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: 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*

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.

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 To investigate the effect of total paeony glycoside(TPG)on airway remodeling in bronchial asthma mice and its underlying mechanisms.Methods Forty-eight BALB/c mice were randomly divided into control group,model group,ovalbumin+budesonide group(OVA+BUD group),and OVA+TPG group,with 12 mice in each group.Except the control group,mice in other groups were sensitized by intraperitoneal injection of 10%OVA aluminum hydroxide suspension,and then stimulated by atomized inhalation of 1%OVA to establish mouse asthma model.One hour before each inhalation of OVA,mice in OVA+BUD group were atomized with 2 ml BUD suspension,and mice in OVA+TPG group were given 5 g/kg TPG by intragastric administration.Lung tissues and bronchoalveolar lavage fluid(BALF)of mice from each group were collected,and the pathological morphology of the lung tissues was detected by hematoxylin-eosin(HE)and periodic acid schiff(PAS)staining.Inflammatory cell counts[white blood cell(WBC),neutrophil(NEU),eosinophils(EOS),and leukomonocyte(LYM)]in BALF were detected by Wright-giemsa staining.The contents of inflammatory factors including tumor necrosis factor-α(TNF-α),interleukin-1β(IL-1β)and IL-6 in BALF were determined by ELISA.Airway remodeling proteins[fibronectin,α-smooth muscle actin(α-SMA),collagen Ⅰ]and NOD-like receptor protein 3(NLRP3)inflammasome-related proteins[NLRP3,cleaved caspase-1,apoptosis-associated speck-like protein(ASC)]levels were detected by Western blotting.Human bronchial smooth muscle cells(HBSMCs)were divided into control group(normal culture),transforming growth factor(TGF)-β1 group(culture medium containing 10 ng/ml TGF-β1),and TGF-β1+TPG group(culture medium containing 10 ng/ml TGF-β1 and 50 μg/ml TPG).Cell proliferation was detected by CCK-8 method,and Western blotting was used to detect the expression of airway remodeling proteins and NLRP3 inflammasome-related proteins.Results Compared with control group,model group exhibited increased infiltration of inflammatory cell in lung tissues,mucosal epithelium hyperplasia,narrowed bronchial lumen narrowed,tube wall thickened,increased cup cells and mucus secretion,and an elevated pathological score of lung injury(P<0.05);the number of inflammatory cells(WBC,NEU,EOS,and LYM)and the levels of inflammatory factors(TNF-α,IL-1β,and IL-6)in BALF were increased(P<0.05),and the expressions of fibronectin,α-SMA,collagen Ⅰ,NLRP3,cleaved caspase-1 and ASC were elevated(P<0.05).Compared with model group,BUD or TPG treatment effectively reduced asthma symptoms,improved lung histopathology injury,inhibited bronchial wall thickening,significantly reduced the number of inflammatory cells(WBC,NEU,EOS,and LYM)and the content of inflammatory factors(TNF-α,IL-1β,and IL-6)in BALF,and inhibited expression of fibronectin,α-SMA,collagen Ⅰ,NLRP3,cleaved caspase-1 and ASC(P<0.05).Compared with control group,the proliferation rate of HBSMCs was increased,and the protein expression levels of fibronectin,α-SMA,collagen Ⅰ,NLRP3,cleaved caspase-1 and ASC were increased in TGF-β1 group(P<0.05).Compared with TGF-β1 group,TPG treatment decreased cell proliferation and inhibited the protein expression of fibronectin,α-SMA,collagen Ⅰ,NLRP3,cleaved caspase-1 and ASC(P<0.05).Conclusion TPG may alleviate airway remodeling and asthma symptoms by decreasing the expression of airway remodeling-related proteins,inhibiting NLRP3 inflammasome activation,and reducing the inflammatory response.