Systemic lupus erythematosus （SLE）， a refractory autoimmune disease， is among the dominant diseases where traditional Chinese medicine （TCM） shows advantages in the field of rheumatology and immunology. The China-Japan Friendship Hospital hosted the "46^th Youth Salon on Dominant Diseases （Systemic Lupus Erythematosus）" organized by the China Association of Chinese Medicine， which led to a consensus on "the advantages， challenges， interdisciplinary approaches， and translational achievements of integrated TCM and Western medical approaches in the diagnosis and treatment of SLE." The diagnosis and treatment of SLE currently face several challenges， such as frequent misdiagnosis and missed diagnosis in the early stages， difficulty in achieving treatment targets， multiple side effects from pharmacotherapy， and the lack of management strategies for special populations， all of which hinder the fulfillment of the clinical needs of patients. Integrated TCM and Western medical approaches can improve clinical symptoms such as skin erythema， aversion to cold and cold limbs， fatigue， dry mouth， restlessness， and heat sensation in the palms and soles， thereby improving patients' quality of life. The approaches also help consolidate the efficacy of conventional Western medicine， slow disease progression， reduce relapse rates， address multi-organ involvement， and prevent or treat complications. Additionally， they enhance efficacy and reduce toxicity， prevent the side effects of Western medications， help reduce hormone use， and offer distinct advantages in the individualized intervention of special populations， contributing to the whole-process management of the disease. However， evidence-based medical support for this integrated approach remains limited， and the quality of available evidence is generally low. Common evaluation systems and modern research methodologies should be adopted to clarify the efficacy of TCM in SLE treatment. Efforts should be made to carry out high-quality evidence-based medical research， strengthen the development of fundamental and pharmacological research， and further explain the distinct advantages of TCM in the diagnosis and treatment of SLE. Future efforts should focus on advancing the integration of TCM and modern medicine， incorporating multi-omics technologies， individualized stratification， and other precision medicine concepts， in combination with artificial intelligence. Moreover， interdisciplinary collaboration should be promoted to utilize modern technology in exploring the essence of TCM theories and screening effective formulae， thereby comprehensively improving the diagnosis and treatment of SLE through integrated TCM and Western medical approaches.

Systemic lupus erythematosus （SLE）， a refractory autoimmune disease， is among the dominant diseases where traditional Chinese medicine （TCM） shows advantages in the field of rheumatology and immunology. The China-Japan Friendship Hospital hosted the "46^th Youth Salon on Dominant Diseases （Systemic Lupus Erythematosus）" organized by the China Association of Chinese Medicine， which led to a consensus on "the advantages， challenges， interdisciplinary approaches， and translational achievements of integrated TCM and Western medical approaches in the diagnosis and treatment of SLE." The diagnosis and treatment of SLE currently face several challenges， such as frequent misdiagnosis and missed diagnosis in the early stages， difficulty in achieving treatment targets， multiple side effects from pharmacotherapy， and the lack of management strategies for special populations， all of which hinder the fulfillment of the clinical needs of patients. Integrated TCM and Western medical approaches can improve clinical symptoms such as skin erythema， aversion to cold and cold limbs， fatigue， dry mouth， restlessness， and heat sensation in the palms and soles， thereby improving patients' quality of life. The approaches also help consolidate the efficacy of conventional Western medicine， slow disease progression， reduce relapse rates， address multi-organ involvement， and prevent or treat complications. Additionally， they enhance efficacy and reduce toxicity， prevent the side effects of Western medications， help reduce hormone use， and offer distinct advantages in the individualized intervention of special populations， contributing to the whole-process management of the disease. However， evidence-based medical support for this integrated approach remains limited， and the quality of available evidence is generally low. Common evaluation systems and modern research methodologies should be adopted to clarify the efficacy of TCM in SLE treatment. Efforts should be made to carry out high-quality evidence-based medical research， strengthen the development of fundamental and pharmacological research， and further explain the distinct advantages of TCM in the diagnosis and treatment of SLE. Future efforts should focus on advancing the integration of TCM and modern medicine， incorporating multi-omics technologies， individualized stratification， and other precision medicine concepts， in combination with artificial intelligence. Moreover， interdisciplinary collaboration should be promoted to utilize modern technology in exploring the essence of TCM theories and screening effective formulae， thereby comprehensively improving the diagnosis and treatment of SLE through integrated TCM and Western medical approaches.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To investigate the current status and influencing factors for cognitive frailty among the elderly in community, so as to provide the evidence for early identification and prevention of cognitive frailty among the elderly. Methods: Residents aged 60 years and above with local household registration from 11 counties (cities, districts) in Zhejiang Province from 2021 to 2023 were selected as study participants using a multistage random sampling method. Demographic information, lifestyle, and health status were collected through questionnaire surveys. Depressive symptoms were assessed using the Patient Health Questionnaire. Cognitive frailty was evaluated using the FRAIL Scale and the Mini-Mental State Examination. Factors affecting cognitive frailty among the elderly in community were identified using a multivariable logistic regression model. Results: A total of 16 613 individuals were surveyed, including 7 465 males (44.93%) and 9 148 females (55.07%). The average age was (70.97±7.29) years. A total of 784 individuals were detected with depressive symptoms, with a detection rate of 4.72%. A total of 724 individuals were detected with cognitive frailty, with a detection rate of 4.36%. Multivariable logistic regression analysis showed that females (OR=1.419, 95%CI: 1.179-1.708), aged ≥70 years (70-<80 years old, OR=1.869, 95%CI: 1.490-2.345; ≥80 years old, OR=5.017, 95%CI: 3.935-6.398), without a spouse (OR=1.495, 95%CI: 1.234-1.810), sedentary (OR=2.420, 95%CI: 1.829-3.202), chronic diseases (1 type, OR=1.456, 95%CI: 1.175-1.804; ≥2 types, OR=1.639, 95%CI: 1.314-2.045), and depressive symptoms (OR=4.191, 95%CI: 3.361-5.225) were associated with a higher risk of cognitive frailty among the elderly in community. Conversely, a lower risk of cognitive frailty was seen among the elderly in community who had primary school or above (primary school, OR=0.512, 95%CI: 0.389-0.676; junior high school or above, OR=0.464, 95%CI: 0.354-0.608), engaged in physical exercise (OR=0.396, 95%CI: 0.291-0.539), and were reported average or good self-rated health status (average, OR=0.641, 95%CI: 0.475-0.866; good, OR=0.150, 95%CI: 0.109-0.208). Conclusions The detection rate of cognitive frailty among the elderly in community is relatively low and is influenced by demographic factors such as gender, age, education level, as well as lifestyle like sedentary and physical exercise, and health status. It is recommended to reduce the risk of cognitive frailty among the elderly through multidimensional interventions, including health education, promotion of healthy lifestyles, and enhanced mental health support.

