OBJECTIVE: To evaluate the efficacy and safety of Shexiang Tongxin Dropping Pill (STDP) in treating stable angina patients with phlegm-heat and blood-stasis syndrome by exercise duration and metabolic equivalents. METHODS: This multicenter, randomized, double-blind, placebo-controlled clinical trial enrolled stable angina patients with phlegm-heat and blood-stasis syndrome from 22 hospitals. They were randomized 1:1 to STDP (35 mg/pill, 6 pills per day) or placebo for 56 days. The primary outcome was the exercise duration and metabolic equivalents (METs) assessed by the standard Bruce exercise treadmill test after 56 days of treatment. The secondary outcomes included the total angina symptom score, Chinese medicine (CM) symptom scores, Seattle Angina Questionnaire (SAQ) scores, changes in ST-T on electrocardiogram and adverse events (AEs). RESULTS: This trial enrolled 309 patients, including 155 and 154 in the STDP and placebo groups, respectively. STDP significantly prolonged exercise duration with an increase of 51.0 s, compared to a decrease of 12.0 s with placebo (change rate: -11.1% vs. 3.2%, P<0.01). The increase in METs was significantly greater in the STDP group than in the placebo group (change: -0.4 vs. 0.0, change rate: -5.0% vs. 0.0%, P<0.01). The improvement of total angina symptom scores (25.0% vs. 0.0%), CM symptom scores (38.7% vs. 11.8%), reduction of nitroglycerin consumption (100.0% vs. 11.3%), and all domains of SAQ, were significantly greater with STDP than placebo (all P<0.01). The changes in Q-T intervals at 28 and 56 days from baseline were similar between the two groups (both P>0.05). Twenty-five participants (16.3%) with STDP and 16 (10.5%) with placebo experienced AEs (P=0.131), with no serious AEs observed. CONCLUSION STDP could improve exercise tolerance in patients with stable angina and phlegm-heat and blood stasis syndrome, with a favorable safety profile. (Registration No. ChiCTR-IPR-15006020).
Humans ; Double-Blind Method ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Middle Aged ; Angina, Stable/physiopathology* ; Aged ; Syndrome ; Treatment Outcome ; Placebos ; Tablets

Despite therapy with potent antiviral agents, chronic hepatitis B (CHB) patients remain at high risk of hepatocellular carcinoma (HCC). While metabolites have been rediscovered as active drivers of biological processes including carcinogenesis, the specific metabolites modulating HCC risk in CHB patients are largely unknown. Here, we demonstrate that baseline plasma from CHB patients who later developed HCC during follow-up exhibits growth-promoting properties in a case-control design nested within a large-scale, prospective cohort. Metabolomics analysis reveals a reduction in long-chain acylcarnitines (LCACs) in the baseline plasma of patients with HCC development. LCACs preferentially inhibit the proliferation of HCC cells in vitro at a physiological concentration and prevent the occurrence of HCC in vivo without hepatorenal toxicity. Uptake and metabolism of circulating LCACs increase the intracellular level of acetyl coenzyme A, which upregulates histone H3 Lys14 acetylation at the promoter region of KLF6 gene and thereby activates KLF6/p21 pathway. Indeed, blocking LCAC metabolism attenuates the difference in KLF6/p21 expression induced by baseline plasma of HCC/non-HCC patients. The deficiency of circulating LCACs represents a driver of HCC in CHB patients with viral control. These insights provide a promising direction for developing therapeutic strategies to reduce HCC risk further in the antiviral era.

Cardiotoxicity is a critical issue in drug development that poses serious health risks,including potentially fatal arrhythmias.The human ether-à-go-go related gene(hERG)potassium channel,as one of the pri-mary targets of cardiotoxicity,has garnered widespread attention.Traditional cardiotoxicity testing methods are expensive and time-consuming,making computational virtual screening a suitable alter-native.In this study,we employed machine learning techniques utilizing molecular fingerprints and descriptors to predict the cardiotoxicity of compounds,with the aim of improving prediction accuracy and efficiency.We used four types of molecular fingerprints and descriptors combined with machine learning and deep learning algorithms,including Gaussian naive Bayes(NB),random forest(RF),support vector machine(SVM),K-nearest neighbors(KNN),eXtreme gradient boosting(XGBoost),and Trans-former models,to build predictive models.Our models demonstrated advanced predictive performance.The best machine learning model,XGBoost Morgan,achieved an accuracy(ACC)value of 0.84,and the deep learning model,Transformer_Morgan,achieved the best ACC value of 0.85,showing a high ability to distinguish between toxic and non-toxic compounds.On an external independent validation set,it achieved the best area under the curve(AUC)value of 0.93,surpassing ADMETlab3.0,Cardpred,and CardioDPi.In addition,we explored the integration of molecular descriptors and fingerprints to enhance model performance and found that ensemble methods,such as voting and stacking,provided slight improvements in model stability.Furthermore,the SHapley Additive exPlanations(SHAP)explanations revealed the relationship between benzene rings,fluorine-containing groups,NH groups,oxygen in ether groups,and cardiotoxicity,highlighting the importance of these features.This study not only improved the predictive accuracy of cardiotoxicity models but also promoted a more reliable and scientifically interpretable method for drug safety assessment.Using computational methods,this study facilitates a more efficient drug development process,reduces costs,and improves the safety of new drug candidates,ultimately benefiting medical and public health.

