With the accelerating global trend of population aging, health issues among the elderly have garnered increasing attention. Cognitive frailty and depressive symptoms, are common health concerns in this population, significantly impacting mental health and quality of life, and are closely linked to a range of adverse health outcomes. This article provides an updated review of recent advances in research on depressive symptoms in the elderly with cognitive frailty, focusing on clinical epidemiology, shared pathophysiological mechanisms, and key influencing factors. The aim is to offer robust theoretical foundations and practical guidance for the optimization of elderly health services and policy development, and ultimately achieve the goals of healthy aging and improvement of quality of life for the elderly.

Concepts are the cornerstone of the development of disciplines. The concepts of TCM present that a superior concept contains more subordinate concepts. The superordinate concepts are often used to refer to different subordinate concepts, which can refer to both superior concepts themselves and non-specific subordinate concepts, that is, the characteristics of subordinate coverage and indefinite reference, which cause confusion in concept meaning, concept relationships, reasoning logic, and other problems. Nowadays, the TCM scholars pay little attention to this characteristic. Therefore, this article analyzed this characteristic, discussed its impact on the inheritance and development of TCM, and proposed that starting from the anchoring of concepts and entities to clarify the connotation of concepts, looking forward to provide new ideas for the definition of the concepts of TCM and the development of the discipline.

Background::B-cell maturation antigen (BCMA)-directed chimeric antigen receptor T (CAR-T) therapy yield remarkable responses in patients with relapsed/refractory multiple myeloma (R/RMM). Circulating tumor DNA (ctDNA) reportedly exhibits distinct advantages in addressing the challenges posed by tumor heterogeneity in the distribution and genetic variations in R/RMM.Methods::Herein, the ctDNA of 108 peripheral blood plasma samples from patients with R/RMM at the First Affiliated Hospital, School of Medicine, Zhejiang University was thoroughly investigated before administration of anti-BCMA CAR-T therapy to establish its predictive potential. Flow cytometry is used primarily to detect subgroups of T cells or CAR-T cells.Results::In this study, several tumor and T cell effector-mediated factors were considered to be related to treatment failure by an integrat analysis, including higher percentages of multiple myeloma (MM) cells in the bone marrow ( P = 0.0125), lower percentages of CAR-T cells in the peripheral blood at peak ( P = 0.0375), and higher percentages of CD8 + T cells ( P = 0.0340). Furthermore, there is a substantial correlation between high ctDNA level (>143 ng/mL) and shorter progression-free survival (PFS) ( P = 0.007). Multivariate Cox regression analysis showed that high levels of ctDNA (>143 ng/mL), MM-driven high-risk mutations (including IGLL5 [ P = 0.004], IRF4 [ P = 0.024], and CREBBP [ P = 0.041]), number of multisite mutations, and resistance-related mutation ( ERBB4, P = 0.040) were independent risk factors for PFS. Conclusion::Finally, a ctDNA-based risk model was built based on the above independent risk factors, which serves as an adjunct non-invasive measure of substantial tumor burden and a prognostic genetic feature that can assist in predicting the response to anti-BCMA CAR-T therapy.

The wear debris generated during artificial joint prosthesis service can react with bone tissues to form osteolysis,seriously affecting the life-time of artificial joint prostheses.This paper reviews,summarizes,and analyzes domestic and international research literature on the extraction,characterization,and identification of wear debris from different artificial joint materials,aiming to provide references and feasible ideas for the future construction of a systematic and hierarchical research system for artificial joint wear debris.The main findings are as follows:strong alkali protein degradation test,strong acid protein degradation test,and protease protein degradation test are the commonly used method for extracting artificial joint wear debris,and researchers have clarified the protein degradation mechanisms of these three debris extraction methods.The characterization of wear debris in-vitro and in-vivo is mostly for hip and knee joints,with a small amount involving cervical spine and ankle joints.Studies have shown that the size,quantity,shape,and volume of wear particles are influenced by factors such as joint type,contact area,material selection,and implantation time.Both domestic and international studies have conducted characterization research on wear debris after in-vitro simulation testing,but there is still a lack of wear debris characterization analysis of clinical retrievals in China.Currently,most research is on the recognition of wear debris in the traditional mechanical field,but research on the intelligent recognition of artificial joint wear debris is relatively few,indicating that there is a certain lag in the application of computer technology in the field of artificial joint wear debris recognition.

