Objective:To establish a prognostic model based on endoplasmic reticulum stress-related genes for evaluating the prognosis of patients with renal cell carcinoma.Methods:This study utilized Non-negative Matrix Factorization to identify molecular subgroups based on endoplasmic reticulum stress-related genes and employed Weighted Correlation Network Analysis to determine co-expressed genes associ-ated with these subgroups.A risk prognostic model was constructed using univariate Cox regression analysis and Lasso regression analysis.Preliminary experimental validations were conducted to elucidate the biological functions of model genes in renal cell carcinoma.Results:Two molecular subgroups with distinct survival prognoses were identified,and an intersection of related genes was used to construct a nov-el endoplasmic reticulum stress-related prognostic model.Patients in the high-risk group exhibited significantly poorer overall survival in both the training and validation cohorts.In vivo experiments demonstrated that PCK1,a model gene,could inhibit the proliferation,migra-tion,and invasion of renal cell carcinoma cells.Conclusions:The risk scoring model developed in this study effectively predicts the survival probability of renal cell carcinoma patients and can serve as an independent prognostic indicator.This model offers a new direction for per-sonalized treatment strategies in renal cell carcinoma patients.

[摘 要] 目的：探讨白蛋白结合型紫杉醇联合PD-1抑制剂用于治疗一线化疗失败的骨与软组织肉瘤的疗效及安全性。方法：回顾性分析北京积水潭医院骨肿瘤科2017年8月至2020年8月收治的一线化疗失败的晚期骨与软组织肉瘤患者。患者接受白蛋白结合型紫杉醇（125~140 mg/m2，第1天和第8天）与PD-1抑制剂（信迪利单抗或特瑞普利单抗，每21 d一次）联合治疗。每2个治疗周期评估1次疗效，按RECIST 1.1标准评估肿瘤疗效，按NCI-CTCAE5.0标准评估不良反应。结果：共20名患者纳入研究，完成1至8个治疗周期，中位治疗周期数为3个。所有患者均可评估疗效，完全缓解4例（20%），部分缓解0例，稳定9例（45%），疾病进展7例（35%）。客观缓解率（ORR）为20%，疾病控制率（DCR）为65%。中位无进展生存期（PFS）为3.0个月。治疗期间主要不良反应包括2级白细胞减少（40%）、1-2级神经毒性反应（20%），以及2级甲状腺功能减退（10%）。结论：白蛋白结合型紫杉醇联合PD-1抑制剂治疗为一线化疗失败的晚期骨与软组织肉瘤患者提供了一种潜在的治疗选择，其不良反应可控，值得开展更大样本的前瞻性研究进一步验证其疗效。

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.

Liver diseases have a high prevalence rate worldwide with relatively poor long-term clinical outcomes and have become one of the leading causes of disease burden and death around the world,which poses significant challenges to public health.Eosinophils(Eos)are a class of highly conserved multifunctional immune cells that play critical effector roles in allergic diseases.In recent years,an increasing amount of evidence has shown that Eos plays an important role in the pathogenesis of liver diseases,exerting a protective or harmful effect in different liver diseases,which has become a research hotspot in this field.This article elaborates on the role and potential mechanism of action of Eos in liver diseases,in order to provide a new perspective for in-depth research on the pathogenesis of liver diseases and lay the foundation for developing therapeutic strategies targeting Eos.

Objective To discuss the risk factors for impaired fasting glucose(IFG)and construct and validate a predictive model based on column charts of the risk of IFG occurrence.Methods This retrospective study included 3 037 individuals who underwent routine physical examinations at a hospital in Shenyang between August and December 2022.The population was randomly divided into a training group(n=2 126)and a validation group(n=911)in a 7∶3 ratio,and physical examination data were collected.LASSO regression analy-sis was used to screen predictive variables and logistic regression analysis was used to further screen and construct a column chart pre-dictive model.The validation group was used to conduct an internal validation of the feasibility of the model,and the area under the curve(AUC)of receiver operator characteristic(ROC)and goodness of fit tests were used to evaluate the model effectiveness.Results Among the 3 037 included individuals,2 880 did not experience IFG and 157 did.The results showed that age(OR=1.04,95％CI:1.02-1.05),body mass index(BMI,OR=1.10,95％CI:1.05-1.17),systolic blood pressure(SBP,OR=1.01,95％CI:1.00-1.03),triglycerides(TG,OR=1.20,95％CI:0.99-1.51),and a history of hypertension(OR=1.28,95％CI:1.04-1.59)were independent risk factors for IFG occurrence in this population.Based on these variables,a column chart prediction model was constructed.In the training group,the model predicted an AUC of 0.722(95％CI:0.68-0.77)for IFG occurrence,while in the validation group,it predicted an AUC of 0.907(95％CI:0.87-0.94)for IFG occurrence.The results of the Hosmer-Lemeshow goodness of fit test showed that the models of the training and validation groups were not significantly different(P＞0.05);that is,the actual probability was consistent with the prediction probability of the model,and the models calibration was good.Conclusion A risk prediction model for IFG occurrence that included five variables:age,BMI,SBP,TG,and history of hypertension could be construted.This model might help to identify high-risk groups for IFG early and allow for inter-vention in a timely manner.

