Objective To analyze the differences in distribution of traditional Chinese medicine (TCM) syndrome elements and salivary microbiota between the individuals with pulmonary nodules and those without, and to explore the potential correlation between the distribution of TCM syndrome elements and salivary microbiota in patients with pulmonary nodules. Methods We retrospectively recruited 173 patients with pulmonary nodules (PN) and 40 healthy controls (HC). The four diagnostic information was collected from all participants, and syndrome differentiation method was used to analyze the distribution of TCM syndrome elements in both groups. Saliva samples were obtained from the subjects for 16S rRNA high-throughput sequencing to obtain differential microbiota and to explore the correlation between TCM syndrome elements and salivary microbiota in the evolution of the pulmonary nodule disease. Results The study found that in the PN group, the primary TCM syndrome elements related to disease location were the lung and liver, and the primary TCM syndrome elements related to disease nature were yin deficiency and phlegm. In the HC group, the primary TCM syndrome elements related to disease location were the lung and spleen, and the primary TCM syndrome elements related to disease nature were dampness and qi deficiency. There were differences between the two groups in the distribution of TCM syndrome elements related to disease location (lung, liver, kidney, exterior, heart) and disease nature (yin deficiency, phlegm, qi stagnation, qi deficiency, dampness, blood deficiency, heat, blood stasis) (P<0.05). The species abundance of the salivary microbiota was higher in the PN group than that in the HC group (P<0.05), and there was significant difference in community composition between the two groups (P<0.05). Correlation analysis using multiple methods, including Mantel test network heatmap analysis and Spearman correlation analysis and so on, the results showed that in the PN group, Prevotella and Porphyromonas were positively correlated with disease location in the lung, and Porphyromonas and Granulicatella were positively correlated with disease nature in yin deficiency (P<0.05). Conclusion The study concludes that there are notable differences in the distribution of TCM syndrome elements and the species abundance and composition of salivary microbiota between the patients with pulmonary nodules and the healthy individuals. The distinct external syndrome manifestations in patients with pulmonary nodules, compared to healthy individuals, may be a cascade event triggered by changes in the salivary microbiota. The dual correlation of Porphyromonas with both disease location and nature suggests that changes in its abundance may serve as an objective indicator for the improvement of symptoms in patients with yin deficiency-type pulmonary nodules.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

ObjectiveTo characterize the evidence distribution and methodological quality of randomized controlled trials （RCTs） on oral Chinese herbal medicine （CHM） for atopic dermatitis （AD） based on evidence mapping. MethodsSeven databases （CNKI， Wanfang Data， VIP， CBM， Cochrane Library， PubMed， and Embase） and the Chinese Clinical Trial Registry were searched for the RCTs in Chinese and English. Evidence distribution was presented graphically and textually， and methodological quality was assessed via the Cochrane Risk of Bias tool （ROB 1.0）. ResultsA total of 168 RCTs were included. The number of annual publications showing an increasing trend， and 72.6% RCTs had sample sizes of 51-100 participants. The studies evaluated 108 distinct CHM interventions categorized as decoctions， granules， Chinese patent medicines， and extracts. Compound Glycyrrhizin was the most frequently used， followed by Xiaofengsan and Chushi Weiling decoction. Among the RCTs， 57.1% had the treatment courses of 4-8 weeks. Outcome measures predominantly focused on clinical response rate， skin lesion severity scores， and adverse events， with less attention to TCM symptom scores， skin barrier function， and relapse rates. The overall risk of bias was generally high. ConclusionWhile CHM for AD is a research hotspot and demonstrates clinical advantages， the related studies have problems such as unclear clinical positioning， poor research standardization and methodological quality， and insufficient prominence of TCM clinical advantages. Large-sample， methodologically rigorous， and high-quality studies are needed to enhance the evidence base for CHM in treating AD.

