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.

Intra-aortic balloon pump(IABP),as the most commonly used percutaneous mechanical circulatory assist device,is routinely implanted through the femoral artery pathway.However,when implantation through the femoral artery pathway is difficult or contraindicated,other pathways including the brachial artery pathway,axillary artery pathway,etc.can be considered.IABP assisted interventional treatment for complex high-risk and indicated patients(CHIP)can reduce the risk of intraoperative complications,stabilize hemodynamics,increase the complete revascularization rate of CHIP,and improve long-term prognosis.The reverse guidewire technique can improve the success rate of percutaneous coronary intervention(PCI)for chronic total occlusion(CTO)of coronary arteries.In clinical practice,the reverse pathway is often chosen in addition to the forward pathway,such as the contralateral radial and brachial artery pathway,femoral artery pathway,etc.This article reports a case of bilateral femoral artery occlusion,in which IABP was implanted through the left brachial artery pathway,and then with the assistance of IABP,the right coronary artery CTO lesion was successfully opened through the right radial artery in the forward direction and the right brachial artery in the reverse direction,with the aim of providing reference for clinical PCI treatment for such special cases.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To explore the safety of establishing the veno-arterial extracorporeal membrane oxygenation(VA-ECMO)pipeline through percutaneous puncture and the effectiveness of high-risk percutaneous coronary intervention(HR-PCI)under VA-ECMO assistance.Methods In this single-center retrospective study,patients who underwent HR-PCI assisted by VA-ECMO at Wuhan Asian Heart Hospital from December 2022 to December 2023 were included.The primary endpoint was the safety of establishing the VA-ECMO pipeline through percutaneous puncture,mainly characterized by extracorporeal membrane oxygenation(ECMO)-related complications,including severe bleeding/hematoma at the wound,arteriovenous fistula,pseudoaneurysm,peripheral arterial dissection,etc.The secondary endpoint was the effectiveness of HR-PCI under VA-ECMO assistance,defined as major adverse cardiovascular and cerebrovascular events(MACCE)at 6-month follow-up,including the composite events of all-cause death,non-fatal myocardial infarction,repeat revascularization,and ischemic stroke.Results Among the 189 patients included in the study,15(7.9％)had severe bleeding/hematoma at the puncture wound caused by VA-ECMO,8(4.2％)had wound infection,1(0.5％)had arteriovenous fistula,4(2.1％)had pseudoaneurysm,3(1.6％)had peripheral arterial dissection,8(4.2％)had systemic embolism,8(4.2％)had acute kidney injury,3(1.6％)had acute neurological injury,and 1(0.5％)died.percutaneous coronary intervention(PCI)was successful in 182 cases(96.3％),and a total of 36 cases(19.0％)had PCI-related complications.164 patients completed 6-month follow-up,among which 4(2.4％)had all-cause death,11(6.7％)had non-fatal myocardial infarction,11(6.7％)had unplanned repeat revascularization,and 2(1.2％)had the composite event of ischemic stroke.Conclusions Ultrasound-guided percutaneous puncture for establishing the VA-ECMO pipeline has high safety,and HR-PCI patients assisted by VA-ECMO have good short-term effects.

Objective:To analysis the influenced factors of readmission in hospital among patients with mood disorder,construct predictive models and compare the predictive performance of the models.Methods:The electron-ic medical record data of patients with mood disorder admitted to Beijing Anding Hospital from January 2010 to De-cember 2018 were retrospectively collected.Utilizing stepwise logistic regression to analyze the related factors of re-admission in patients with mood disorder.Logistic regression,support vector machine,random forest,extreme gradi-ent boosting(XGBoost)algorithms and convolutional neural networks(CNN)were used to construct a readmission prediction model and compare the predictive performances of the different models by using the accuracy,precision,recall,et.al.Results:A total of 6 234 patients with mood disorder were enrolled,24.9％(n=1 549)patients were readmitted after discharge.The stepwise logistic regression results revealed that readmissions were more likely to occur in patients with mood disorder who were female,had comorbidities,had a treatment outcome of cure,were treated with MECT and were on second-generation antipsychotics(OR=1.26,1.68,1.26,1.35,1.18).The CNN model demonstrated the highest accuracy,precision,and F1 scores,all at 0.87,with a recall of 0.86.The random forest achieved a recall of 0.86 and an AUC of 0.95.Conclusion:Readmission of patients with mood disorder is influenced by a multitude of factors.The convolutional neural networks and random forest models outperform other models in prediction.

