This paper systematically reviews the current status of integrated traditional Chinese and western medicine in the treatment of Helicobacter pylori （Hp） infection， as well as recent progress in clinical and basic research both in China and internationally. It summarizes the advantages of traditional Chinese medicine （TCM） in Hp infection management， including improving Hp eradication rates， enhancing antibiotic sensitivity， reducing antimicrobial resistance， decreasing drug-related adverse effects， and ameliorating gastric mucosal lesions. These advantages are particularly evident in patients who are intolerant to bismuth-containing regimens， those with refractory Hp infection， and individuals with precancerous gastric lesions. An integrated， whole-process management approach and individualized， staged comprehensive treatment strategies combining TCM and western medicine are proposed for Hp infection. Future prevention and control of Hp infection should adopt an integrative Chinese-western medical strategy， emphasizing prevention， strengthening primary care， implementing proactive long-term monitoring， optimizing screening strategies， and advancing the development of novel technologies and mechanistic studies of Chinese herbal interventions. These efforts aim to provide a theoretical basis and practical pathways for the establishment and improvement of Hp infection prevention and control systems.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveFat infiltration has been shown to be closely related to muscle mass loss and a variety of muscle diseases. This study proposes a method based on phase-angle electrical impedance tomography (ΦEIT) to visualize the electrical characteristic response caused by muscle fat infiltration, aiming to provide a new technical means for early non-invasive detection of muscle mass deterioration. MethodsThis study was divided into two parts. First, a laboratory pork model was constructed to simulate different degrees of fat infiltration by injecting1 ml or 2 ml of emulsified fat solution into different muscle compartments, and the phase angle images were reconstructed using ΦEIT. Second, a human experiment was conducted to recruit healthy subjects (n=8) from two age groups (20-25 years old and 26-30 years old). The fat content percentage η_fat of the left and right legs was measured by bioelectrical impedance analysis (BIA), and the phase angle images of the left and right calves were reconstructed using ΦEIT. The relationship between the global average phase angle Φ_M and the spatial average phase angle Φ_Mi of each muscle compartment and fat infiltration was further analyzed. ResultsIn the laboratory pork model, the grayscale value of the image increased with the increase of η_fat and Φ_M showed a downward trend. The results of human experiments showed that at the same fat content percentage, the Φ_M of the 26-30-year-old group was about 20%-35% lower than that of the 20-25-year-old group. The fat content percentage was significantly negatively correlated with Φ_M. In addition, the M₂ (soleus) compartment was most sensitive to fat infiltration, and the spatial average phase angles of the M₂ (soleus), M₃ (tibialis posterior and flexor digitorum longus), and M₄ (tibialis anterior, extensor digitorum longus, and peroneus longus) compartments all showed significant inter-group differences. ConclusionΦEIT imaging can effectively distinguish different degrees of fat infiltration, especially in deep, small or specially located muscles, showing high sensitivity, demonstrating the potential application of this method in local muscle mass monitoring and early non-invasive diagnosis.

Traditional Chinese medicine(TCM) processing is a unique and highly distinctive pharmaceutical technology in China. Utilizing modern scientific methods to elucidate the connotations of traditional processing theory and its effects is expected to facilitate the inheritance, development, innovation, and enhancement of TCM processing, and lead to more original research outcomes in the field of TCM. The breakthrough in TCM processing lies in the study of its underlying principles, and analyzing these principles involves researching the transformation mechanisms of chemical components and the biological effect mechanisms of the transformed components. This paper proposed the concept of "TCM processing chemical biology"(TCMPCB) for the first time. Under the guidance of TCM theory, the active components transformed during TCM processing were used as chemical tools to study their targets and molecular regulatory mechanisms, aiming to clarify the scientific principles by which TCM processing affected biological effects in the organism. The research findings also provided new directions for discovering novel active components, new lead compounds, creating new decoction pieces, and developing new TCM drugs. This paper provided a detailed introduction to the background, definition, research content, research ideas, research methods, and prospects of TCMPCB, with the aim of offering new research perspectives for analyzing the principles of TCM processing and providing new pathways for achieving the "four new and eight transformations" in TCM processing.
Drugs, Chinese Herbal/chemistry* ; Medicine, Chinese Traditional/methods* ; Humans ; Animals