Objective: To establish a nomogram prediction model for Alzheimer's disease (AD) among the elderly in community, so as to provide the evidence for early screening and prevention of AD. Methods: Based on the Zhejiang Healthy Aging Cohort Study, the elderly aged 60-90 years who completed the baseline survey were selected as the study subjects. Follow-up surveys were conducted from 2015 to 2016 and from 2019 to 2021. Sociodemographic characteristics, lifestyle factors, medical history, and waist circumference were collected through questionnaire surveys and physical examinations. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and a diagnosis of AD was made based on the Alzheimer's Disease Assessment Scale-Cognitive Subscale and medical history. The participants were randomly divided into training and validation sets at 8∶2 ratio. LASSO regression was used to screen for predictive factors. Multivariable logistic regression model was used to analyze predictive factors and construct a nomogram. The model was analyzed and evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: A total of 6 988 elderly were included at baseline, with a mean age of (68.19±6.63) years. There were 3 438 males (49.20%), and 3 550 females (50.80%). The median follow-up duration was 4.90 (interquartile range, 3.80) years, with 817 new cases of AD were identified, yielding an incidence of 11.69%. LASSO regression and multivariable logistic regression showed that age (OR=1.017, 95%CI: 1.005-1.030), gender (female, OR=1.820, 95%CI: 1.533-2.165), educational level (primary school, OR=0.813, 95%CI: 0.673-0.980), physical exercise (not active, OR=1.572, 95%CI: 1.260-1.980), dining companions (spouse and children, OR=0.771, 95%CI: 0.598-0.995), baseline MMSE score (OR=0.843, 95%CI: 0.821-0.866), and waist circumference (OR=0.981, 95%CI: 0.973-0.989) were risk predictors for AD among the elderly in community. The prediction model demonstrated an area under the ROC curve of 0.740 (95%CI: 0.698-0.783) in the validation set, with a sensitivity of 0.731 and a specificity of 0.667. DCA indicated that when the probability threshold was 0.060 to 0.325, the clinical net benefit was relatively high. Conclusion The AD risk prediction model constructed in this study has good discrimination and clinical practicability, can be used for early screening of AD among the elderly in the community.

Clinicians need to focus on various points in the diagnosis and treatment of insomnia.This article prescribed the treatment protocol based on the unique features,such as insomnia in the elderly,women experiencing specific physiologi-cal periods,children insomnia,insomnia in sleep-breathing disorder patients,insomnia in patients with chronic liver and kidney dysfunction.It pro-vides some reference for clinicians while they make decision on diagnosis,differentiation and treat-ment methods.