BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To ...BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023.A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.RESULTS A total of 171 patients were included in this study:93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN.The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800,respectively,while the least absolute shrinkage and selection operator(LASSO)regression model showed sensitivity and specificity values of 0.842 and 0.840,respectively.The area under the curve(AUC)for the logistic model was 0.896,slightly lower than the AUC of 0.904 for the LASSO model.Internal validation with 30%of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model.The LASSO model provided greater utility in clinical decision-making.CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.展开更多
Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the s...Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.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
This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hype...This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.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: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction mode...Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction model for postoperative recurrence in pancreatic cancer patients, incorporating multiple preoperative, intraoperative, and postoperative factors to assist clinical decision-making. Methods: A retrospective study was conducted on 216 patients who underwent surgical treatment for pancreatic malignancy at the First Affiliated Hospital of Chongqing Medical University between January 2015 and January 2023. An independent external validation cohort of 76 patients from the Second Affiliated Hospital of Chongqing Medical University was used to validate the model. Seven independent risk factors for postoperative recurrence were identified through univariate and multivariate Cox regression analyses. The model’s performance was evaluated using the concordance index (C-index) and ROC curves, and its accuracy and clinical value were assessed using calibration curves and decision curve analysis (DCA). Results: The predictive model demonstrated good discriminatory power, with a C-index of 0.72 in the training cohort and 0.66 in the validation cohort. The ROC curves for predicting recurrence at 3, 6, and 12 months postoperatively showed AUC values ranging from 0.72 to 0.83, indicating strong predictive value. Calibration curves and DCA confirmed the model’s accuracy and clinical utility. Conclusion: This study successfully developed and validated a clinical prediction model that incorporates seven independent risk factors for postoperative recurrence in pancreatic cancer. The model provides a useful tool for predicting recurrence risk, aiding in the identification of high-risk patients, and informing clinical decision-making.展开更多
BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk...BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.展开更多
BACKGROUND 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 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
Testicular torsion is a urological emergency that requires prompt diagnosis and treatment,accounting for 10%-15%of cases of acute scrotum.[1]It occurs most frequently during the perinatal period and adolescence and ca...Testicular torsion is a urological emergency that requires prompt diagnosis and treatment,accounting for 10%-15%of cases of acute scrotum.[1]It occurs most frequently during the perinatal period and adolescence and can occur at any age.[2]The incidence of testicular torsion is 1/4,000 in males under 25 years of age and 1/160 in males over 25 years of age.[3]Unilateral torsion is relatively common,with a higher incidence on the left side.Testicular torsion is typically managed through surgical exploration.Necrotic testes,identified by a black appearance,require orchiectomy.[4]展开更多
BACKGROUND Low rectal cancer poses a significant surgical challenge because of its close proximity to the anal sphincter,often requiring radical resection with permanent colostomy to achieve oncological safety.Revisit...BACKGROUND Low rectal cancer poses a significant surgical challenge because of its close proximity to the anal sphincter,often requiring radical resection with permanent colostomy to achieve oncological safety.Revisited rectal anatomy,advances in surgical techniques and neoadjuvant therapies have enabled the possibility of sphincter-preserving procedures,however,it is uniformly not applicable.Selecting appropriate candidates for sphincter preservation is crucial,as an illadvised approach may compromise oncological outcome or lead to poor functional outcomes.Currently there is no consensus-which clinical,anatomical,or molecular factors most accurately predict the feasibility of sphincter-preserving surgery(SPS)in this subset of patients.By identifying these predictors,the study seeks to support improved patient selection,enhance surgical planning,and ultimately contribute to better functional and oncological outcomes in patients with low rectal cancer.AIM To identify predictive factors that determine the feasibility of SPS in patients with low rectal cancer.METHODS A comprehensive literature search was conducted using PubMed/MEDLINE databases.The search focused on various factors influencing the feasibility of SPS in low rectal cancer.These included patient-related factors,anatomical considerations,findings from different imaging modalities,advancements in diagnostic tools and