By employing the analytic hierarchy process(AHP), the CRITIC method(a weight determination method based on indicator correlations), and the AHP-CRITIC hybrid weighting method, the weight coefficients of evaluation indicators were determined, followed by a comprehensive score comparison. The grey correlation analysis was then performed to analyze the results calculated using the hybrid weighting method. Subsequently, a backpropagation-artificial neural network(BP-ANN) model was constructed to predict the extraction process parameters and optimize the extraction process for Shenxiong Huanglian Jiedu Granules(SHJG). In the extraction process, an L_9(3~4) orthogonal experiment was designed to optimize three factors at three levels, including extraction frequency, water addition amount, and extraction time. The evaluation indicators included geniposide, berberine, ginsenoside Rg_1 + Re, ginsenoside Rb_1, ferulic acid, and extract yield. Finally, the optimal extraction results obtained by the orthogonal experiment, grey correlation analysis, and BP-ANN method were compared, and validation experiments were conducted. The results showed that the optimal extraction process involved two rounds of aqueous extraction, each lasting one hour; the first extraction used ten times the amount of added water, while the second extraction used eight times the amount. In the validation experiments, the average content of each indicator component was higher than the average content obtained in the orthogonal experiment, with a higher comprehensive score. The optimized extraction process parameters were reliable and stable, making them suitable for subsequent preparation process research.
Drugs, Chinese Herbal/analysis* ; Neural Networks, Computer

The medicinal materials of Bawei Chenxiang Pills(BCPs) were extracted via three methods: reflux extraction by water, reflux extraction by 70% ethanol, and extraction by pure water following reflux extraction by 70% ethanol, yielding three extracts of ST, CT, and CST. The efficacy of ST(760 mg·kg~(-1)), CT(620 mg·kg~(-1)), and CST(1 040 mg·kg~(-1)) were evaluated by acute myocardial ischemia(AMI) and p-chlorophenylalanine(PCPA)-induced insomnia in mice, respectively. Western blot was further utilized to investigate their hypnosis mechanisms. The main chemical components of different extracts were identified by the UPLC-Q-Exactive-MS technique. The results showed that CT and CST significantly increased the ejection fraction(EF) and fractional shortening(FS) of myocardial infarction mice, reduced left ventricular internal dimension at end-diastole(LVIDd) and left ventricular internal dimension at end-systole(LVIDs). In contrast, ST did not exhibit significant effects on these parameters. In the insomnia model, CT significantly reduced sleep latency and prolonged sleep duration, whereas ST only prolonged sleep duration without shortening sleep latency. CST showed no significant effects on either sleep latency or sleep duration. Additionally, both CT and ST upregulated glutamic acid decarboxylase 67(GAD67) protein expression in brain tissue. A total of 15 main chemical components were identified from CT, including 2-(2-phenylethyl) chromone and 6-methoxy-2-(2-phenylethyl) chromone. Six chemical components including chebulidic acid were identified from ST. The results suggested that chromones and terpenes were potential anti-myocardial ischemia drugs of BCPs, and tannin and phenolic acids were potential hypnosis drugs. This study enriches the pharmacological and chemical research of BCPs, providing a basis and reference for their secondary development, quality standard improvement, and clinical application.
Animals ; Drugs, Chinese Herbal/isolation & purification* ; Mice ; Male ; Sleep Initiation and Maintenance Disorders/physiopathology* ; Humans ; Myocardial Infarction/drug therapy* ; Myocardial Ischemia/drug therapy*