ObjectiveTo characterize the evidence distribution and methodological quality of randomized controlled trials （RCTs） on oral Chinese herbal medicine （CHM） for atopic dermatitis （AD） based on evidence mapping. MethodsSeven databases （CNKI， Wanfang Data， VIP， CBM， Cochrane Library， PubMed， and Embase） and the Chinese Clinical Trial Registry were searched for the RCTs in Chinese and English. Evidence distribution was presented graphically and textually， and methodological quality was assessed via the Cochrane Risk of Bias tool （ROB 1.0）. ResultsA total of 168 RCTs were included. The number of annual publications showing an increasing trend， and 72.6% RCTs had sample sizes of 51-100 participants. The studies evaluated 108 distinct CHM interventions categorized as decoctions， granules， Chinese patent medicines， and extracts. Compound Glycyrrhizin was the most frequently used， followed by Xiaofengsan and Chushi Weiling decoction. Among the RCTs， 57.1% had the treatment courses of 4-8 weeks. Outcome measures predominantly focused on clinical response rate， skin lesion severity scores， and adverse events， with less attention to TCM symptom scores， skin barrier function， and relapse rates. The overall risk of bias was generally high. ConclusionWhile CHM for AD is a research hotspot and demonstrates clinical advantages， the related studies have problems such as unclear clinical positioning， poor research standardization and methodological quality， and insufficient prominence of TCM clinical advantages. Large-sample， methodologically rigorous， and high-quality studies are needed to enhance the evidence base for CHM in treating AD.

Lung cancer is the leading cause of death worldwide. With the prevalence of CT screening and early diagnosis and treatment of lung cancer in China, more and more patients with early-stage lung cancer characterized with ground-glass opacity are discovered and urgently require treatment, which poses a significant challenge to surgeons. As an emerging technology, three dimensional reconstruction technology plays a crucial auxiliary role in clinical work. This review aims to briefly introduce this technology, focusing on its latest advances in surgical applications in early lung cancer screening, malignant risk assessment, and perioperative period application and medical education.

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.

The purpose of this paper is to explore the decoction and dosing methods of rhubarb root and rhizome in classical prescriptions and to provide a reference basis for the clinical use of rhubarb root and rhizome. By collating the relevant classical prescriptions of rhubarb root and rhizome in Shanghan Lun and Jingui Yaolüe, the relationship between its decoction and dosing methods and the syndrome was analyzed. The decoction of rhubarb root and rhizome in classical prescriptions can be divided into three categories: simultaneous decoction, decoction later, and other methods (impregnation in Mafei decoction, decoction with water from the well spring first taken in the morning, and pills). If it enters the blood level or wants to slow down, rhubarb root and rhizome should be decocted at the same time with other drugs. If it enters the qi level and wants to speed up, rhubarb root and rhizome should be decocted later. If it wants to upwardly move, rhubarb root and rhizome should be immersed in Mafei decoction. If it wants to suppress liver yang, rhubarb root and rhizome should be decocted with water from the well spring first taken in the morning. If the disease is prolonged, rhubarb root and rhizome should be taken in pill form. The dosing methods of rhubarb root and rhizome can be divided into five categories: draught, twice, three times, before meals, and unspecified. For acute and serious illnesses with excess of pathogenic qi and adequate vital qi, we choose draught. For gastrointestinal diseases, we choose to take the medicine twice. For achieving a moderate and long-lasting effect, we choose to take the medicine three times. If the disease is located in the lower part of the heart and abdomen, we choose to take it before meals. The use of rhubarb root and rhizome in clinical practice requires the selection of the appropriate decoction and dosing methods according to the location of the disease, the severity of the disease, the patient′s constitution, and the condition after taking the medicine.

Chronic fatigue syndrome (CFS) is a common problem in military maritime navigation, which greatly affects the safety of military missions. The use of animal models to carry out research on the mechanism of CFS and treatment measures is a common method. This paper systematically introduced the construction methods of CFS models such as single-factor and multi-factor models, summarized common evaluation indicators of CFS, including behavioral and biochemical indicators, and summed up key characteristics of CFS animal models in military oceanic navigation combined with common causes of CFS in military contexts, such as prolonged continuous work, high-intensity physical activity, sleep deprivation, psychological stress, and extreme environmental conditions. The key characteristics of the animal models included, but not limited to, chronic fatigue, sleep disorders, impaired cognitive function, psychological stress responses, and abnormal biochemical indicators. Furthermore, this article identified future research directions for CFS animal models in military oceanic navigation to enhance the application value of the models and provide robust support for the health protection and disease prevention of military personnel.