techniques,and the role of neoadjuvant chemoradiotherapy.The relevance of each factor in predicting the potential for sphincter preservation was critically analyzed and presented based on the current evidence RESULTS Multiple studies have identified a range of predictive factors influencing the feasibility of SPS in low rectal cancer.Patient-related factors include age,sex,preoperative continence status,comorbidities,and body mass index.Anatomical considerations,such as tumor distance from the anal verge,involvement of the external anal sphincter,and levator ani muscles,also play a critical role.Additionally,a favourable response to neoadjuvant chemoradiotherapy has been associated with improved suitability for sphincter preservation.Several biomarkers,such as inflammatory markers like interleukins and C-reactive protein,as well as tumor markers like carcinoembryonic antigen,are important.Molecular markers,including BRAF and KRAS mutations and microsatellite instability status,have been linked to prognosis and may further guide decision-making regarding sphincter-preserving approaches.Artificial intelligence(AI)can further add in to select an ideal patient for sphincter preservation.CONCLUSION SPS is feasible in low rectal cancer and depends on patient factors,tumor anatomy and biology,preoperative treatment response,and biomarkers.In addition,tools and technology including AI can further help in selecting an ideal patient for long term optimal outcome.展开更多
BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high...BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high.AIM To compare the clinical characteristics of patients with cirrhosis,cirrhosis combined with intrapulmonary vascular dilatation(IPVD),and HPS,and to establish predictive models for IPVD and HPS.METHODS Patients with cirrhosis were prospectively screened at a liver-specialized university teaching hospital.Clinical information and blood samples were collected,and biomarker levels in blood samples were measured.Patients with cirrhosis were divided into three groups:Those with pure cirrhosis,those with combined IPVD,and those with HPS based on contrast-enhanced transthoracic echocardiography results and the pulmonary alveolar-arterial oxygen gradient values.Univariate logistic regression and Least Absolute Shrinkage and Selection Operator(LASSO)regression methods were utilized to identify risk factors for IPVD and HPS,and nomograms were constructed to predict IPVD and HPS.RESULTS A total of 320 patients were analyzed,with 101 diagnosed with IPVD,of whom 54 were diagnosed with HPS.There were statistically significant differences in clinical parameters among these three groups of patients.Among the tested biomarkers,sphingosine 1 phosphate,angiopoietin-2,and platelet-derived growth factor BB were significantly associated with IPVD and HPS in patients with cirrhosis.Following LASSO logistic regression screening,prediction models for IPVD and HPS were established.The area under the receiver operating characteristic curve for IPVD prediction was 0.792(95%confidence interval[CI]:0.737-0.847),and for HPS prediction was 0.891(95%CI:0.848-0.934).CONCLUSION This study systematically compared the clinical characteristics of patients with cirrhosis,IPVD,and HPS,and constructed predictive models for IPVD and HPS based on clinical parameters and laboratory indicators.These models showed good predictive value for IPVD and HPS in patients with cirrhosis.They can assist clinicians in the early prognosis assessment of patients with cirrhosis,ultimately benefiting the patients.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
Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Pat...Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Patients diagnosed with essential hypertension and admitted to Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shang-hai Hospital of Traditional Chinese Medicine,and Shanghai Hospital of Integrated Tradition-al Chinese and Western Medicine from July 6th 2020 to June 16th 2021,and from August 11th 2023 to January 22nd 2024,were enrolled in this retrospective research.The baselines and clinical biochemical indicators of patients were collected.The SMART-I TCM pulse instru-ment was applied to gather pulse diagram parameters.Multivariate logistic regression was adopted to analyze the risk factors for HTH.RStudio was employed to construct the nomo-gram model,receiver operating characteristic(ROC)curve,and calibration curve(bootstrap self-sampling 200 times),and clinical decision curve were drawn to evaluate the model’s dis-crimination and clinical effectiveness.Results A total of 168 hospitalized patients with essential hypertension were selected and di-vided into non-HTH group(n=29)and HTH group(n=139).Compared with non-HTH group,HTH group had a lower body mass index(BMI),and higher proportions of male pa-tients and drinkers(P<0.05).The ventricular wall thickening(VWT)could not be deter-mined.The proportions of left common carotid intima-media wall thickness(LCCIMWT)and serum creatinine(SCR)were higher in HTH group(P<0.05).The pulse diagram parameter As was significantly higher,and H4/H1 and T1/T were lower in HTH group(P<0.05).Gender,al-cohol consumption,serum creatinine,and the pulse diagram parameter H4/H1 were identi-fied as independent risk factors for HTH(P<0.05).The nomogram’s area under the ROC curve(AUC)was 0.795[95%confidence interval(CI):(0.7066,0.8828)],with a specificity of 0.724 and sensitivity of 0.799.After 200 times repeated bootstrap self-samplings,the calibra-tion curve showed that the simulated curve fits well with the actual curve(x^(2)=9.5002,P=0.3019).The clinical decision curve indicated that the nomogram’s applicability was optimal when the threshold for predicting HTH was between 0.38 and 1.00.Conclusion The nomogram model could be valuable for predicting the onset risk of HTH and pulse diagram parameters can facilitate early screening and prevention of HTH.展开更多
Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and sub...Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.展开更多
BACKGROUND At present,there is a lack of non-invasive indicators to evaluate the changes in endoscopic activity between two visits for patients with Crohn's disease(CD).AIM To develop a model for predicting whethe...BACKGROUND At present,there is a lack of non-invasive indicators to evaluate the changes in endoscopic activity between two visits for patients with Crohn's disease(CD).AIM To develop a model for predicting whether endoscopic activity will improve in CD patients.METHODS This is a single-center retrospective study that included patients diagnosed with CD from January 2014 to December 2022.The patients were randomly divided into a modeling group(70%)and an internal validation group(30%),with an external validation group from January 2023 to March 2024.Univariate and binary logistic regression analyses were conducted to identify independent risk factors,which were used to construct a nomogram model.The model's performance was evaluated using receiver operating characteristic curves,calibration curves,and decision curve analysis(DCA).Additionally,further sensitivity analyses were performed.RESULTS One hundred seventy patients were included in the training group,while 64 were included in the external validation group.A binary logistic stepwise regression analysis revealed that the changes in the amplitudes of albumin(ALB)and fibrinogen(FIB)were independent risk factors for endoscopic improvement.A nomogram model was developed based on these risk factors.The area under the curve of the model for the training group,internal validation group,and external validation group were 0.802,0.788,and 0.787,respectively.The average absolute errors of the calibration curves were 0.011,0.016,and 0.018,respectively.DCA indicated that the model performs well in clinical practice.Additionally,sensitivity analysis demonstrated that the model has strong robustness and applicability.CONCLUSION Our study shows that changes in the amplitudes of ALB and FIB are effective predictors of endoscopic improvement in patients with CD during follow-up visits compared to their previous ones.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
Digital modeling and autonomous control of the die forging process are significant challenges in realizing high-quality intelli-gent forging of components.Using the die forging of AA2014 aluminum alloy as a case study...Digital modeling and autonomous control of the die forging process are significant challenges in realizing high-quality intelli-gent forging of components.Using the die forging of AA2014 aluminum alloy as a case study,a machine-learning-assisted method for di-gital modeling of the forging force and autonomous control in response to forging parameter disturbances was proposed.First,finite ele-ment simulations of the forging processes were conducted under varying friction factors,die temperatures,billet temperatures,and for-ging velocities,and the sample data,including process parameters and forging force under different forging strokes,were gathered.Pre-diction models for the forging force were established using the support vector regression algorithm.The prediction error of F_(f),that is,the forging force required to fill the die cavity fully,was as low as 4.1%.To further improve the prediction accuracy of the model for the ac-tual F_(f),two rounds of iterative forging experiments were conducted using the Bayesian optimization algorithm,and the prediction error of F_(f) in the forging experiments was reduced from 6.0%to 1.5%.Finally,the prediction model of F_(f) combined with a genetic algorithm was used to establish an autonomous optimization strategy for the forging velocity at each stage of the forging stroke,when the billet and die temperatures were disturbed,which realized the autonomous control in response to disturbances.In cases of−20 or−40℃ reductions in the die and billet temperatures,forging experiments conducted with the autonomous optimization strategy maintained the measured F_(f) around the target value of 180 t,with the relative error ranging from−1.3%to+3.1%.This work provides a reference for the study of di-gital modeling and autonomous optimization control of quality factors in the forging process.展开更多
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
基金Supported by the National Key Research and Development Program of China,No.2022YFC2503600。
文摘BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023.A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.RESULTS A total of 171 patients were included in this study:93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN.The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800,respectively,while the least absolute shrinkage and selection operator(LASSO)regression model showed sensitivity and specificity values of 0.842 and 0.840,respectively.The area under the curve(AUC)for the logistic model was 0.896,slightly lower than the AUC of 0.904 for the LASSO model.Internal validation with 30%of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model.The LASSO model provided greater utility in clinical decision-making.CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.
