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.

Objective: To evaluate the clinical efficacy of digitally guided precision crown lengthening in secondary aesthetic rehabilitation cases, and to provide a clinical reference for digitally guided crown lengthening procedures and secondary aesthetic restorations. Methods: We present a case of a patient with tetracycline-stained teeth, partial detachment of anterior resin veneers, and gingival margin discrepancies. The patient underwent digitally guided precision crown lengthening followed by secondary aesthetic rehabilitation. Multimodal data, including intraoral, facial, and CBCT scans, were integrated to construct a four-dimensional virtual patient model (incorporating teeth, face, bone, and occlusion) for surgical planning and 3D-printed guide fabrication. Secondary aesthetic restoration was performed after achieving stable post-surgical outcomes. Based on this case, we conducted a detailed analysis and reviewed relevant literature on crown lengthening in secondary aesthetic rehabilitation. Results: The gingival contour of the anterior teeth exhibited significant improvement, with enhanced symmetry and stable gingival margin positioning that closely matched the preoperative design. The crown lengthening procedure demonstrated high precision, and the final outcome was aesthetic and functional. Literature review indicated that secondary restorations frequently present challenges such as gingival contour discrepancies and inflammation. Aesthetic crown lengthening in the anterior region should optimize both soft and hard tissue morphology to meet aesthetic standards, with digital technology improving procedural accuracy. Conclusion Precision crown lengthening effectively addresses gingival margin discrepancies in secondary aesthetic rehabilitation, ensuring stable gingival positioning and superior aesthetic outcomes. This approach is particularly suitable for cases with high aesthetic demands.

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.

Objective To explore the changes in Th1 cell,B cell and related gene expression during the healing of diabetic foot ulcers(DFU).Methods Lesional skin tissues from DFU patients surgically treated in our hospital from January 2022 to January 2023 were selected.DFU associated gene expression data were collected from GSE80178 and GSE143735 datasets.The expressions of genes(DEGs)between ulcer and no-ulcer patients were identified.Bioinformatics methods were used to identify abnormal immune cells and key genes.Finally,the key results were detected by RT-qPCR and flow cytometry.Results A total of 548 common DEGs were identified in two datasets.In 14 co-expression modules,darkgrey and darkgreen were most related to ulcer,which were mainly associated with the regulation of enzyme activity,immune function and endocrine regulation.Flow cytometry showed that Th1 and B cells were highly infiltrated in ulcer tissue and decreased in healing tissue.MMP13,S100A9 and STAT4 were involved in immune signaling pathways.MMP13 and STAT4 were lowly expressed in healed ulcers,whereas S100A9 was highly expressed.Conclusion Reduced levels of Th1 and B cell infiltration may promote DFU healing.