Objective:To develop an economical,simple,and accessible method for early identification of high-risk pregnant women with gestational diabetes mellitus(GDM),this study developed and evaluated multiple machine learning models,identified the optimal prediction model,and constructed a clinical decision support sys-tem(CDSS)based on this model.Methods:A total of 464 pregnant women who visited the Second Affiliated Hospital of Dalian Medical University from January 1,2023 to December 30,2024 were included,of which 386 were used to establish a prediction model(231 in the training set and 155 in the testing set),and the remaining 78 were used as a validation.Adopting the methods of double-point sequence correlation and chi-square test,four machine learning models were constructed after selecting feature variables:Logistic Regression,Random Forest,Support Vector Machine,and eXtreme Gradient Boosting(XGBoost).Preliminary judgment of the maximum weight mod-el,further comparison of the discriminative ability,calibration ability,and clinical practicality of each model to evalu-ate and select the optimal model,develop its CDSS,and verify the accuracy of the model.Results:①Correlation analysis identified predictors of GDM:age,pre-pregnancy body mass index(BMI),systolic/diastolic blood pres-sure,white blood cell count,hemoglobin,lymphocyte ratio,fasting plasma glucose,uric acid,direct bilirubin,chronic hypertension complicating pregnancy,and assisted reproductive technology conception.②XGBoost dominated the ensemble model and demonstrated the best performance in discrimination(AUC 0.931,95%CI 0.910-0.967),cali-bration,and clinical utility among the four models.③The CDSS achieved an accuracy of 78.2%,sensitivity of 64.7%,and specificity of 82.0%in the XGBoost model.Conclusions:The XGBoost model has the highest ability to predict GDM in the early stage.Developing its CDSS not only facilitates doctors to quickly assess GDM risk,but also is suitable for promotion to remote areas,where high-risk population screening can be achieved through re-mote data.

Objective To investigate the status and influencing factors of psychological distress associated with weight loss among young and middle-aged adults with obesity,so as to provide a theoretical basis for developing personalized interventions and improving psychological well-being.Methods A convenience sampling method was used to select young and middle-aged obese patients who visited the weight management clinic of a Grade A tertiary hospital in Jiangxi Province from October 2024 to May 2025.Data were collected using a general information questionnaire,the distress thermometer,the perceived social support scale,the brief illness perception questionnaire,and the simplified coping style questionnaire.Binary logistic regression was applied to identify factors influencing psychological distress.Results A total of 204 valid questionnaires were collected.The detection rate of significant psychological distress was 58.82%(120 cases).Regarding weight loss methods,32.84%of participants opted for medication.The top five issues reported on the psychological distress problem list were:appearance/body image,work/studies,lack of time/energy to care for elderly parents or children,bathing/dressing,and relationship with a partner.Binary logistic regression indicated that age,body mass index,waist circumference,history of chronic disease,perceived social support,illness perception,and negative coping style were significant influencing factors of psychological distress(P﹤0.05).Conclusions The rate of significant psychological distress is relatively high among young and middle-aged obese patients and is influenced by multiple factors.Medical staff may develop personalized interventions based on these factors to reduce the incidence of psychological distress.

Objective To investigate the status and influencing factors of psychological distress associated with weight loss among young and middle-aged adults with obesity,so as to provide a theoretical basis for developing personalized interventions and improving psychological well-being.Methods A convenience sampling method was used to select young and middle-aged obese patients who visited the weight management clinic of a Grade A tertiary hospital in Jiangxi Province from October 2024 to May 2025.Data were collected using a general information questionnaire,the distress thermometer,the perceived social support scale,the brief illness perception questionnaire,and the simplified coping style questionnaire.Binary logistic regression was applied to identify factors influencing psychological distress.Results A total of 204 valid questionnaires were collected.The detection rate of significant psychological distress was 58.82%(120 cases).Regarding weight loss methods,32.84%of participants opted for medication.The top five issues reported on the psychological distress problem list were:appearance/body image,work/studies,lack of time/energy to care for elderly parents or children,bathing/dressing,and relationship with a partner.Binary logistic regression indicated that age,body mass index,waist circumference,history of chronic disease,perceived social support,illness perception,and negative coping style were significant influencing factors of psychological distress(P﹤0.05).Conclusions The rate of significant psychological distress is relatively high among young and middle-aged obese patients and is influenced by multiple factors.Medical staff may develop personalized interventions based on these factors to reduce the incidence of psychological distress.

