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.

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.

Piper nigrum L. is an evergreen climbing vine, which belongs to the genus Piperia in the Piperaceae family. Piper nigrum L., which known as the “king of spices”, is used as both food and medicine. The main active substances in Piper nigrum L. are alkaloids mainly composed of amides, and essential oil, as well as phenolic compounds. In this paper, the chemical compositions, especially amide alkaloids, and their biological activities of Piper nigrum L. were summarized. These studies showed that Piper nigrum L., as a medicinal and food plant, had a wide range of biological activities and was deserved further research and in-depth utilization.

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.

Randomized controlled trial(RCT) is considered to represent the gold standard for evaluating the efficacy of interventions and has been widely used to evaluate the clinical efficacy of traditional Chinese medicine(TCM). However, there are unique challenges in implementing RCT in TCM. Patients seeking TCM treatment often have preferences for TCM due to the unsatisfactory therapeutic effect of western medicine, their personal intolerance, and their rejection of certain drugs, medical devices, or surgery. Patients are generally reluctant to be randomly assigned to a group, making it challenging to enroll patients in the control group of western medicine during the implementation of RCT in TCM. This has become a prominent problem restricting the implementation of RCT in TCM and needs to be solved urgently. Therefore, this paper introduced commonly used research designs used in solving the problem of enrolling patients in control group during the implementation of RCT in TCM, including Zelen design, partially randomized patient preference trial(PRPP), single-arm objective performance criteria(OPC), cohort studies, single-arm clinical trials using real world data(RWD) alone as the external control group, and the design method based on RWD-augmented control group samples in RCT. The paper outlined the definitions and principles of these methods, evaluated their advantages, disadvantages, and applicable scenarios, and explored their applications in the TCM field, so as to offer insights for solving the difficulty in enrolling patients in the control group during the implementation of RCT in TCM.
Humans ; Medicine, Chinese Traditional/methods* ; Randomized Controlled Trials as Topic/methods* ; Research Design ; Patient Selection ; Drugs, Chinese Herbal/therapeutic use* ; Control Groups

This study investigated the effects of total flavonoids of Dracocephalum moldavica(TFDM) on apoptosis in rat H9c2 cells induced by endoplasmic reticulum stress(ERS) established by oxygen-glucose deprivation and reoxygenation(OGD/R) injury and tunicamycin(TM), and explored the potential mechanisms. After successful modeling, the following groups were set in this experiment: control group, model(OGD/R or TM) group, and TFDM low-, medium-, and high-dose groups(12.5, 25, and 50 μg·mL~(-1)). The OGD/R injury model was constructed in vitro. Cell proliferation was assessed using the cell counting kit-8(CCK-8) method. The levels of lactate dehydrogenase(LDH) and creatine kinase MB isoenzyme(CKMB) in the cell supernatant were detected. Western blot was used to assess the expression of ERS-related proteins, including glucose regulatory protein 78(GRP78), C/EBP homologous protein(CHOP), activating transcription factor 6(ATF6), and apoptotic proteins B-cell lymphoma 2(Bcl-2) and Bcl-2-associated X protein(Bax). Apoptosis was detected using the terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling(TUNEL) method. In the TM-induced ERS model, Western blot was used to measure the expression of ERS pathway-related proteins GRP78, CHOP, inositol-requiring enzyme 1(IRE1), X-box binding protein 1(XBP1), protein kinase RNA-like endoplasmic reticulum kinase(PERK), eukaryotic initiation factor 2α(eIF2α), ATF6, p-ATF6, and apoptotic proteins Bcl-2, Bax, cysteinyl aspartate specific proteinase-12(caspase-12), and cleaved caspase-12. Gene expression of GRP78, CHOP, PERK, and ATF6 was detected by real-time fluorescence quantitative PCR(RT-qPCR). Apoptosis was again detected using the TUNEL method. The results showed that in the OGD/R model, compared with the control group, the levels of LDH and CKMB in the cell supernatant were significantly increased in the OGD/R group. Compared with the OGD/R group, the levels of LDH and CKMB in the TFDM group were significantly reduced. Western blot results revealed that compared with the control group, the expression of ERS-related proteins and Bax in the OGD/R group was significantly increased, while the expression of Bcl-2 was significantly decreased. Compared with the OGD/R group, the expression of ERS-related proteins and Bax in the TFDM groups was significantly reduced, and the expression of Bcl-2 was significantly increased. TUNEL assay showed that apoptosis was significantly decreased after TFDM treatment. In the TM-induced ERS experiment, compared with the control group, the expression of ERS-related genes, ERS-related proteins, and apoptotic proteins in the TM group was significantly increased, while the expression of Bcl-2 was significantly decreased. Compared with the TM group, the expression of ERS-related genes, ERS-related proteins, and apoptotic proteins in the TFDM group was significantly reduced, and the expression of Bcl-2 was significantly increased. These results suggest that ERS exists in the OGD/R-injured H9c2 cell model, and TFDM can effectively inhibit ERS-induced apoptosis. The mechanism may be related to the downregulation of ERS pathway-related proteins and apoptotic proteins.
Animals ; Endoplasmic Reticulum Stress/drug effects* ; Apoptosis/drug effects* ; Rats ; Flavonoids/pharmacology* ; Glucose/metabolism* ; Cell Line ; Lamiaceae/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Oxygen/metabolism* ; Reperfusion Injury/physiopathology* ; Myocytes, Cardiac/cytology*