Objective To reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. Methods Publications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. Results A marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. Conclusion Over the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.

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 concept of "qi deficiency with stagnation" refers to a pathological state characterized by the depletion of primordial qi， impaired qi transformation， and the development of internal stagnation. Under the cyclic chemotherapy regimen in oncology， chemotherapy-induced myelosuppression follows a progressive pathological course from qi deficiency to increasing stagnation. This sequential evolution from mild to severe myelosuppression closely aligns with the dynamic syndrome differentiation and treatment framework of "qi deficiency with stagnation". "Qi deficiency" reflects the gradual depletion of qi， blood， and essence， while "stagnation" refers to the accumulation of phlegm， turbid dampness， and blood stasis. These two components interact reciprocally， forming a vicious cycle where deficiency leads to stagnation， and stagnation further damages the healthy qi. In the early stage of mild myelosuppression， chemotoxicity begins to accumulate in the bone marrow， leading to qi consumption， blood deficiency， yin injury， and the gradual formation of turbid phlegm and damp stagnation. In the advanced stage of severe myelosuppression， the accumulation of toxicity causes qi sinking， exhaustion of essence， and marrow depletion， along with blood stasis obstructing the collaterals. Treatment strategies should be based on syndrome differentiation， with an emphasis on assessing the severity of the condition， balancing deficiency and excess， and achieving both symptomatic relief and root cause resolution.

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.

Based on the theory of qi， blood and fluids， and taking into account of the pathogenesis evolution process from constraint to phlegm， stasis and then mass in pulmonary nodules， an attempt has been made to construct a three-dimensional differentiation system for pulmonary nodules from the dimensions of time and space. The temporal progression of the early， middle， and late stages of pulmonary nodules reflects the pathological changes from constraint to phlegm and then stasis in the metabolism disorders of qi， blood and fluid. The spatial structures such as size， density， and morphology of pulmonary nodules reflect the pathological states of the duration， severity， and primary and secondary conditions of qi， blood and fluid metabolism disorders. Based on the temporal progression， the therapeutic principles have been proposed， which are dispelling pathogenic factors and promoting the use of beneficial factors to interrupt the growth momentum in the early stage， removing turbidity and dispersing phlegm to reduce the degree of nodules in the middle stage， and dispersing nodulation and eliminating abnormalities in the late stage. Based on the spatial structures， the suggested therapeutic methods are using wind herbs， employing multiple approaches to treat phlegm， and promoting blood circulation to resolve stasis， so as to provide theoretical reference for the systematic diagnosis and treatment of pulmonary nodules in traditional Chinese medicine.

ObjectiveTo observe the effect of Tongxie Yaofang on the function of tumor-related natural killer （NK） cells under chronic stress and explore the possible molecular mechanism. MethodFifty SPF-grade BABL/C male mice were randomized into normal， model， and low-， medium-， and high-dose （6.825， 13.65， and 27.3 g·kg^-1， respectively） Tongxie Yaofang groups， with 10 mice in each group. Other groups except the blank group were subjected to 7 days of chronic restraint stress， and then forced swimming and tail suspension tests were carried out to evaluate the modeling performance. After the successful modeling， rats in Tongxie Yaofang groups were administrated with low-， medium-， and high-doses of Tongxie Yaofang by gavage， while those in the other groups were administrated with normal saline by gavage. After 14 days， each group of mice was inoculated with subcutaneous colon cancer to establish the model of colon cancer under chronic stress. The pathological changes of the tumor tissue in each group of mice were observed using hematoxylin-eosin （HE） staining. The content of CD49b-positive cells in the peripheral blood and tumor tissue of mice was measured by flow cytometry. Enzyme-linked immunosorbent assay （ELISA） was employed to measure the content of molecules associated with NK cell activation in the peripheral blood. Western blot was employed to determine the protein levels of major histocompatibility complex class Ⅰ polypeptide-related sequences A and B （MICA+MICB） and UL-16-binding protein 1 （ULBP1） in the tumor tissue. ResultCompared with the normal group， the model group showed a decrease in 5-hydroxytryptamine （5-HT） content and an increase in corticosterone （CORT） content in the serum （P<0.05）. Compared with the model group， Tongxie Yaofang increased the 5-HT content and decreased the CORT content （P<0.05， P<0.01）. Compared with the normal group， the modeling increased the tumor volume and weight （P<0.05）， while Tongxie Yaofang inhibited such increases with no statistical significance. The tumor cells in the model group presented neat arrangement， irregular shape， uneven size， obvious atypia， common nuclear division， and small necrotic area， and blood vessels were abundant surrounding the tumor cells. Compared with the model group， Tongxie Yaofang groups showed sparse arrangement of tumor cells， different degrees of patchy necrosis areas in the tumor， and karyorrhexis， dissolution， and nuclear debris in the necrotic part. Compared with the normal group， the model group showed reduced CD49b-positive cells in the peripheral blood and tumor tissue （P<0.01）. Compared with the model group， Tongxie Yaofang increased CD49b-positive cells （medium dose P<0.01， high dose P<0.05， P<0.01）. Compared with the normal group， the modeling lowered the serum levels of granzymes-B （Gzms-B）， perforin （PF）， interferon （IFN）-γ， and tumor necrosis factor （TNF）-α （P<0.05， P<0.01）. Compared with the model group， low-dose Tongxie Yaofang elevated the serum levels of PF， Gzms-B， and TNF-α （P<0.05， P<0.01）， and medium-dose Tongxie Yaofang elevated the serum levels of Gzms-B， PF， IFN-γ， and TNF-α （P<0.05， P<0.01）. In addition， high-dose Tongxie Yaofang elevated the serum levels of PF， IFN-γ， and TNF-α （P<0.01）. Compared with the normal group， the model group presented down-regulated protein level of ULBP1 （P<0.05）. Compared with the model group， low-， medium-， and high-dose Tongxie Yaofang up-regulated the protein level of ULBP1 （P<0.05， P<0.01）， and medium- and high-dose Tongxie Yaofang up-regulated the protein level of MICA+MICB （P<0.05， P<0.01）. ConclusionTongxie Yaofang may promote NK cell activation by up-regulating the expression of MICA+MICB and ULBP1， thereby delaying the progression of colon cancer under chronic stress.