Liver diseases have a high prevalence rate worldwide with relatively poor long-term clinical outcomes and have become one of the leading causes of disease burden and death around the world,which poses significant challenges to public health.Eosinophils(Eos)are a class of highly conserved multifunctional immune cells that play critical effector roles in allergic diseases.In recent years,an increasing amount of evidence has shown that Eos plays an important role in the pathogenesis of liver diseases,exerting a protective or harmful effect in different liver diseases,which has become a research hotspot in this field.This article elaborates on the role and potential mechanism of action of Eos in liver diseases,in order to provide a new perspective for in-depth research on the pathogenesis of liver diseases and lay the foundation for developing therapeutic strategies targeting Eos.

Objective:To establish a prognostic model based on endoplasmic reticulum stress-related genes for evaluating the prognosis of patients with renal cell carcinoma.Methods:This study utilized Non-negative Matrix Factorization to identify molecular subgroups based on endoplasmic reticulum stress-related genes and employed Weighted Correlation Network Analysis to determine co-expressed genes associ-ated with these subgroups.A risk prognostic model was constructed using univariate Cox regression analysis and Lasso regression analysis.Preliminary experimental validations were conducted to elucidate the biological functions of model genes in renal cell carcinoma.Results:Two molecular subgroups with distinct survival prognoses were identified,and an intersection of related genes was used to construct a nov-el endoplasmic reticulum stress-related prognostic model.Patients in the high-risk group exhibited significantly poorer overall survival in both the training and validation cohorts.In vivo experiments demonstrated that PCK1,a model gene,could inhibit the proliferation,migra-tion,and invasion of renal cell carcinoma cells.Conclusions:The risk scoring model developed in this study effectively predicts the survival probability of renal cell carcinoma patients and can serve as an independent prognostic indicator.This model offers a new direction for per-sonalized treatment strategies in renal cell carcinoma patients.

Objective To discuss the risk factors for impaired fasting glucose(IFG)and construct and validate a predictive model based on column charts of the risk of IFG occurrence.Methods This retrospective study included 3 037 individuals who underwent routine physical examinations at a hospital in Shenyang between August and December 2022.The population was randomly divided into a training group(n=2 126)and a validation group(n=911)in a 7∶3 ratio,and physical examination data were collected.LASSO regression analy-sis was used to screen predictive variables and logistic regression analysis was used to further screen and construct a column chart pre-dictive model.The validation group was used to conduct an internal validation of the feasibility of the model,and the area under the curve(AUC)of receiver operator characteristic(ROC)and goodness of fit tests were used to evaluate the model effectiveness.Results Among the 3 037 included individuals,2 880 did not experience IFG and 157 did.The results showed that age(OR=1.04,95％CI:1.02-1.05),body mass index(BMI,OR=1.10,95％CI:1.05-1.17),systolic blood pressure(SBP,OR=1.01,95％CI:1.00-1.03),triglycerides(TG,OR=1.20,95％CI:0.99-1.51),and a history of hypertension(OR=1.28,95％CI:1.04-1.59)were independent risk factors for IFG occurrence in this population.Based on these variables,a column chart prediction model was constructed.In the training group,the model predicted an AUC of 0.722(95％CI:0.68-0.77)for IFG occurrence,while in the validation group,it predicted an AUC of 0.907(95％CI:0.87-0.94)for IFG occurrence.The results of the Hosmer-Lemeshow goodness of fit test showed that the models of the training and validation groups were not significantly different(P＞0.05);that is,the actual probability was consistent with the prediction probability of the model,and the models calibration was good.Conclusion A risk prediction model for IFG occurrence that included five variables:age,BMI,SBP,TG,and history of hypertension could be construted.This model might help to identify high-risk groups for IFG early and allow for inter-vention in a timely manner.

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     To construct a radiomics model for identifying clinical high-risk carotid plaques. Methods     A retrospective analysis was conducted on patients with carotid artery stenosis in China-Japan Friendship Hospital from December 2016 to June 2022. The patients were classified as a clinical high-risk carotid plaque group and a clinical low-risk carotid plaque group according to the occurrence of stroke, transient ischemic attack and other cerebrovascular clinical symptoms within six months. Six machine learning models including eXtreme Gradient Boosting, support vector machine, Gaussian Naive Bayesian, logical regression, K-nearest neighbors and artificial neural network were established. We also constructed a joint predictive model combined with logistic regression analysis of clinical risk factors. Results    Finally 652 patients were collected, including 427 males and 225 females, with an average age of 68.2 years. The results showed that the prediction ability of eXtreme Gradient Boosting was the best among the six machine learning models, and the area under the curve (AUC) in validation dataset was 0.751. At the same time, the AUC of eXtreme Gradient Boosting joint prediction model established by clinical data and carotid artery imaging data validation dataset was 0.823. Conclusion     Radiomics features combined with clinical feature model can effectively identify clinical high-risk carotid plaques.