Cardiotoxicity is a critical issue in drug development that poses serious health risks, including potentially fatal arrhythmias. The human ether-à-go-go related gene (hERG) potassium channel, as one of the primary targets of cardiotoxicity, has garnered widespread attention. Traditional cardiotoxicity testing methods are expensive and time-consuming, making computational virtual screening a suitable alternative. In this study, we employed machine learning techniques utilizing molecular fingerprints and descriptors to predict the cardiotoxicity of compounds, with the aim of improving prediction accuracy and efficiency. We used four types of molecular fingerprints and descriptors combined with machine learning and deep learning algorithms, including Gaussian naive Bayes (NB), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), eXtreme gradient boosting (XGBoost), and Transformer models, to build predictive models. Our models demonstrated advanced predictive performance. The best machine learning model, XGBoost Morgan, achieved an accuracy (ACC) value of 0.84, and the deep learning model, Transformer_Morgan, achieved the best ACC value of 0.85, showing a high ability to distinguish between toxic and non-toxic compounds. On an external independent validation set, it achieved the best area under the curve (AUC) value of 0.93, surpassing ADMETlab3.0, Cardpred, and CardioDPi. In addition, we explored the integration of molecular descriptors and fingerprints to enhance model performance and found that ensemble methods, such as voting and stacking, provided slight improvements in model stability. Furthermore, the SHapley Additive exPlanations (SHAP) explanations revealed the relationship between benzene rings, fluorine-containing groups, NH groups, oxygen in ether groups, and cardiotoxicity, highlighting the importance of these features. This study not only improved the predictive accuracy of cardiotoxicity models but also promoted a more reliable and scientifically interpretable method for drug safety assessment. Using computational methods, this study facilitates a more efficient drug development process, reduces costs, and improves the safety of new drug candidates, ultimately benefiting medical and public health.

This paper used the Experimental Teaching Management Center as an example to explore the transformation practices of experimental technicians in job responsibilities, team structure, teaching and research capabilities, performance mechanisms, and collaborative education systems. The article systematically analyzed the advantages of the experimental technical team in resource openness, technical support, large instrument management, and multi-team collaboration, and summarized the practical performance through data on project approvals, competition awards, research achievements, and student growth. Additionally, it identified key challenges, including insufficient training, incomplete incentive mechanisms, and the need for improved resource coordination. To address these challenges, the study recommends the continuous enhancement of personnel capacity building, the reform of assessment systems, and the reinforcement of cross-departmental collaboration. This study provides a reference for medical schools to construct the practical path of experimental technical teams participating in the cultivation of innovative and entrepreneurial talents.

This paper used the Experimental Teaching Management Center as an example to explore the transformation practices of experimental technicians in job responsibilities, team structure, teaching and research capabilities, performance mechanisms, and collaborative education systems. The article systematically analyzed the advantages of the experimental technical team in resource openness, technical support, large instrument management, and multi-team collaboration, and summarized the practical performance through data on project approvals, competition awards, research achievements, and student growth. Additionally, it identified key challenges, including insufficient training, incomplete incentive mechanisms, and the need for improved resource coordination. To address these challenges, the study recommends the continuous enhancement of personnel capacity building, the reform of assessment systems, and the reinforcement of cross-departmental collaboration. This study provides a reference for medical schools to construct the practical path of experimental technical teams participating in the cultivation of innovative and entrepreneurial talents.

Epstein-Barr virus(EBV)is the first virus discovered to be associated with human tumors,and its association with gastric cancer has received widespread attention in recent years.In 2014,The Cancer Genome Atlas initially classified gastric cancer into four subtypes based on molecular characteristics,with EBV positive individuals grouped into a distinct subtype known as EBV-positive gastric cancer,which has unique molecular and clinicopathological characteristics.While EBV infection in malignant gastric epithelial cells and the induction of host genetic mutations and epigenetic abnormalities were generally considered as important bases for EBV carcinogenesis,the mechanisms un-derlying the relationship between EBV infection and gastric cancer risk remain unclear.We here summarize the existing epidemiological data supporting associations between different indicators of EBV infection and gastric cancer and provide an overview of the potential roles and mechanisms of EBV in gastric cancer carcinogenesis,which is expected to offer significant guidance for future studies elucidating the rela-tionship between EBV infection and gastric cancer development.

