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.

ObjectiveTo explore the therapeutic effect and mechanism of Huangqi Xiayuxue decoction against hepatocellular carcinoma based on network pharmacology， animal experiments， and cell experiments. MethodsThe active ingredients and corresponding targets of Huangqi Xiayuxue decoction were screened via databases including the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） and Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine （BATMAN-TCM）. Hepatocellular carcinoma-related targets were retrieved from the GeneCards database. The common targets shared by Huangqi Xiayuxue decoction and hepatocellular carcinoma were subjected to Gene Ontology （GO） functional annotation and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analyses. Huangqi Xiayuxue decoction-containing plasma was prepared. The cell counting kit-8 （CCK-8） assay was adopted to observe the effects of different volume fractions （5%， 10%， 15%， and 20%） of Huangqi Xiayuxue decoction-containing plasma on the proliferation of HepG2， MHCC-97H， Hepa1-6， and Huh-7 cells， and the concentration for subsequent experiments was determined. Flow cytometry was adopted to detect the effects of Huangqi Xiayuxue decoction-containing plasma （5%， 10%， 15%， and 20%） on the apoptosis of MHCC-97H cells. A mouse model of subcutaneous xenograft hepatocellular carcinoma was established. The model mice were assigned into low-， medium-， and high-dose （4.63， 9.25， 18.5 g·kg^-1） Huangqi Xiayuxue decoction and sorafenib （20 mg·kg^-1） groups. Body mass and tumor volume of mice were dynamically monitored during the intervention period. After 3 weeks of intervention， hematoxylin-eosin （HE） staining was performed to observe tumor morphology. Terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling （TUNEL） assay was employed to detect cell apoptosis. Immunohistochemistry （IHC） was adopted to examine the expression of cysteinyl aspartate-specific proteinase-3 （Caspase-3）， B-cell lymphoma-2 （Bcl-2）， and Bcl-2-associated X protein （Bax） in the tumor tissue. Real-time PCR and Western blot were employed to verify the changes in apoptosis-related factors and the phosphatidylinositol 3-kinase/protein kinase B/forkhead box O1 （PI3K/Akt/FoxO1） signaling pathway. ResultsA total of 220 common targets shared by Huangqi Xiayuxue decoction and hepatocellular carcinoma were screened out by network pharmacology， among which tumor protein p53 （TP53）， Akt1， and signal transducer and activator of transcription 3 （STAT3） were the core targets. Pathway enrichment analysis indicated that the common targets were involved in cell apoptosis and proliferation， as well as the PI3K/Akt and FoxO signaling pathways. Cell experiments showed that compared with the blank plasma group， all volume fractions of Huangqi Xiayuxue decoction-containing plasma inhibited the proliferation of HepG2， MHCC-97H， Hepa1-6， and Huh-7 cells （P<0.01）. The proportion of early apoptotic cells in MHCC-97H cells was increased after treatment with different volume fractions of Huangqi Xiayuxue decoction-containing plasma （P<0.01）. Animal experiments revealed that Huangqi Xiayuxue decoction inhibited the growth of tumor volume， promoted tumor cell apoptosis in the high-dose group， up-regulated the protein and mRNA levels of Bax and Caspase-3， down-regulated the protein and mRNA levels of Bcl-2， and reduced the phosphorylation level of the PI3K/Akt/FoxO1 signaling pathway （P<0.05， P<0.01）. ConclusionHuangqi Xiayuxue decoction exerts anti-hepatocellular carcinoma effects by inhibiting the activation of the PI3K/Akt/FoxO1 signaling pathway and promoting hepatocellular carcinoma cell apoptosis.

ObjectiveTo explore the therapeutic effect and mechanism of Huangqi Xiayuxue decoction against hepatocellular carcinoma based on network pharmacology， animal experiments， and cell experiments. MethodsThe active ingredients and corresponding targets of Huangqi Xiayuxue decoction were screened via databases including the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） and Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine （BATMAN-TCM）. Hepatocellular carcinoma-related targets were retrieved from the GeneCards database. The common targets shared by Huangqi Xiayuxue decoction and hepatocellular carcinoma were subjected to Gene Ontology （GO） functional annotation and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analyses. Huangqi Xiayuxue decoction-containing plasma was prepared. The cell counting kit-8 （CCK-8） assay was adopted to observe the effects of different volume fractions （5%， 10%， 15%， and 20%） of Huangqi Xiayuxue decoction-containing plasma on the proliferation of HepG2， MHCC-97H， Hepa1-6， and Huh-7 cells， and the concentration for subsequent experiments was determined. Flow cytometry was adopted to detect the effects of Huangqi Xiayuxue decoction-containing plasma （5%， 10%， 15%， and 20%） on the apoptosis of MHCC-97H cells. A mouse model of subcutaneous xenograft hepatocellular carcinoma was established. The model mice were assigned into low-， medium-， and high-dose （4.63， 9.25， 18.5 g·kg^-1） Huangqi Xiayuxue decoction and sorafenib （20 mg·kg^-1） groups. Body mass and tumor volume of mice were dynamically monitored during the intervention period. After 3 weeks of intervention， hematoxylin-eosin （HE） staining was performed to observe tumor morphology. Terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling （TUNEL） assay was employed to detect cell apoptosis. Immunohistochemistry （IHC） was adopted to examine the expression of cysteinyl aspartate-specific proteinase-3 （Caspase-3）， B-cell lymphoma-2 （Bcl-2）， and Bcl-2-associated X protein （Bax） in the tumor tissue. Real-time PCR and Western blot were employed to verify the changes in apoptosis-related factors and the phosphatidylinositol 3-kinase/protein kinase B/forkhead box O1 （PI3K/Akt/FoxO1） signaling pathway. ResultsA total of 220 common targets shared by Huangqi Xiayuxue decoction and hepatocellular carcinoma were screened out by network pharmacology， among which tumor protein p53 （TP53）， Akt1， and signal transducer and activator of transcription 3 （STAT3） were the core targets. Pathway enrichment analysis indicated that the common targets were involved in cell apoptosis and proliferation， as well as the PI3K/Akt and FoxO signaling pathways. Cell experiments showed that compared with the blank plasma group， all volume fractions of Huangqi Xiayuxue decoction-containing plasma inhibited the proliferation of HepG2， MHCC-97H， Hepa1-6， and Huh-7 cells （P<0.01）. The proportion of early apoptotic cells in MHCC-97H cells was increased after treatment with different volume fractions of Huangqi Xiayuxue decoction-containing plasma （P<0.01）. Animal experiments revealed that Huangqi Xiayuxue decoction inhibited the growth of tumor volume， promoted tumor cell apoptosis in the high-dose group， up-regulated the protein and mRNA levels of Bax and Caspase-3， down-regulated the protein and mRNA levels of Bcl-2， and reduced the phosphorylation level of the PI3K/Akt/FoxO1 signaling pathway （P<0.05， P<0.01）. ConclusionHuangqi Xiayuxue decoction exerts anti-hepatocellular carcinoma effects by inhibiting the activation of the PI3K/Akt/FoxO1 signaling pathway and promoting hepatocellular carcinoma cell apoptosis.