Objective To establish an animal model of lung adenoma in BALB/c mice based on dynamic characterization by micro-computed tomography(CT).Methods Eighty female SPF-grade BALB/c mice were divided randomly into four groups:model low dose group(1 mg/g urethane,iP,once),model medium dose group(1 mg/g urethane,ip,once a week,followed by 2 weeks),model high dose group(1 mg/g urethane,ip,once a week,followed by 4 weeks),and blank group(equal volume of saline).Growth of lung nodules in the mice was monitored regularly using Micro-CT.Three-dimensional images of the lungs were drawn using the Analyze 12.0 system,and lung tissues were taken for histopathological examination(hematoxylin and eosin).Results Lung nodules with round high-density shadows were observed at week 11 in all model groups compared with the findings in the blank group.The rate of nodule formation increased with increasing modeling weeks,with rates of nodule formation in the model high,medium,and low dose groups of 93.8%,93.8%,and 87.5%,respectively,at week 21.Most mice had two to four,followed by one,and one to two nodules,respectively.The average maximum diameter of the lung nodules in the low dose group was significantly higher than the diameters in the medium-and high-dose groups(P＜0.05),but there was no significant difference in lung nodule volume among the three groups.Regarding pathological type,hematoxylin and eosin staining revealed that the tumors in all the model groups were lung adenomas.Conclusions Lung adenomas were successfully induced in all urethane dose groups of mice and growth of the lung nodules could be characterized by micro-CT.The rate of nodule formation was highest in the medium dose group,which developed a moderate number of lung adenomas and provided a stable model,and was thus considered the most suitable model for the study of lung adenomas in mice.

Pulmonary nodule is a key window for moving ahead the diagnosis and treatment of lung cancer. Traditional Chinese medicine （TCM） can delay the transformation of lung nodules into lung cancer， improve the prognosis of patients， effectively fill the treatment gap during the follow-up period of pulmonary nodules， and has been applied it in the whole cycle and multi-dimensional management of pulmonary nodules. This paper discussed the construction ideas and feasible paths of the whole process management diagnosis and treatment system of pulmonary nodules in TCM， proposed the diagnosis and treatment database of TCM for pulmonary nodules based on the social module of “family-community-hospital”. Through artificial intelligence， we can develop， improve and promote the multi-level and multi-modal “disease-symptom combination” risk prediction model and effectiveness evaluation system of pulmonary nodules. At the same time， the biological connotation of the prevention and treatment of pulmonary nodules by TCM is excavated， which provided empirical evidence for the construction of TCM diagnosis and treatment system， in order to further improve the quality and diagnosis and treatment level of the whole course management of pulmonary nodules.

During the carcinoma transformation of colitis， the imbalance of “metabolic-immune interaction” resulted from abnormal energy and metabolic substrates flow and direction was the key process， which caused by intercellular metabolic competition. Based on traditional Chinese medicine （TCM） theory and clinical research， we found that “fire failing to warm earth” is the key pathogenesis of colon-cancer transformation. “Fire” was a synonym for TCM to understand the energy metabolism， combined modern medical research findings， we thought energy metabolism disorders was a microcosmic manifestation of the “fire decline”， while abnormal immune function was the biological basis of “earth deficiency”. The imbalance between “metabolism-immune interaction” and the “fire failing to warm earth” pathogenesis of colitis-cancer transformation demonstrated the different understanding of the same pathological mechanism between western medicine and TCM. For treatment， it could be effectivce to delay the transformation of colitis-cancer by synergistically regulated the energy metabolism - “replenish fire” and enhanced the immune function - “nourish earth”， which was called the methods of replenishing fire to nourish earth.