Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

OBJECTIVE T o explore the long-term effect of multidimensional evidence-based interventions based on i-PARIHS theoretical framework on reduction of incidence of central line-associated bloodstream infections(CLABSI)in pediatric intensive care units(PICU)of pediatrics department and evaluate the impact on nurses'compliance to taking the interventions and use intensity of catheters.METHODS By means of quasi-experimental design,the multidimensional intervention system covering multidisciplinary collaboration,standardized operation procedures,information system optimization and hierarchical training was established and staged for implementa-tion of 5 years(from T0 baseline stage to T3 maintenance stage).The variations in implementation rates of cathe-ter maintenance(daily maintenance,dressings change,catheter removal)were analyzed by Chi-square test,and the change of incidence of CLABSI was monitored with the use of statistical process control U chart.RESULTS The nurses'compliance to operations was remarkably improved(P＜0.05)o The implementation rate of dressings change continuously increased from 52.91％in T0 to81.62％in T3(x2=72.444,P＜0.001),the implementa-tion rate of catheter removal increased from 48.72％to 79.31％(x2=8.179,P=0.042).The incidence rate of CLABSI decreased from 1.92％0 in 2019 to 0.5％0 in 2022,and the control chart showed that most of the months fluctuated within control limits.CONCLUSIONS The multidimensional evidence-based interventions can achieve a long term control of CLABSI by raising the nurses' compliance to operations.The information monitoring and closed-loop management are crucial to maintenance of the interventional effect,and the risk early warning system should be optimized with the combination of artificial intelligence technology.

Colorectal cancer stands as a leading cause of cancer-related morbidity and mortality globally,with liver metastases being a significant determinant of patient prognosis.Conventional diagnostic methods,includ-ing imaging studies and biomarker testing,frequently exhibit inadequate sensitivity and specificity,underscoring the necessity for more advanced technologies.Recent advancements in genomics,transcriptomics,proteomics,me-tabolomics,and epigenomics have revolutionized our understanding of the biological mechanisms driving colorectal cancer.These methodologies enable comprehensive analyses of genetic mutations,gene expression profiles,protein modifications,and metabolic reprogramming,all of which are pivotal to the metastatic process.This article high-lights the advanced capabilities of artificial intelligence(AI)technologies in processing complex multi-omics data,thereby enhancing diagnostic accuracy and supporting personalized treatment strategies.It also addresses the challenges AI encounters in multi-omics analyses,such as ensuring data quality,improving model interpretability,and facilitating clinical translation.Additionally,it explores the potential integration of emerging technologies like single-cell sequencing and spatial omics into large-scale,multicenter studies to further enhance the clinical utility of these tools.

Objective:To construct a decision tree model to predict the risk of breast cancer in patients with pathological nipple discharge.Methods:A total of 157 patients with pathological nipple discharge,who were diagnosed and treated at Weifang Municipal Hospital of Traditional Chinese Medicine from January 2019 to April 2024 and met the inclusion criteria,were selected.A risk prediction model for concurrent breast cancer in patients with pathological nipple discharge was developed using Logistic regression analysis.A decision tree was then constructed,and the predictive performance of the model was assessed based on the area under the receiver operating characteristic curve(AUC).Re-sults:The incidence of concurrent breast cancer among patients with pathological nipple discharge was 24.2%.Accord-ing to the results of binary Logistic regression analysis,elevated CEA and CA 153 levels in nipple discharge,as well as bloody discharge,emerged as independent risk factors for the development of breast cancer in such patients(P<0.05).Based on these findings,a decision tree model was constructed to predict the risk of concurrent breast cancer in patients with pathological nipple discharge.The validation results showed that the Logistic regression model had an AUC value of 0.800,while the decision tree model achieved an AUC value of 0.889.Conclusions:The decision tree model,built upon the identified influencing factors,exhibits strong predictive power for the risk of developing concurrent breast can-cer in patients with pathological nipple discharge,thus facilitating more precise preoperative diagnoses by clinicians for these patients.