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.

The neurophysiological mechanisms by which exercise improves cognitive function have not been fully elucidated. A comprehensive and systematic review of current domestic and international neurophysiological evidence on exercise improving cognitive function was conducted from multiple perspectives. At the molecular level, exercise promotes nerve cell regeneration and synaptogenesis and maintains cellular development and homeostasis through the modulation of a variety of neurotrophic factors, receptor activity, neuropeptides, and monoamine neurotransmitters, and by decreasing the levels of inflammatory factors and other modulators of neuroplasticity. At the cellular level, exercise enhances neural activation and control and improves brain structure through nerve regeneration, synaptogenesis, improved glial cell function and angiogenesis. At the structural level of the brain, exercise promotes cognitive function by affecting white and gray matter volumes, neural activation and brain region connectivity, as well as increasing cerebral blood flow. This review elucidates how exercise improves the internal environment at the molecular level, promotes cell regeneration and functional differentiation, and enhances the brain structure and neural efficiency. It provides a comprehensive, multi-dimensional explanation of the neurophysiological mechanisms through which exercise promotes cognitive function.
Animals ; Humans ; Brain/physiology* ; Cognition/physiology* ; Exercise/physiology* ; Nerve Regeneration/physiology* ; Neuronal Plasticity/physiology*

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.

ObjectiveTo investigate the contamination status, transmission risk and drug resistance of Klebsiella pneumoniae (KP) on the object surfaces in the surrounding environment of hospitalized patients infected with carbapenem-resistant Klebsiella pneumoniae (CRKP) , so as to provide a scientific guidance for the prevention and control of healthcare-associated infection. MethodsSamples from the surfaces of objects in the surrounding environment of CRKP infected patients living in the intensive care unit (ICU) and hand specimens from healthcare workers were collected for KP isolation and identification, as well as drug susceptible test in a medical institution located in Minhang District, Shanghai from 2021 to 2023. Additionally, both univariate and multivariate logistic regression analyses were used to identify the influencing factors associated with KP contamination in the hospital environment. ResultsA total of 546 surface samples were collected from the surrounding environment objects of 15 patients infected with CRKP, with a KP detection rate of 6.59% (36/546).The KP detection rate in the ICU of general ward (10.22%) was higher than that in the ICU of emergency department (2.94%) (χ²=12.142, P<0.001). Moreover, the KP detection rate on the surfaces of patient-contacted items (15.66%) was higher than that on shared-use items (6.25%), cleaning items (10.00%), and medical supplies (3.30%) (χ²=17.943, P<0.001). Besides, the detection rate of KP in items sent out of hospital for disinfection (15.38%) was higher than that in those self-disinfected (4.20%) (χ²=19.996, P<0.001).The highest detection rate of KP was observed in high-temperature washing (15.13%, 18/119) (χ²=21.219, P<0.001), while the lowest detection rate was observed in antibacterial hand sanitizer with trichlorohydroxydiphenyl ether sanitizing factor (0, 0/60) ( χ²=21.219, P<0.001).The detection rate of KP in samples taken more than 24 hours after the last disinfection (23.08%) was higher than that in those taken at 4 to24 hours (12.90%) and less than 4 hours (4.22%) (χ²=23.398,P<0.001).ICU of general ward (OR=4.045, 95%CI: 2.206‒7.416), patient-contacted items (OR=3.113, 95%CI: 1.191‒8.141), and self-disinfection ( OR=0.241, 95%CI:0.144‒0.402) were influencing factors for KP contamination in environmental surface. From 2021 to 2023, the drug resistance rates of hospital environmental KP isolates showed an upward trend (P<0.001) to antibiotics such as ceftazidime and gentamicin. Furthermore, high drug resistance rates of KP (>90%) were observed to ciprofloxacin, levofloxacin, cefotaxime, ceftriaxone, and cefepime. ConclusionCRKP can be transmitted outward through the surfaces of objects in the patients’ surroundings, and the drug resistance situation is severe. In clinical settings, it is necessary to implement isolation measures for CRKP infection patients, to increase the frequency of disinfection for objects in their surroundings, to strengthen hand hygiene practices, and to use antibiotics appropriately.

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.