Pulmonary nodules (PN) are defined as focal, roundish or irregular opacities with a diameter of ≤3 cm detected by imaging examinations. Most patients present no obvious clinical symptoms and are often incidentally identified during physical examinations. Currently, clinical management is plagued by multiple challenges, including difficulties in differentiating benign from malignant nodules, anxiety induced by overdiagnosis, and dilemmas in the selection of follow-up and treatment strategies. Studies have indicated that the occurrence and progression of PN are closely associated with the local microenvironment, among which the "immune-metabolic axis" acts as a core regulatory link. The "immune-metabolic axis" refers to a bidirectional regulatory network formed by the interaction and coordinated regulation between immune cells and metabolic components in the local microenvironment of PN. Through the interactive regulation of immune cell infiltration and metabolites, this axis directly affects the progression and outcome of nodules, and also serves as a pivotal entry point for investigating the pathological mechanisms of PN. Endowed with the characteristics of holistic regulation, multi-components and multi-targets, Traditional Chinese Medicine (TCM) exhibits significant potential in regulating immune cell functions and intervening in abnormal metabolic pathways. This paper systematically elaborates on the core regulatory mechanisms of the PN-related "immune-metabolic axis", clarifies the key targets and mechanisms through which TCM regulates this axis, aiming to deepen the understanding of the pathological mechanisms of PN and provide new strategies for clinical diagnosis and treatment.
ObjectiveTo establish a hypertension prediction model for middle-aged and elderly people in China and to use the basic public health service database for performance validation. MethodsThe literature related to hypertension was retrieved from the internet. Using meta-analysis to assess the effect value of influencing factors. Statistically significant factors, which were also combined in the database, were extracted as the predictors of the models. The predictors’ effect values were logarithmarithm-transformed as the parameters of the Logit function model and the risk score model. Participants who were never diagnosed with hypertension at the physical examination of health service project of Hongguang Town Health Center in Pidu District of Chengdu from January 1, 2017, to January 1, 2022, were considered as the external validation group. ResultsA total of 15 original studies were involved in the meta-analysis and 11 statistically significant influencing factors for hypertension were identified, including age, female, systolic blood pressure, diastolic blood pressure, BMI, central obesity, triglyceride, smoking, drinking, history of diabetes and family history of hypertension. Of 4997 qualified participants, 684 individuals were identified with hypertension during the five-years follow-up. External validation indicated an AUC of 0.571 for the Logit function model and an AUC of 0.657 for the risk score model. ConclusionIn this study, we developed two different prediction models based on the results of meta-analysis. National basic public health service database is used to verify the models. The risk score model has a better prediction performance, which may help quickly stratify the risk class of the community crowd and strengthen the primary-level assistance system.