Traditional Chinese medicine(TCM) is the pillar for the development of motherland medicine, and animal medicine has a long history of application in China, characterized by wide resources, strong activity, definite efficacy, and great benefits. It has significant potential and important status in the consumption market of raw materials of TCM. In the context of global climate change, farming system alterations, and low renewability, the depletion of wild medicinal animal resources has accelerated. Accordingly, the conservation and sustainable utilization of wild resources of animal medicinal materials has become a problem that garners increasing attention and urgently needs to be solved. This paper summarizes the current situation of domestic and foreign medicinal animal breeding and research progress in industrial application in recent years and points out the issues related to standardized breeding, germplasm selection and breeding, and quality evaluation standards for medicinal animals. Furthermore, this paper discusses standardized breeding, quality standards, resource protection and utilization, and the search for alternative resources for rare and endangered medicinal animals. It proposes that researchers should systematically carry out in-depth basic research on animal medicine, improve the breeding scale and level of medicinal animals, employ modern technology to enhance the quality standards of medicinal materials, and strengthen the research and development of alternative resources. This approach aims to effectively address the relationship between protection and utilization and make a significant contribution to the sustainable development of medicinal animal resources and the animal-based Chinese medicinal material industry.
Animals ; Breeding ; China ; Medicine, Chinese Traditional ; Conservation of Natural Resources

OBJECTIVE: To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis (TB) in the Guangdong Province between 2010 and 2019. METHOD: Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering. Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive (ST-CAR) model. RESULTS: Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000 in 2019. Spatial hotspots were found in northeastern Guangdong, particularly in Heyuan, Shanwei, and Shantou, while Shenzhen, Dongguan, and Foshan had the lowest rates in the Pearl River Delta. The ST-CAR model showed that the TB risk was lower with higher per capita Gross Domestic Product (GDP) [Relative Risk ( RR), 0.91; 95% Confidence Interval ( CI): 0.86-0.98], more the ratio of licensed physicians and physician ( RR, 0.94; 95% CI: 0.90-0.98), and higher per capita public expenditure ( RR, 0.94; 95% CI: 0.90-0.97), with a marginal effect of population density ( RR, 0.86; 95% CI: 0.86-1.00). CONCLUSION The incidence of TB in Guangdong varies spatially and temporally. Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection. Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
Humans ; China/epidemiology* ; Incidence ; Bayes Theorem ; Spatio-Temporal Analysis ; Tuberculosis/epidemiology* ; Socioeconomic Factors

Ambient air pollution is increasingly being recognized as a risk factor for heart failure; however, its effects on cardiac biomarkers remain unclear. This scoping review assessed the existing evidence on the association between air pollution and cardiac biomarkers in heart failure, described the key concepts, synthesized data, and identified research gaps. Following the PRISMA-ScR guidelines, PubMed, Embase, Web of Science, and CNKI databases were searched for studies on air pollution, heart failure, and biomarkers. A total of 765 records were screened, and 81 full texts were assessed for eligibility, resulting in 15 studies. The results showed that the exposure to particulate matter was associated with elevated N-terminal pro-B-type natriuretic peptide and troponin levels. Several studies have linked particulate matter exposure to a higher cardiovascular risk and heart failure biomarkers. Inflammatory and oxidative stress markers were consistently elevated across studies, supporting the biological relevance of these associations. However, few studies have focused specifically on populations with heart failure or clinically relevant biomarkers, and the evidence for gaseous pollutants remains inconclusive. These findings highlight the need to integrate environmental risk assessment into heart failure care and inform policy efforts to reduce the pollution-related cardiovascular burden. Further research should address these gaps through improved exposure assessments and the integration of mechanistic evidence.
Heart Failure/epidemiology* ; Biomarkers/metabolism* ; Humans ; Air Pollution/adverse effects* ; Air Pollutants/adverse effects* ; Particulate Matter/adverse effects* ; Environmental Exposure ; Natriuretic Peptide, Brain/blood* ; Oxidative Stress ; Troponin/blood*