and depression rating scales.Patients were categorized into two groups based on the presence or absence of anxiety and depression symptoms:The psychiatric disorder group and the non-psychiatric disorder group.Differences between the two groups were compared,and high-risk factors were identified using multivariate logistic regression analysis.A predictive model was then developed based on these factors to evaluate its predictive performance.RESULTS Among the 384 patients who received rabies vaccinations,36 cases(9.38%)were diagnosed with anxiety,52 cases(13.54%)with depression,and 88 cases(22.92%)with either condition.Logistic regression analysis identified the following signi ficant risk factors for psychiatric disorders:Education level of primary school or below,exposure site at the head and neck,exposure classified as grade III,family status of divorced/widowed/unmarried/living alone,number of wounds greater than one,and low awareness of rabies prevention and control(P<0.05).The risk prediction model demonstrated good performance,with an area under the receiver operating characteristic curve of 0.859,a specificity of 74.42%,and a sensitivity of 93.02%.CONCLUSION In real-world settings,psychiatric disorders following rabies vaccination are relatively common and are associated with factors such as lower education level,higher exposure severity,vulnerable family status,and limited awareness of rabies prevention and control.The developed risk prediction model may aid in early identification of high-risk individuals and support timely clinical intervention.
基金Supported by the Qihuang Scholars Program in 202114th Five-Year National Key R&D Program Project:2022YFC3500504。
文摘Objective:To construct a clinical prediction model of acupuncture treatment for diminished ovarian reserve(DOR)based on a machine learning algorithm to provide a clinical prediction of acupuncture for ameliorating pregnancy outcomes in DOR.Methods:We enrolled 377 DOR patients treated with acupuncture and with records of pregnancy outcomes(139 cases of pregnancy and 238 cases failed)exported from the International Patient Registry Platform of Acupuncture-moxibustion(IPRPAM).The predictive variables were determined using Spearman’s correlation analysis and feature engineering methods.The model was constructed by adopting logistic regression,naïve Bayes,random forest,support vector machine,extreme gradient boosting,the knearest neighbor algorithm,linear discriminant analysis,and neural network methods.The models were validated by the area under the curve(AUC),accuracy(ACC),and importance sequencing,and individual pregnancy prediction was conducted for the best-performing model.Results:The key factors determining pregnancy after acupuncture in patients with DOR were age,luteinizing hormone(LH)level after treatment,follicle-stimulating hormone(FSH)level after treatment,the ratio of FSH to LH(FSH/LH)after treatment,and history of acupuncture treatment.Random forest model ACC was 0.95,Fβwas 0.93,Logloss was 0.30,Logloss value was the lowest,the model variables exhibited the highest accuracy and precision.Conclusion:The random forest model for the effects of acupuncture on pregnancy outcomes in patients with DOR,constructed based on the IPRPAM,presents a favorable value for clinical application.
文摘Machine learning models can predict material properties quickly and accurately at a low computational cost.This study generated novel hybridized nanocomposites with unsaturated polyester resin as the matrix and Areca fruit husk fiber(AFHF),tamarind fruit fiber(TFF),and nano-sized coconut shell powder(NCSP).It is challenging to determine the optimal proportion of raw materials in this composite to achieve maximum mechanical properties.This task was accomplished with the help of ML techniques in this study.The tensile strength of the hybridized nanocomposite was increased by 134.06% compared to the neat unsaturated polyester resin at a 10:5:2 wt.% ratio,AFHF:TFF:NCSP.The stiffness and impact behavior of hybridized nanocomposites were similar.The scanning electron microscope showed homogeneous reinforcement and nanofiller distribution in the matrix.However,the hybridized nanocomposite with a 20:5:0 wt.% combination ratio had the highest strain at break of 5.98%,AFHF:TFF:NCSP.The effectiveness of recurrent neural networks and recurrent neural networks with Levenberg’s algorithm was assessed using R2,mean absolute errors,and minimum squared errors.Tensile and impact strength of hybridized nanocomposites were well predicted by the recurrent neural network with Levenberg’s model with 2 and 3 hidden layers,80 neurons and 80 neurons,respectively.A recurrent neural network model with 4 hidden layers,60 neurons,and 2 hidden layers,100 neurons predicted hybridized nanocomposites’Young’s modulus and elongation at break with maximum R2 values.The mean absolute errors and minimum squared errors were evaluated to ensure the reliability of the machine learning algorithms.The models optimize hybridized nanocomposites’mechanical properties,saving time and money during experimental characterization.
