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.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

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.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Objective To investigate the association between baseline depression and the risk of type 2 diabetes mellitus(T2DM)in middle-aged and elderly people in Chengguan District of Lanzhou.Methods A total of 4471 residents who were followed up in Chengguan District,Lanzhou City from August 2014 to July 2016 in the 2011 REACTION study were selected as the research subjects.According to the 9-item patient health questionnaire(PHQ-9),they were divided into the non-depression group with a score of 0～4 points(ND,n=3827),the mild depression group with a score of 5～10 points(MD,n=546)and the moderate to severe depression group with a score of≥10 points(MSD,n=98).The general data and biochemical indicators of the three groups were compared.The correlation between depression status and glycolipid metabolism indicators was analyzed.The follow-up results of the three groups with different baseline glucose metabolism status populations were compared.Logistic regression was used to analyze the influencing factors of progression in different glucose metabolism populations.Results The married rate in the ND,MD and MSD groups decreased sequentially(P<0.05),while the rate of living alone and the PHQ-9 score increased(P<0.05).The female population,family history of DM,coronary heart disease,LDL-C and TC in the MD group were higher than those in the ND group(P<0.05),while the age,BMI,WHR,FPG and 2 hPG in the MD group were lower than those in the ND group(P<0.05).The family history of DM in the MSD group was higher than those in the ND group(P<0.05),drinking and LDL-C in the MSD group were higher than those in the MD group(P<0.05),the BMI was lower than that in the ND group(P<0.05).Spearman correlation analysis showed that the baseline PHQ-9 score was negatively correlated with FPG level(r=-0.039,P<0.05),and positively correlated with HDL-C and TC(r=0.049,0.031,P<0.05).There was no significant difference in the incidence of pre-DM and T2DM at the end of follow-up among the three groups with different baseline glucose metabolism(P>0.05).Logistic regression analysis showed that after adjusting for confounding factors,the risk of pre-DM and T2DM in normal glucose tolerance people with different depression status and the risk of T2DM in pre-DM patients were not increased.Conclusions Depressive state may not be the main factor affecting the occurrence of T2DM in middle-aged and elderly people in Chengguan District of Lanzhou.

Objective To investigate the association between baseline depression and the risk of type 2 diabetes mellitus(T2DM)in middle-aged and elderly people in Chengguan District of Lanzhou.Methods A total of 4471 residents who were followed up in Chengguan District,Lanzhou City from August 2014 to July 2016 in the 2011 REACTION study were selected as the research subjects.According to the 9-item patient health questionnaire(PHQ-9),they were divided into the non-depression group with a score of 0～4 points(ND,n=3827),the mild depression group with a score of 5～10 points(MD,n=546)and the moderate to severe depression group with a score of≥10 points(MSD,n=98).The general data and biochemical indicators of the three groups were compared.The correlation between depression status and glycolipid metabolism indicators was analyzed.The follow-up results of the three groups with different baseline glucose metabolism status populations were compared.Logistic regression was used to analyze the influencing factors of progression in different glucose metabolism populations.Results The married rate in the ND,MD and MSD groups decreased sequentially(P<0.05),while the rate of living alone and the PHQ-9 score increased(P<0.05).The female population,family history of DM,coronary heart disease,LDL-C and TC in the MD group were higher than those in the ND group(P<0.05),while the age,BMI,WHR,FPG and 2 hPG in the MD group were lower than those in the ND group(P<0.05).The family history of DM in the MSD group was higher than those in the ND group(P<0.05),drinking and LDL-C in the MSD group were higher than those in the MD group(P<0.05),the BMI was lower than that in the ND group(P<0.05).Spearman correlation analysis showed that the baseline PHQ-9 score was negatively correlated with FPG level(r=-0.039,P<0.05),and positively correlated with HDL-C and TC(r=0.049,0.031,P<0.05).There was no significant difference in the incidence of pre-DM and T2DM at the end of follow-up among the three groups with different baseline glucose metabolism(P>0.05).Logistic regression analysis showed that after adjusting for confounding factors,the risk of pre-DM and T2DM in normal glucose tolerance people with different depression status and the risk of T2DM in pre-DM patients were not increased.Conclusions Depressive state may not be the main factor affecting the occurrence of T2DM in middle-aged and elderly people in Chengguan District of Lanzhou.

Objective To observe the value of pseudo-continuous arterial spin labeling(PCASL)MRI for evaluating renal function in patients with renal occupying lesions.Methods Totally 56 patients with single renal occupying lesion were retrospectively enrolled.The left and right side kidneys were divided into normal renal function group(normal group,30 ml/min)and damaged renal function group(damaged group,＜30 ml/min)according to glomerular filtration rate(GFR)measured with 99Tcm-DTPA dynamic renal imaging,respectively.The total renal blood flow(tRBF)and cortical renal blood flow(cRBF)were calculated using total nephrometry and cortical nephrometry based on PCASL MRI,respectively,then GFR,tRBF and cRBF were compared between groups on the same side.Receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the efficacy of tRBF and cRBF for assessing unilateral renal injury.Pearson correlation analysis was performed to observe the correlations of tRBF and cRBF with GFR.Results GFR,tRBF and cRBF in left/right damaged group were all significantly lower than those in ipsilateral normal group(all P＜0.05).AUC of tRBF and cRBF for assessing left renal injury was 0.823 and 0.813,respectively,being not significantly different(P＞0.05).AUC of tRBF and cRBF for assessing right renal injury was 0.940 and 0.922,respectively,being not significantly different(P＞0.05).No obvious correlation of bilateral tRBF nor cRBF with GFR was found(all P＞0.05).Conclusion PCASL MRI could effectively evaluate renal function in patients with renal occupying lesion,and the efficacy of total nephrometry was comparable to that of cortical nephrometry.