Objective To compare and find an optimal model for predicting the risk of DKD occurrence in patients with type 2 diabetes mellitus(T2DM).Methods A total of 2005 patients with T2DM were enrolled in this study from The Second Hospital of Shijiazhuang City during December 2017 to December 2022.All the subjects were divided into a training set(n=1403)and a validation set(n=602)according to the ratio of 3∶1 by simple random sampling.With the occurrence of DKD as the outcome variablein the training set,important feature variables were screened by LASSO regression.Six different machine learning models were established according to the feature variables,thenthe optimal model was determined by comparison,and anonlinerisk predictor for DKD occurrence was constructed in patients with T2DM.Results Taking the occurrence of DKD as the outcome variable in the training set,the results of LASSO regression analysis showed that the optimal value of the model was 10-fold cross validation lambda.1se=0.01662473,and 15 characteristic variables with nonzero coefficient were screened out to be related to the occurrence of DKD.The data included sex,age,family history of DM,DM duration,LDL-C,HbA1c,WBC,PDW,Scr,urine α1-microglobulin,urine β2-microglobulin,urine microalbumin,hypertension,hypokalemia,and DR.In the training set and validation set,the prediction performance of XGBoost model was better than that of other models(AUC=0.872,0.893,95%CI 0.853～0.891,0.865～0.921),the sensitivity was 0.779,0.863,and the specificity was 0.721,0.758,respectively.The F1 scores were 0.774 and 0.787.DCA analysis showed that the XGBoost model had a greater net benefit and threshold probability.According to the XGBoost model,the online predictor of DKD risk in T2DM patients was laid out,and two patients were selected for application,the results showed that the predictive value of the model was 0.185 in non-DKD patients,and the predictive value was 0.510 in DKD patients.Conclusions The XGBoost model is the best model for predicting the occurrence of DKD in T2DM patients,and an online predictor was successfully built.

Colorectal cancer stands as a leading cause of cancer-related morbidity and mortality globally,with liver metastases being a significant determinant of patient prognosis.Conventional diagnostic methods,includ-ing imaging studies and biomarker testing,frequently exhibit inadequate sensitivity and specificity,underscoring the necessity for more advanced technologies.Recent advancements in genomics,transcriptomics,proteomics,me-tabolomics,and epigenomics have revolutionized our understanding of the biological mechanisms driving colorectal cancer.These methodologies enable comprehensive analyses of genetic mutations,gene expression profiles,protein modifications,and metabolic reprogramming,all of which are pivotal to the metastatic process.This article high-lights the advanced capabilities of artificial intelligence(AI)technologies in processing complex multi-omics data,thereby enhancing diagnostic accuracy and supporting personalized treatment strategies.It also addresses the challenges AI encounters in multi-omics analyses,such as ensuring data quality,improving model interpretability,and facilitating clinical translation.Additionally,it explores the potential integration of emerging technologies like single-cell sequencing and spatial omics into large-scale,multicenter studies to further enhance the clinical utility of these tools.

Objective To compare and find an optimal model for predicting the risk of DKD occurrence in patients with type 2 diabetes mellitus(T2DM).Methods A total of 2005 patients with T2DM were enrolled in this study from The Second Hospital of Shijiazhuang City during December 2017 to December 2022.All the subjects were divided into a training set(n=1403)and a validation set(n=602)according to the ratio of 3∶1 by simple random sampling.With the occurrence of DKD as the outcome variablein the training set,important feature variables were screened by LASSO regression.Six different machine learning models were established according to the feature variables,thenthe optimal model was determined by comparison,and anonlinerisk predictor for DKD occurrence was constructed in patients with T2DM.Results Taking the occurrence of DKD as the outcome variable in the training set,the results of LASSO regression analysis showed that the optimal value of the model was 10-fold cross validation lambda.1se=0.01662473,and 15 characteristic variables with nonzero coefficient were screened out to be related to the occurrence of DKD.The data included sex,age,family history of DM,DM duration,LDL-C,HbA1c,WBC,PDW,Scr,urine α1-microglobulin,urine β2-microglobulin,urine microalbumin,hypertension,hypokalemia,and DR.In the training set and validation set,the prediction performance of XGBoost model was better than that of other models(AUC=0.872,0.893,95%CI 0.853～0.891,0.865～0.921),the sensitivity was 0.779,0.863,and the specificity was 0.721,0.758,respectively.The F1 scores were 0.774 and 0.787.DCA analysis showed that the XGBoost model had a greater net benefit and threshold probability.According to the XGBoost model,the online predictor of DKD risk in T2DM patients was laid out,and two patients were selected for application,the results showed that the predictive value of the model was 0.185 in non-DKD patients,and the predictive value was 0.510 in DKD patients.Conclusions The XGBoost model is the best model for predicting the occurrence of DKD in T2DM patients,and an online predictor was successfully built.

