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.

Purpose: Cancer has become a significant major public health concern, making the discovery of new cancer markers or therapeutic targets exceptionally important. Elevated expression of tumor necrosis factor receptor superfamily member 12A (TNFRSF12A) expression has been observed in certain types of cancer. This project aims to investigate the function of TNFRSF12A in tumors and the underlying mechanisms. Materials and Methods: Various websites were utilized for conducting the bioinformatics analysis. Tumor cell lines with stable knockdown or overexpression of TNFRSF12A were established for cell phenotyping experiments and subcutaneous tumorigenesis in BALB/c mice. RNA-seq was employed to investigate the mechanism of TNFRSF12A. Results: TNFRSF12A was upregulated in the majority of cancers and associated with a poor prognosis. Knockdown TNFRSF12A hindered the colorectal cancer progression, while overexpression facilitated malignancy both in vitro and in vivo. TNFRSF12A overexpression led to increased nuclear factor кB (NF-κB) signaling and significant upregulation of baculoviral IAP repeat containing 3 (BIRC3), a transcription target of the NF-κB member RELA, and it was experimentally confirmed to be a critical downstream factor of TNFRSF12A. Therefore, we speculated the existence of a TNFRSF12A/RELA/BIRC3 regulatory axis in colorectal cancer. Conclusion TNFRSF12A is upregulated in various cancer types and associated with a poor prognosis. In colorectal cancer, elevated TNFRSF12A expression promotes tumor growth, potentially through the TNFRSF12A/RELA/BIRC3 regulatory axis.

Purpose: Cancer has become a significant major public health concern, making the discovery of new cancer markers or therapeutic targets exceptionally important. Elevated expression of tumor necrosis factor receptor superfamily member 12A (TNFRSF12A) expression has been observed in certain types of cancer. This project aims to investigate the function of TNFRSF12A in tumors and the underlying mechanisms. Materials and Methods: Various websites were utilized for conducting the bioinformatics analysis. Tumor cell lines with stable knockdown or overexpression of TNFRSF12A were established for cell phenotyping experiments and subcutaneous tumorigenesis in BALB/c mice. RNA-seq was employed to investigate the mechanism of TNFRSF12A. Results: TNFRSF12A was upregulated in the majority of cancers and associated with a poor prognosis. Knockdown TNFRSF12A hindered the colorectal cancer progression, while overexpression facilitated malignancy both in vitro and in vivo. TNFRSF12A overexpression led to increased nuclear factor кB (NF-κB) signaling and significant upregulation of baculoviral IAP repeat containing 3 (BIRC3), a transcription target of the NF-κB member RELA, and it was experimentally confirmed to be a critical downstream factor of TNFRSF12A. Therefore, we speculated the existence of a TNFRSF12A/RELA/BIRC3 regulatory axis in colorectal cancer. Conclusion TNFRSF12A is upregulated in various cancer types and associated with a poor prognosis. In colorectal cancer, elevated TNFRSF12A expression promotes tumor growth, potentially through the TNFRSF12A/RELA/BIRC3 regulatory axis.

Purpose: Cancer has become a significant major public health concern, making the discovery of new cancer markers or therapeutic targets exceptionally important. Elevated expression of tumor necrosis factor receptor superfamily member 12A (TNFRSF12A) expression has been observed in certain types of cancer. This project aims to investigate the function of TNFRSF12A in tumors and the underlying mechanisms. Materials and Methods: Various websites were utilized for conducting the bioinformatics analysis. Tumor cell lines with stable knockdown or overexpression of TNFRSF12A were established for cell phenotyping experiments and subcutaneous tumorigenesis in BALB/c mice. RNA-seq was employed to investigate the mechanism of TNFRSF12A. Results: TNFRSF12A was upregulated in the majority of cancers and associated with a poor prognosis. Knockdown TNFRSF12A hindered the colorectal cancer progression, while overexpression facilitated malignancy both in vitro and in vivo. TNFRSF12A overexpression led to increased nuclear factor кB (NF-κB) signaling and significant upregulation of baculoviral IAP repeat containing 3 (BIRC3), a transcription target of the NF-κB member RELA, and it was experimentally confirmed to be a critical downstream factor of TNFRSF12A. Therefore, we speculated the existence of a TNFRSF12A/RELA/BIRC3 regulatory axis in colorectal cancer. Conclusion TNFRSF12A is upregulated in various cancer types and associated with a poor prognosis. In colorectal cancer, elevated TNFRSF12A expression promotes tumor growth, potentially through the TNFRSF12A/RELA/BIRC3 regulatory axis.

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.

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.

ObjectiveTo systematically review the intervention effect of Chinese medicine on the structure and function of testicular Sertoli cells in animal models of impaired spermatogenesis. MethodThe databases， such as China National Knowledge Infrastructure （CNKI），VIP，Wanfang Data，EMbase，and Pubmed，were searched for experimental studies on the effect of Chinese medicine on the structure and function of testicular Sertoli cells in animal models with impaired spermatogenesis. The included studies were evaluated for risks of bias，and the outcome indicators were analyzed with RevMan and Stata software. ResultThirty studies were included，involving 37 randomized controlled trials （RCTs）. As indicated by the Meta-analysis results， compared with the model group，Chinese medicine increased sperm density（SMD=2.42，95% confidence interval（CI）［1.47，3.37］，P<0.000 01）， promoted sperm motility（SMD=2.35，95%CI ［1.70， 2.99］，P<0.000 01）， up-regulated the protein and mRNA levels of Vimentin （related to Sertoli cell cytoskeleton）， elevated the levels of Occludin and Claudin-11 （related to tight junction of blood-testis barrier）， boosted the levels of β-catenin and N-cadherin （related to adherens junction of blood-testis barrier）， raised the level of connexin 43 （Cx43， related to gap junction of blood-testis barrier）， improved the function of Sertoli cells， increased the serum content of Inhibin B （INHB）， and up-regulated the levels of testicular follicle-stimulating hormone receptor （FSHR）， INHB mRNA， androgen-binding protein （ABP） mRNA， transferrin（TF），stem cell factor（SCF），SCF mRNA，glial cell line-derived neurotrophic factor （GDNF），GDNF mRNA，bone morphogenetic protein 4（BMP4），and BMP4 mRNA （P<0.05）. ConclusionChinese medicine can effectively increase sperm density and motility of animal models of impaired spermatogenesis，and improve the structure and function of testicular Sertoli cells. However，affected by the quality of the included studies，the above conclusion needs to be further verified by relevant high-quality studies.