To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

Objective:To analyze the changes in the prevalence characteristics of multimorbidity among adolescents aged 13-18 in Inner Mongolia Autonomous Region from 2019 to 2022 and to explore the association between multimorbidity and moderate to high-intensity physical activity among them.Methods:A stratified random cluster sampling method was used to select students aged 13-18 in Inner Mongolia Autonomous Region every September from 2019 to 2022. Physical examinations, demographic characteristics, and depression-related surveys were conducted to analyze the multimorbidity of overweight, obesity, high blood pressure, myopia, spinal curvature abnormality, and depression. A logistic regression model was used to analyze the association between multimorbidity and moderate to high-intensity physical activity.Results:From 2019 to 2022, 70 972, 62 923, 80 254, and 78 288 study subjects were included, with the rates of multimorbidity being 56.4%, 55.4%, 57.2%, and 55.8%, respectively. The rates of multimorbidity remained relatively stable from 2019 to 2022 ( χ2=0.06, P=0.950). The incidence of multimorbidity among girls was significantly higher than that among boys ( P<0.001). The incidence of multimorbidity among urban students was significantly higher than that among suburban students ( P<0.001). The incidence of multimorbidity among high school students was higher than that among middle school students ( P<0.001). The top three multimorbidity combinations were myopia and overweight/obesity (26.4%), myopia and high blood pressure (24.4%), and myopia and depression (19.8%), while the least common combination was depression and spinal curvature abnormality (1.1%). The multimorbidity patterns showed no significant differences between years ( χ2=0.03, P=0.999). The multimorbidity status was significantly associated with the status of meeting the standard of moderate to high-intensity physical activity ( OR=0.83, 95% CI: 0.80-0.86). The association was stronger in boys ( OR=0.77, 95% CI: 0.73-0.81) compared with girls ( OR=0.90, 95% CI: 0.85-0.96), with a significant interaction term ( P<0.001). Conclusion:From 2019 to 2022, the incidence of multimorbidity among adolescents aged 13 to 18 in Inner Mongolia Autonomous Region is relatively high, mainly due to the co-occurrence of myopia and other health problems. Adequate physical activity is an important factor in reducing multimorbidity.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Exosomal circular RNA (circRNAs) are pivotal in cancer biology, and tumor pathophysiology. These stable, non-coding RNAs encapsulated in exosomes participated in cancer progression, tumor growth, metastasis, drug sensitivity and the tumor microenvironment (TME). Their presence in bodily fluids positions them as potential non-invasive biomarkers, revealing the molecular dynamics of cancers. Research in exosomal circRNAs is reshaping our understanding of neoplastic intercellular communication. Exploiting the natural properties of exosomes for targeted drug delivery and disrupting circRNA-mediated pro-tumorigenic signaling can develop new treatment modalities. Therefore, ongoing exploration of exosomal circRNAs in cancer research is poised to revolutionize clinical management of cancer. This emerging field offers hope for significant breakthroughs in cancer care. This review underscores the critical role of exosomal circRNAs in cancer biology and drug resistance, highlighting their potential as non-invasive biomarkers and therapeutic targets that could transform the clinical management of cancer.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Heart failure is a complex clinical syndrome and pediatric heart failure (PHF) has a high mortality rate. Early diagnosis is crucial for treatment and management of PHF. In clinical practice, various tests and examinations play a key role in the diagnosis of PHF, including continuously updated biomarkers, echocardiography, and cardiac magnetic resonance imaging. This article focuses on summarizing relevant research on biomarkers, examinations, combined testing, clinical models, and the grading and staging of PHF diagnosis, aiming to provide insights and directions for the diagnosis of PHF.
Humans ; Heart Failure/diagnosis* ; Child ; Biomarkers/blood* ; Echocardiography ; Magnetic Resonance Imaging

The patient, an 11-year-old girl, was admitted with recurrent fever for 20 days, worsening with abdominal distension for 7 days. Upon admission, she presented with recurrent fever, lymphadenopathy, hepatosplenomegaly, polyserositis, and multiple organ dysfunction. Lymph node pathology and clinical manifestations confirmed the diagnosis of idiopathic multicentric Castleman disease-TAFRO syndrome. Treatment with siltuximab combined with glucocorticoids was initiated, followed by maintenance therapy with tocilizumab. The patient is currently in complete clinical remission. Therefore, once a child is diagnosed with idiopathic multicentric Castleman disease -TAFRO syndrome, early use of siltuximab should be considered for rapid disease control, followed by tocilizumab for maintenance therapy.
Humans ; Castleman Disease/drug therapy* ; Child ; Antibodies, Monoclonal, Humanized/administration & dosage* ; Female ; Antibodies, Monoclonal/administration & dosage*