Objective This study aimed to analyze the differences between the distribution of Traditional Chinese Medicine (TCM) syndrome elements and salivary microbiota between the individuals with pulmonary nodules and those without. Additionally, it seeked to explore the potential correlation between the distribution of Traditional Chinese Medicine (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 Traditional Chinese Medicine (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 Traditional Chinese Medicine (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 Traditional Chinese Medicine (TCM) syndrome elements related to disease location were the lung and liver, and the primary Traditional Chinese Medicine (TCM) syndrome elements related to disease nature were yin deficiency and phlegm. In the HC group, the primary Traditional Chinese Medicine (TCM) syndrome elements related to disease location were the lung and spleen, and the primary Traditional Chinese Medicine (TCM) syndrome elements related to disease nature were dampness and qi deficiency. There were differences between the two groups in the distribution of Traditional Chinese Medicine (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 were significant differences 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, 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 Traditional Chinese Medicine (TCM) syndrome elements and the species abundance and composition of salivary microbiota between patients with pulmonary nodules and 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.
Purpose 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. MethodsThe 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 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.