Objective:To develop an economical,simple,and accessible method for early identification of high-risk pregnant women with gestational diabetes mellitus(GDM),this study developed and evaluated multiple machine learning models,identified the optimal prediction model,and constructed a clinical decision support sys-tem(CDSS)based on this model.Methods:A total of 464 pregnant women who visited the Second Affiliated Hospital of Dalian Medical University from January 1,2023 to December 30,2024 were included,of which 386 were used to establish a prediction model(231 in the training set and 155 in the testing set),and the remaining 78 were used as a validation.Adopting the methods of double-point sequence correlation and chi-square test,four machine learning models were constructed after selecting feature variables:Logistic Regression,Random Forest,Support Vector Machine,and eXtreme Gradient Boosting(XGBoost).Preliminary judgment of the maximum weight mod-el,further comparison of the discriminative ability,calibration ability,and clinical practicality of each model to evalu-ate and select the optimal model,develop its CDSS,and verify the accuracy of the model.Results:①Correlation analysis identified predictors of GDM:age,pre-pregnancy body mass index(BMI),systolic/diastolic blood pres-sure,white blood cell count,hemoglobin,lymphocyte ratio,fasting plasma glucose,uric acid,direct bilirubin,chronic hypertension complicating pregnancy,and assisted reproductive technology conception.②XGBoost dominated the ensemble model and demonstrated the best performance in discrimination(AUC 0.931,95%CI 0.910-0.967),cali-bration,and clinical utility among the four models.③The CDSS achieved an accuracy of 78.2%,sensitivity of 64.7%,and specificity of 82.0%in the XGBoost model.Conclusions:The XGBoost model has the highest ability to predict GDM in the early stage.Developing its CDSS not only facilitates doctors to quickly assess GDM risk,but also is suitable for promotion to remote areas,where high-risk population screening can be achieved through re-mote data.

The wear debris generated during artificial joint prosthesis service can react with bone tissues to form osteolysis,seriously affecting the life-time of artificial joint prostheses.This paper reviews,summarizes,and analyzes domestic and international research literature on the extraction,characterization,and identification of wear debris from different artificial joint materials,aiming to provide references and feasible ideas for the future construction of a systematic and hierarchical research system for artificial joint wear debris.The main findings are as follows:strong alkali protein degradation test,strong acid protein degradation test,and protease protein degradation test are the commonly used method for extracting artificial joint wear debris,and researchers have clarified the protein degradation mechanisms of these three debris extraction methods.The characterization of wear debris in-vitro and in-vivo is mostly for hip and knee joints,with a small amount involving cervical spine and ankle joints.Studies have shown that the size,quantity,shape,and volume of wear particles are influenced by factors such as joint type,contact area,material selection,and implantation time.Both domestic and international studies have conducted characterization research on wear debris after in-vitro simulation testing,but there is still a lack of wear debris characterization analysis of clinical retrievals in China.Currently,most research is on the recognition of wear debris in the traditional mechanical field,but research on the intelligent recognition of artificial joint wear debris is relatively few,indicating that there is a certain lag in the application of computer technology in the field of artificial joint wear debris recognition.

Objective To address the quality problems caused by high porosity in the preparation of dental cobalt-chrome alloy prosthetics based on selective laser melting(SLM)technology,we investigated the influence mechanism of different forming process parameters on the microstructure and properties of the materials.Moreover,the range of form-ing process parameters that can effectively reduce defects was precisely defined.Methods The effects of laser power,scanning speed,and scanning distance on the pore properties,surface roughness,and hardness of dental cobalt-chrome al-loy were investigated by adjusting the printing parame-ters in the process of SLM.Through metallographic anal-ysis,image analysis,and molten pool simulation,the pore formation mechanism was revealed,and the relation-ship between the porosity and energy density of SLM dental cobalt-chrome alloy was elucidated.Results When the linear energy density was higher than 0.18 J/mm,the po-rosity defect easily appeared at the bottom of the molten pool.When the laser energy density was lower than 0.13 J/mm,defects occurred in the gap of the molten pool due to insufficient melting of powder.In particular,when the linear energy density exceeded the threshold of 0.30 J/mm or was below 0.12 J/mm,the porosity increased significantly to more than 1%.In addition,we observed a negative correlation between free surface roughness and energy density and an inverse re-lationship between macroscopic hardness and porosity.Conclusion On the basis of the conditions of raw materials and molding equipment used in this study,the key process parameters of SLM of molding parts with porosity lower than 1%were successfully determined.Specifically,these key parameters included the line energy density,which ranged from 0.13 J/mm to 0.30 J/mm,and the scan spacing should be strictly controlled below 90 μm.