ObjectiveTo analyze the epidemiological characteristics and trends of hand-foot-mouth disease (HFMD) in Hangzhou, so as to provide an evidence for developing effective prevention and control measures and evaluating the control effects. MethodsThe incidence data of HFMD in Hangzhou were collected from the Infectious Disease Reporting Information Management System of China Information System for Disease Control and Prevention. Descriptive epidemiology was applied to analyze the temporal, spatial and demographic distribution characteristics and etiology monitoring results of HFMD cases in Hangzhou from 2010 to 2023. Joinpoint regression model was used to analyze the trends of incidence rate of HFMD. Furthermore, circular distribution method was utilized to calculate the incidence peak of HFMD. ResultsFrom 2010 to 2023, the average annual reported incidence rate of HFMD in Hangzhou was 138.85/100 000, the proportion of severe cases was 0.04%, the mortality rate was 0.01/100 000, and the case fatality rate was 5.30/100 000. Both the total incidence rate and the incidence rate by sex showed an increasing trend. The annual reported incidence rate in males (158.72/100 000) was higher than that in females (117.61/100 000). The reported incidence rate showed a significant seasonal characteristic, with summer being the peak of epidemic. The results of surveillance samples suggested that the prevalence of HFMD in Hangzhou is characterized by the co-existence of multiple pathogens, with EV-A71 and CV-A16 being the dominant pathogens in the previous years and CV-A6 being the dominant pathogen since 2018. The proportion of EV-A71 in severe cases (77.19%) was higher than that in ordinary cases (15.37%), in addition, its proportion in ordinary cases, severe cases, and fatal cases all showed a decreasing trend. ConclusionThe incidence rate of HFMD in Hangzhou is still high, so it’s still necessary to continue to strengthen the prevention and control measures for key populations. In recent years, CV-A6 has been the main prevalent pathogen in Hangzhou. Further efforts in pathogen detection and analysis should be enhanced in the future.

目的：筛选果蝇Zeste基因增强子同源物2（EZH2）基因上游miRNA及lncRNA，分析其在胃癌细胞中的表达并验证其间的靶向关系，探讨它们对胃癌细胞增殖、迁移和凋亡的影响。方法：通过ENCORI、miRDB和Target Scan数据库查询并分析、筛选EZH2上游miRNA（has-miR-450b-5p），ENCORI数据库和DAINA数据库筛选has-miR-450b-5p上游lncRNA（lncRNA NEAT1），预测hsa-miR-450b-5p、lncRNA NEAT1与EZH2之间的结合位点，双荧光素酶报告基因实验验证hsa-miR-450b-5p与lncRNA NEAT1的结合关系。采用qPCR和WB法检测lncRNA NEAT1和EZH2在正常胃黏膜细胞（GES-1）与胃癌细胞（MGC-803、SGC-7901和MKN-28）中的表达量。按转染物的不同将MGC-803和SGC-7901细胞分为hsa-miR-450b-5p-mimic组、mimic-NC组、si-NEAT1组和si-NC组，转染36~48 h后qPCR法验证过表达及敲减效果；通过qPCR、WB法检测观察过表达hsa-miR-450b-5p对细胞中lncRNA NEAT1和EZH2 mRNA、蛋白表达的影响，以及敲减lncRNA NEAT1对hsa-miR-450b-5p和EZH2 mRNA表达的影响；CCK-8法、划痕愈合实验和流式细胞术分别检测敲减EZH2或敲减lncRNA NEAT1对细胞增殖、迁移和凋亡能力的影响。结果：生物信息学分析筛选获得EZH2上游miRNA和lncRNA为has-miR-450b-5p和lncRNA NEAT1，双荧光素酶报告基因实验验证了两者间存在靶向关系。lncRNA NEAT1和EZH2 mRNA、蛋白在胃癌细胞中均呈高表达（均P<0.05）。与mimic-NC组相比，hsa-miR-450b-5p-mimic组MGC-803、SGC-7901细胞中miR-450b-5p水平均显著升高，而EZH2 mRNA、蛋白和lncRNA NEAT1的表达量均显著降低（P<0.05或P<0.01）；与si-NC组相比，si-NEAT1组MGC-803、SGC-7901细胞中lncRNA NEAT1和EZH2 mRNA的表达量均显著降低（均P<0.01），SGC-7901细胞中hsa-miR-450b-5p表达量显著升高（P<0.05）。敲减EZH2或敲减lncRNA NEAT1后，MGC-803、SGC-7901细胞的增殖、迁移能力均显著降低（均P<0.01）。结论：lncRNA NEAT1 和EZH2在胃癌细胞中均呈高表达，lncRNA NEAT1可通过hsa-miR-450b-5p促进EZH2的表达并提高胃癌MGC-803和SGC-7901细胞的增殖和迁移能力。