Objective To explore the safety of establishing the veno-arterial extracorporeal membrane oxygenation(VA-ECMO)pipeline through percutaneous puncture and the effectiveness of high-risk percutaneous coronary intervention(HR-PCI)under VA-ECMO assistance.Methods In this single-center retrospective study,patients who underwent HR-PCI assisted by VA-ECMO at Wuhan Asian Heart Hospital from December 2022 to December 2023 were included.The primary endpoint was the safety of establishing the VA-ECMO pipeline through percutaneous puncture,mainly characterized by extracorporeal membrane oxygenation(ECMO)-related complications,including severe bleeding/hematoma at the wound,arteriovenous fistula,pseudoaneurysm,peripheral arterial dissection,etc.The secondary endpoint was the effectiveness of HR-PCI under VA-ECMO assistance,defined as major adverse cardiovascular and cerebrovascular events(MACCE)at 6-month follow-up,including the composite events of all-cause death,non-fatal myocardial infarction,repeat revascularization,and ischemic stroke.Results Among the 189 patients included in the study,15(7.9％)had severe bleeding/hematoma at the puncture wound caused by VA-ECMO,8(4.2％)had wound infection,1(0.5％)had arteriovenous fistula,4(2.1％)had pseudoaneurysm,3(1.6％)had peripheral arterial dissection,8(4.2％)had systemic embolism,8(4.2％)had acute kidney injury,3(1.6％)had acute neurological injury,and 1(0.5％)died.percutaneous coronary intervention(PCI)was successful in 182 cases(96.3％),and a total of 36 cases(19.0％)had PCI-related complications.164 patients completed 6-month follow-up,among which 4(2.4％)had all-cause death,11(6.7％)had non-fatal myocardial infarction,11(6.7％)had unplanned repeat revascularization,and 2(1.2％)had the composite event of ischemic stroke.Conclusions Ultrasound-guided percutaneous puncture for establishing the VA-ECMO pipeline has high safety,and HR-PCI patients assisted by VA-ECMO have good short-term effects.

Objective:To analysis the influenced factors of readmission in hospital among patients with mood disorder,construct predictive models and compare the predictive performance of the models.Methods:The electron-ic medical record data of patients with mood disorder admitted to Beijing Anding Hospital from January 2010 to De-cember 2018 were retrospectively collected.Utilizing stepwise logistic regression to analyze the related factors of re-admission in patients with mood disorder.Logistic regression,support vector machine,random forest,extreme gradi-ent boosting(XGBoost)algorithms and convolutional neural networks(CNN)were used to construct a readmission prediction model and compare the predictive performances of the different models by using the accuracy,precision,recall,et.al.Results:A total of 6 234 patients with mood disorder were enrolled,24.9％(n=1 549)patients were readmitted after discharge.The stepwise logistic regression results revealed that readmissions were more likely to occur in patients with mood disorder who were female,had comorbidities,had a treatment outcome of cure,were treated with MECT and were on second-generation antipsychotics(OR=1.26,1.68,1.26,1.35,1.18).The CNN model demonstrated the highest accuracy,precision,and F1 scores,all at 0.87,with a recall of 0.86.The random forest achieved a recall of 0.86 and an AUC of 0.95.Conclusion:Readmission of patients with mood disorder is influenced by a multitude of factors.The convolutional neural networks and random forest models outperform other models in prediction.

Objective To develop and validate a nomogram model that integrates systemic inflammatory markers to predict the likelihood of pelvic lymph node metastasis(PLNM)in prostate cancer patients prior to surgery.Methods This study retrospectively analyzed the clinical data and preoperative inflammatory markers—including neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR),systemic immune-inflammation index(SII),and monocyte-to-lymphocyte ratio(MLR)—of patients diagnosed with prostate cancer.Univariate and multi-variate logistic regression analyses were conducted to identify markers that were significantly associated with PLNM.Based on the results of the multivariate analysis,a nomogram was developed and its predictive accuracy was assessed using receiver operating characteristic curves(ROC)and calibration plots.Results Among the 334 enrolled patients with prostate cancer,107 were identified with PLNM.Univariate analysis revealed statistically significant differences in free prostate-specific antigen(fPSA),Gleason score,NLR,PLR,MLR,and SII between the PLNM and non-pelvic lymph node metastasis(NPLNM)groups(P<0.05).Multivariate analysis confirmed that fPSA,Gleason score,and SII were independent predictors of PLNM(P<0.05).A nomogram incorporating these predic-tors exhibited strong discriminative ability,with an area under the ROC curve(AUC)of 0.79(95%CI:0.73～0.84).Calibration analysis further demonstrated good consistency between the predicted and observed probabilities of PLNM.Conclusions This study successfully developed a nomogram model based on systemic inflammatory markers for preoperative prediction of pelvic lymph node metastasis in prostate cancer.Owing to its user-friendly design and high predictive accuracy,this tool may serve as a valuable complementary method to conventional imaging techniques,thereby supporting personalized treatment decision-making.

Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

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.