基金supported in part by the National Natural Science Foundation of China under Grant 52077002。
文摘Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.
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 National Natural Science Foundation of China(12072090).
文摘This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.
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.
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.
文摘BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.
基金Supported by the 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 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supported by the National Natural Science Foundation of China(82371709).
文摘Testicular torsion is a urological emergency that requires prompt diagnosis and treatment,accounting for 10%-15%of cases of acute scrotum.[1]It occurs most frequently during the perinatal period and adolescence and can occur at any age.[2]The incidence of testicular torsion is 1/4,000 in males under 25 years of age and 1/160 in males over 25 years of age.[3]Unilateral torsion is relatively common,with a higher incidence on the left side.Testicular torsion is typically managed through surgical exploration.Necrotic testes,identified by a black appearance,require orchiectomy.[4]
文摘BACKGROUND Low rectal cancer poses a significant surgical challenge because of its close proximity to the anal sphincter,often requiring radical resection with permanent colostomy to achieve oncological safety.Revisited rectal anatomy,advances in surgical techniques and neoadjuvant therapies have enabled the possibility of sphincter-preserving procedures,however,it is uniformly not applicable.Selecting appropriate candidates for sphincter preservation is crucial,as an illadvised approach may compromise oncological outcome or lead to poor functional outcomes.Currently there is no consensus-which clinical,anatomical,or molecular factors most accurately predict the feasibility of sphincter-preserving surgery(SPS)in this subset of patients.By identifying these predictors,the study seeks to support improved patient selection,enhance surgical planning,and ultimately contribute to better functional and oncological outcomes in patients with low rectal cancer.AIM To identify predictive factors that determine the feasibility of SPS in patients with low rectal cancer.METHODS A comprehensive literature search was conducted using PubMed/MEDLINE databases.The search focused on various factors influencing the feasibility of SPS in low rectal cancer.These included patient-related factors,anatomical considerations,findings from different imaging modalities,advancements in diagnostic tools and techniques,and the role of neoadjuvant chemoradiotherapy.The relevance of each factor in predicting the potential for sphincter preservation was critically analyzed and presented based on the current evidence RESULTS Multiple studies have identified a range of predictive factors influencing the feasibility of SPS in low rectal cancer.Patient-related factors include age,sex,preoperative continence status,comorbidities,and body mass index.Anatomical considerations,such as tumor distance from the anal verge,involvement of the external anal sphincter,and levator ani muscles,also play a critical role.Additionally,a favourable response to neoadjuvant chemoradiotherapy has been associated with improved suitability for sphincter preservation.Several biomarkers,such as inflammatory markers like interleukins and C-reactive protein,as well as tumor markers like carcinoembryonic antigen,are important.Molecular markers,including BRAF and KRAS mutations and microsatellite instability status,have been linked to prognosis and may further guide decision-making regarding sphincter-preserving approaches.Artificial intelligence(AI)can further add in to select an ideal patient for sphincter preservation.CONCLUSION SPS is feasible in low rectal cancer and depends on patient factors,tumor anatomy and biology,preoperative treatment response,and biomarkers.In addition,tools and technology including AI can further help in selecting an ideal patient for long term optimal outcome.