Objective:To investigate the effects of cervical cold knife conization (CKC) on preterm delivery, other pregnancy complications and neonatal outcomes, and explore the relationship between preterm delivery risk and the depth and volume of conization.Methods:The clinical data and pregnancy outcomes of 272 women who underwent CKC in Peking Union Medical College Hospital from January 2002 to March 2018 (conization group) and 1 647 pregnant women who gave birth in Peking Union Medical College Hospital during January to December 2019 (control group) were collected. The preterm delivery, premature rupture of membranes, other pregnancy complications and neonatal outcomes of the two groups were compared, and the relationship between the depth and volume of conization and the risk of preterm delivery in postoperative singleton pregnancy was analyzed.Results:(1) There were no significant differences between the two groups in delivery age, parity, proportion of singleton pregnancy, proportion of assisted reproductive technology (all P>0.05). (2) The rate of preterm delivery in the conization group was significantly higher than that in the control group [14.8% (39/264) vs 5.7% (91/1 589); χ2=28.397, P<0.001]. There were still significant differences in preterm delivery rates between the two groups at <34 weeks and 34-37 weeks (all P<0.01). There was no significant difference in the incidence of premature rupture of membrane between the two groups [23.5% (62/264) vs 23.4% (372/1 589); χ2=0.001, P=0.979], but the incidence of preterm premature rupture of membrane in the conization group was significantly higher than that in the control group [11.4% (30/264) vs 2.2% (35/1 589); χ2=56.132, P<0.001]. (3) The rate of cesarean section in the conization group was higher than that in the control group [59.6% (162/272) vs 38.8% (639/1 647); χ2=41.377, P<0.001]. The birth weight of preterm infants in the conization group was significantly higher than that in the control group [(2 409±680) vs (2 150±684) g; t=2.184, P=0.030]. However, there were no statistically significant differences in the incidence of gestational diabetes mellitus, hypertensive disorders in pregnancy, the birth weight of full-term infants, incidence of small for gestational age infant and neonatal intensive care unit admission rate between the two groups (all P>0.05). (4) The preterm delivery rates of coning depth >15 mm, cone size ≥2 cm 3 and cone size <2 cm 3 were higher than that in the control group (all P<0.05). When the coning depth ≤15 mm, the preterm delivery rate in the conization group was higher than that in the control group, but there was no significant difference ( P=0.620). The rate of preterm delivery of pregnant women with coning depth >15 mm was significantly higher than those with coning depth ≤15 mm ( RR=3.084, 95% CI: 1.474-6.453; P=0.001). There was no significant difference in the preterm delivery rate between pregnant women with cone size >2 cm 3 and those with cone size ≥2 cm 3 ( RR=1.700, 95% CI: 0.935-3.092; P=0.077). Conclusion:The risk of preterm delivery and preterm premature rupture of membranes in subsequent pregnancies are increased after cervical CKC, and the risk of preterm delivery is positively correlated with the depth of cervical coning.

Objective:To observe the differences in the positive rate of conjunctival sac microbial culture after different methods of preventing infection before intravitreal injection (IVI).Methods:A prospective case-control study. A total of 1 200 participants with fundus diseases who received IVI injection at Tianjin Medical University Eye Hospital from July 2021 to December 2023 were included. Patients were randomly divided into 6 groups according to eye spot with antibiotic solution 3, 1 and 0 days before IVI and local eye disinfection with povidone-iodine (PVI) 3 min and 30 s before IVI: the first 3 days of antibiotics+3 min PVI group, the first 1 day of antibiotics+3 min PVI group, the first 0 days of antibiotics+3 min PVI group, the first 3 days of antibiotics+30 s PVI group, the first 1 day of antibiotics+30 s PVI group, the first 0 days of antibiotics+30 s PVI group, there were 200 cases in each group. Microbial sampling and cultivation of conjunctival sac were conducted before IVI to compare the differences in positive rates among different groups. Multiple group comparisons were conducted using one-way analysis of variance. The comparison of count data is conducted using χ2 test. Results:Among the 1 200 patients, there were 566 males and 634 females. Age (62.59±13.44) years old. There were 397 cases of diabetes and 482 cases of hypertension. IVI frequency (2.35±2.34). 64 cases were positive for conjunctival sac culture before IVI. The age ( F=1.468), sex composition ratio ( χ2=2.876), diabetes ( χ2=10.002), hypertension ( χ2=6.019), times of IVI ( χ2=4.507), and positive rate of conjunctival sac bacterial culture ( χ2 =6.272) of patients in each group had no statistical significance ( P>0.05). Using the duration of antibiotic application before IVI as a stratified factor, there was no statistically significant difference in the positive rate of conjunctival sac culture between groups with different durations of antibiotic application before IVI [ χ2=0.414, P=0.52, combined odds ratio ( OR)=0.819, 95% confidence interval ( CI) 0.493-1.360]. Using the duration of PVI application as a stratified factor, there was no statistically significant difference in the positive rate of conjunctival sac culture between different PVI disinfection times [ χ2=0.000, P=1.000, combined OR=1.00, 95% CI 0.503-1.988]. Conclusions:Pre IVI treatment with 0.5% PVI for 30 s can inhibit the growth of microbial colonies in the conjunctival sac. The application of local antibiotic eye fluid in the anterior eye of IVI cannot reduce the positive rate of conjunctival sac bacteria.

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.