Objective To evaluate the impact of intensified infection control measures on the incidence of health-care-associated infection(HAI)under the background of the reform of diagnosis-intervention packet(DIP)payment,and provide decision-making basis for HAI management under the reform of medical insurance payment.Methods The interrupted time series research design was used to collect the monitoring data of HAI in a tertiary first-class hospital from October 2021 to September 2024.The changing trend of HAI incidence was analyzed by piecewise li-near regression,and the intervention effect was evaluated by standardized infection ratio(SIR).Results From Oc-tober 2021 to September 2022,the incidence of HAI was 2.17％(95％CI:2.08％-2.26％),which dropped to 1.87％(95％CI:1.82％-1.92％)after the intervention.Piecewise linear regression analysis showed that the inci-dence of HAI decreased by 0.324％immediately after the intervention(95％CI:-0.481％--0.167％,P＜0.001),and the trend after the intervention changed significantly compared with that before the intervention(95％CI:-0.033％--0.009％,P=0.001).SIR analysis showed that the actual incidence during the entire intervention period was equivalent to 74.56％of the incidence in intervention period,gradually stabilized from 88.39％-93.81％at the beginning of the intervention to 67.03％-71.22％at the end of the study,and the intervention effect was sustained.Conclusion Intensified infection control measures under the background of the reform of DIP significantly reduce the incidence of HAI and improve the stability of infection control management,which provide new insights into the synergistic improvement of medical insurance payment and HAI management quality.

Objective To investigate the relationship between serum orexin-A(OXA)and aspartate amin-otransferase(AST)levels and the disease severity and prognosis in patients with acute ischemic stroke(AIS).Methods A total of 167 AIS patients(AIS group)treated at Hebei Provincial People's Hospital from January 2021 to January 2024 and 84 healthy individuals undergoing physical examinations(control group)were selected as the research objects.AIS patients were categorized by severity into mild AIS group[National Institutes of Health Stroke Scale(NIHSS)score＜5,42 cases],moderate AIS group(NIHSS score 5—＜16,56 cases),moderate-to-severe AIS group(NIHSS score 16—＜21,36 cases),and severe AIS group(NIHSS score ≥21,33 cases).Based on 3-month prognosis(modified Rankin scale),patients were divided into poor prognosis group(＞2 grade,54 cases)and good prognosis group(≤2 grade,113 cases).Spearman correlation analysis was used to assess the relationship between NIHSS scores and serum OXA and AST levels in AIS pa-tients.Multivariate unconditional Logistic regression was used to determine the relationship between serum OXA and AST levels and the prognosis of AIS patients.Receiver operating characteristic(ROC)curve was used to analyze the predictive efficacy of serum OXA and AST levels for prognosis.Results Compared with the control group,serum OXA level in the AIS group was lower,while AST level was higher(P＜0.05).Ser-um OXA level progressively decreased,and AST level progressively increased across the mild,moderate,mod-erately severe,and severe AIS groups(P＜0.05).NIHSS score was negatively correlated with serum OXA level and positively correlated with AST level in AIS patients(P＜0.05).High OXA level was an independent protective factor for poor prognosis in AIS patients,while high AST level was an independent risk factor(P＜0.05).The area under the curve(AUC)of the combined assessment of serum OXA and AST levels in predic-ting poor prognosis in AIS patients was 0.873,which was greater than the AUC of OXA(0.793)and AST(0.770)alone(P＜0.05).Conclusion In AIS patients,lower serum OXA level and higher AST level are as-sociated with disease severity and poor prognosis.The combined evaluation of serum OXA and AST levels has higher predictive value for AIS prognosis.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.