基金Supported by the National Key Research and Development Program of China,No.2022YFC2305002Beijing Natural Science Foundation,No.7232079+1 种基金Middle-aged and Young Talent Incubation Programs(Clinical Research)of Beijing Youan Hospital,No.BJYAYY-YN2022-12,No.BJYAYY-YN2022-13,and No.BJYAYY-YN2022-01the China Postdoctoral Science Foundation,No.2023M732410 and No.2024T170595.
文摘BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high.AIM To compare the clinical characteristics of patients with cirrhosis,cirrhosis combined with intrapulmonary vascular dilatation(IPVD),and HPS,and to establish predictive models for IPVD and HPS.METHODS Patients with cirrhosis were prospectively screened at a liver-specialized university teaching hospital.Clinical information and blood samples were collected,and biomarker levels in blood samples were measured.Patients with cirrhosis were divided into three groups:Those with pure cirrhosis,those with combined IPVD,and those with HPS based on contrast-enhanced transthoracic echocardiography results and the pulmonary alveolar-arterial oxygen gradient values.Univariate logistic regression and Least Absolute Shrinkage and Selection Operator(LASSO)regression methods were utilized to identify risk factors for IPVD and HPS,and nomograms were constructed to predict IPVD and HPS.RESULTS A total of 320 patients were analyzed,with 101 diagnosed with IPVD,of whom 54 were diagnosed with HPS.There were statistically significant differences in clinical parameters among these three groups of patients.Among the tested biomarkers,sphingosine 1 phosphate,angiopoietin-2,and platelet-derived growth factor BB were significantly associated with IPVD and HPS in patients with cirrhosis.Following LASSO logistic regression screening,prediction models for IPVD and HPS were established.The area under the receiver operating characteristic curve for IPVD prediction was 0.792(95%confidence interval[CI]:0.737-0.847),and for HPS prediction was 0.891(95%CI:0.848-0.934).CONCLUSION This study systematically compared the clinical characteristics of patients with cirrhosis,IPVD,and HPS,and constructed predictive models for IPVD and HPS based on clinical parameters and laboratory indicators.These models showed good predictive value for IPVD and HPS in patients with cirrhosis.They can assist clinicians in the early prognosis assessment of patients with cirrhosis,ultimately benefiting the patients.
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.
基金National Natural Science Foundation of China (81973749 and 8143594)State Administration of Traditional Chinese Medicine High-level Chinese Medicine Key Discipline Construction Project (zyyzdxk-2023069)。
文摘Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Patients diagnosed with essential hypertension and admitted to Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shang-hai Hospital of Traditional Chinese Medicine,and Shanghai Hospital of Integrated Tradition-al Chinese and Western Medicine from July 6th 2020 to June 16th 2021,and from August 11th 2023 to January 22nd 2024,were enrolled in this retrospective research.The baselines and clinical biochemical indicators of patients were collected.The SMART-I TCM pulse instru-ment was applied to gather pulse diagram parameters.Multivariate logistic regression was adopted to analyze the risk factors for HTH.RStudio was employed to construct the nomo-gram model,receiver operating characteristic(ROC)curve,and calibration curve(bootstrap self-sampling 200 times),and clinical decision curve were drawn to evaluate the model’s dis-crimination and clinical effectiveness.Results A total of 168 hospitalized patients with essential hypertension were selected and di-vided into non-HTH group(n=29)and HTH group(n=139).Compared with non-HTH group,HTH group had a lower body mass index(BMI),and higher proportions of male pa-tients and drinkers(P<0.05).The ventricular wall thickening(VWT)could not be deter-mined.The proportions of left common carotid intima-media wall thickness(LCCIMWT)and serum creatinine(SCR)were higher in HTH group(P<0.05).The pulse diagram parameter As was significantly higher,and H4/H1 and T1/T were lower in HTH group(P<0.05).Gender,al-cohol consumption,serum creatinine,and the pulse diagram parameter H4/H1 were identi-fied as independent risk factors for HTH(P<0.05).The nomogram’s area under the ROC curve(AUC)was 0.795[95%confidence interval(CI):(0.7066,0.8828)],with a specificity of 0.724 and sensitivity of 0.799.After 200 times repeated bootstrap self-samplings,the calibra-tion curve showed that the simulated curve fits well with the actual curve(x^(2)=9.5002,P=0.3019).The clinical decision curve indicated that the nomogram’s applicability was optimal when the threshold for predicting HTH was between 0.38 and 1.00.Conclusion The nomogram model could be valuable for predicting the onset risk of HTH and pulse diagram parameters can facilitate early screening and prevention of HTH.