Objective:To investigate the diagnostic efficacy of targeted biopsy combined with regional systematic biopsy in prostate cancer(PCa)in patients with prostate imaging reporting and data system v2.1(PI-RADS v2.1)4-5.Methods:From January 2023 to October 2023,patients who underwent prostate biopsy for the first time with total prostate specific antigen(tPSA)≤20 ng/mL and had a multi-parametric magnetic resonance imaging(mpMRI)PI-RADS of 4-5 in Peking University First Hospital were prospectively collected.All the patients underwent transrectal ultrasound-guided cognitive fusion tar-geted biopsy(3 cores)followed by systematic biopsy(12 cores).Various hypothetical biopsy schemes were defined based on different biopsy sites.The detection effectiveness of targeted biopsy combined with regional systematic biopsy and other biopsy schemes for prostate cancer were compared using Cochran's Q and McNemar tests.Results:A total of 255 patients were enrolled,of whom 204(80.0％)were de-tected with prostate adenocarcinoma and 187(73.3％)were clinically significant with prostate cancer(csPCa).The detection rate of PCa with targeted biopsy was significantly lower than that of targeted biopsy combined with 12-core system biopsy(77.3％vs.80.0％,P=0.016),and 71.4％(5/7)of the missed patients was csPCa.There was no significant difference in the detection rate between targeted biopsy combined with 4-core regional system biopsy and 12-core system biopsy(P＞0.999),and 1 case of csPCa and clinically insignificant prostate cancer(cisPCa)were missed.There was no significant difference in the detection rate of PCa between targeted combined regional system biopsy and targeted combined lateral or traditional 6-core system biopsy and the number of cores were reduced.Missed diag-nosis of targeted biopsy was correlated with the maximum diameter of the lesion(OR=0.086,95％CI:0.013-0.562,P=0.010).For the patients with PI-RADS 5,only 1 case of PCa was missed in 122 cases by targeted biopsy alone.For patients with PI-RADS 4,6 PCa cases were missed among the 133 patients with targeted biopsy alone,and 1 case of csPCa and cisPCa were missed by targeted biopsy com-bined with regional system biopsy.The statistics of positive core counts for different biopsy schemes indi-cated that targeted combined regional systematic biopsy had a higher proportion of positive cores second only to targeted biopsy alone.Conclusion:Targeted biopsy combined with regional systematic biopsy has high diagnostic efficacy in patients with PI-RADS 4-5 and can be considered as one of the improved schemes for combined biopsy.Targeted biopsy alone is also a feasible option for patients for patients with a PI-RADS score of 5.

OBJECTIVE To provide reference for the improvement of the price system of innovative drugs in China. METHODS Based on the current situation of innovative drug healthcare management in China， innovation recognition and price management of innovative drugs in typical countries or regions were compared and analyzed， and the suggestions were put forward to improve the price system of innovative drugs in China. RESULTS & CONCLUSIONS Taiwan of China， Japan， Australia， Germany and the United Kingdom have different practices in recognition and grading of drug innovation， differentiated pricing and subsequent price management. However， the mainstream practice is to identify and grade drugs mostly based on clinical value， differentiate pricing based on the grading， and carry out risk-sharing agreement or active price adjustment to manage the price. Based on the comparative analysis， it is suggested that China should further clarify the connotation and classification of innovation in the management of innovative drugs， apply a variety of pricing methods to promote differentiated management of innovative drugs， explore the introduction of risk-sharing agreements， and further build an active drug price adjustment mechanism to improve the price system of innovative drugs in China.

Chronic urticaria （CU） is a common skin disease worldwide， and its incidence is increasing year by year in various regions. Clinical manifestations such as severe itching can affect normal work， sleep， and daily life and increase the negative psychological burden caused by stress， anxiety， and depression. Mast cell activation and degranulation induced by immunoglobulin（Ig）E hypersensitivity is one of the core pathogenic mechanisms of CU， and there is no cure. Antihistamines such as cetirizine and loratadine are preferred for the clinical treatment of CU. Although they can effectively improve clinical manifestations such as itchiness， long-term application can increase the risk of adverse reactions and drug resistance. The phosphatidylinositol kinase/serine-threonine protein kinase B（PI3K/Akt） signaling pathway， as a classical signaling pathway regulated by phosphatidylinositol and tyrosine kinase receptor （RTK）， is a key target regulating the production and release of cytokines in macrophages and affecting the migration of leukocytes and the activation of mast cells and inflammation， and it can be involved in a variety of metabolic processes， such as mast cell activation and degranulation induced by IgE hypersensitivity and abnormal activation of the complement system so that the PI3K/Akt molecular pathway could be an important target for the future eradication of CU. However， the mechanism and potential role of the PI3K/Akt signaling pathway in the treatment of CU are less reported in China. Now， this paper reviewed the molecular mechanism of PI3K/Akt signaling pathway regulation in the treatment of CU and provided corroborative evidence and therapeutic strategy choices for the treatment of CU with traditional Chinese medicine （TCM） from the perspectives of molecular regulation and network pharmacology analysis.