基金supported by the National Key Research and Development Program of China(2022YFB3605902)the National Natural Science Foundation of China(52375411,52293402)。
文摘Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.
文摘BACKGROUND At present,there is a lack of non-invasive indicators to evaluate the changes in endoscopic activity between two visits for patients with Crohn's disease(CD).AIM To develop a model for predicting whether endoscopic activity will improve in CD patients.METHODS This is a single-center retrospective study that included patients diagnosed with CD from January 2014 to December 2022.The patients were randomly divided into a modeling group(70%)and an internal validation group(30%),with an external validation group from January 2023 to March 2024.Univariate and binary logistic regression analyses were conducted to identify independent risk factors,which were used to construct a nomogram model.The model's performance was evaluated using receiver operating characteristic curves,calibration curves,and decision curve analysis(DCA).Additionally,further sensitivity analyses were performed.RESULTS One hundred seventy patients were included in the training group,while 64 were included in the external validation group.A binary logistic stepwise regression analysis revealed that the changes in the amplitudes of albumin(ALB)and fibrinogen(FIB)were independent risk factors for endoscopic improvement.A nomogram model was developed based on these risk factors.The area under the curve of the model for the training group,internal validation group,and external validation group were 0.802,0.788,and 0.787,respectively.The average absolute errors of the calibration curves were 0.011,0.016,and 0.018,respectively.DCA indicated that the model performs well in clinical practice.Additionally,sensitivity analysis demonstrated that the model has strong robustness and applicability.CONCLUSION Our study shows that changes in the amplitudes of ALB and FIB are effective predictors of endoscopic improvement in patients with CD during follow-up visits compared to their previous ones.
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.
基金financially supported by the National Key Research and Development Program of China(No.2022YFB3706901)the National Natural Science Foundation of China(No.52090041)the Young Elite Scientists Sponsorship Program by CAST(No.2022QNRC 001).
文摘Digital modeling and autonomous control of the die forging process are significant challenges in realizing high-quality intelli-gent forging of components.Using the die forging of AA2014 aluminum alloy as a case study,a machine-learning-assisted method for di-gital modeling of the forging force and autonomous control in response to forging parameter disturbances was proposed.First,finite ele-ment simulations of the forging processes were conducted under varying friction factors,die temperatures,billet temperatures,and for-ging velocities,and the sample data,including process parameters and forging force under different forging strokes,were gathered.Pre-diction models for the forging force were established using the support vector regression algorithm.The prediction error of F_(f),that is,the forging force required to fill the die cavity fully,was as low as 4.1%.To further improve the prediction accuracy of the model for the ac-tual F_(f),two rounds of iterative forging experiments were conducted using the Bayesian optimization algorithm,and the prediction error of F_(f) in the forging experiments was reduced from 6.0%to 1.5%.Finally,the prediction model of F_(f) combined with a genetic algorithm was used to establish an autonomous optimization strategy for the forging velocity at each stage of the forging stroke,when the billet and die temperatures were disturbed,which realized the autonomous control in response to disturbances.In cases of−20 or−40℃ reductions in the die and billet temperatures,forging experiments conducted with the autonomous optimization strategy maintained the measured F_(f) around the target value of 180 t,with the relative error ranging from−1.3%to+3.1%.This work provides a reference for the study of di-gital modeling and autonomous optimization control of quality factors in the forging process.
基金Supported by 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.
