ObjectiveTo evaluate the antitumor activity of Periplaneta americana extract（PAE） against human-derived lung adenocarcinoma organoids（LUAD-PDOs） and to elucidate its potential mechanism based on transcriptomics. MethodsFresh tumor and adjacent normal tissues from patients with LUAD were collected to construct LUAD-PDOs and normal lung organoid（Nor-PDOs） models using 3D organoid culture technology. The effective intervention concentration of PAE was determined using the cell counting kit-8（CCK-8） assay. Experimental groups included the model group（LUAD-PDOs）， normal group， model administration group（LUAD-PDOs+PAE）， and normal administration group（Nor-PDOs+PAE）. Hematoxylin-eosin（HE） staining was used to observe the pathological structures of PDOs， immunohistochemistry（IHC） was performed to detect the expressions of the proliferation marker Ki-67 and lung adenocarcinoma differentiation markers cytokeratin-7（CK-7） and Napsin A， TUNEL staining was applied to detect cell apoptosis. RNA sequencing（RNA-Seq） was conducted to identify differentially expressed genes（DEGs）， followed by Gene Ontology（GO）， Kyoto Encyclopedia of Genes and Genomes（KEGG）， and Gene Set Enrichment Analysis（GSEA）， alongside protein-protein interaction（PPI） network analysis to screen core mechanisms. Finally， key targets were validated by integrating external database analysis with immunofluorescence（IF）. ResultsNor-PDOs and LUAD-PDOs that highly recapitulated the pathological characteristics of the primary tissues were successfully established. The CCK-8 assay determined that the effective intervention concentration of PAE was 16 g·L^-1. Morphological observation showed that Nor-PDOs exhibited lumen-forming structures， whereas LUAD-PDOs displayed dense， solid structures. CCK-8 and TUNEL assays revealed that， compared with the model group， PAE intervention inhibited the proliferation of LUAD-PDOs and promoted apoptosis in LUAD cells， while showing no significant effect on the viability of Nor-PDOs. Transcriptomic analysis identified 719 DEGs that were significantly reversed after PAE intervention（347 up-regulated and 372 down-regulated）（P<0.05）. GO enrichment analysis indicated that DEGs in the model administration group were significantly enriched in biological processes related to cell cycle regulation compared to the model group. KEGG pathway analysis revealed that PAE affected pathways related to proliferation and metabolism， including pathways in cancer and the p53 signaling pathway. GSEA further confirmed that PAE significantly enhanced the activity of the p53 signaling pathway（P<0.05）. PPI network analysis indicated that breast cancer type 1 susceptibility protein（BRCA1） and checkpoint kinase 1（CHEK1） were the core down-regulated targets in the p53 pathway. IF verified the high expression of BRCA1 and CHEK1 in LUAD-PDOs and their significant downregulation after PAE intervention（P<0.05）. Furthermore， survival analysis based on The Cancer Genome Atlas（TCGA） database indicated that low expression of BRCA1 and CHEK1 was significantly associated with prolonged overall survival in patients with LUAD（P<0.05）. ConclusionPAE effectively inhibits proliferation of LUAD-PDOs and promotes their apoptosis， its anti-tumor mechanism is potentially associated with the activation of the p53 signaling pathway， with BRCA1 and CHEK1 genes likely serving as key downstream targets for the effects of PAE.

Objective To investigate the influencing factors of postoperative complications with Clavien-Dindo grade ≥ Ⅲ in elderly patients with esophageal cancer and construct a line graph prediction model.Methods 376 esophageal cancer patients who underwent radical esophagectomy in our hospital from January 2021 to December 2022 were selected as the study subjects.According to the Claven Mindo surgical complication grading system,the groups were divided into two groups:Clavien Mindo grading Grade Ⅲ group(93 cases)and Clavien Mindo grading＜Grade Ⅲ group(283 cases).Univariate analysis and multifactor Logistic regression were used to analyze the influencing factors of postoperative complications of esophageal cancer with Clavien-Dindo grade ≥ Ⅲ,and establish a risk prediction column chart model using R 3.5.1 software;The discrimination and calibration of the area verification model under the ROC curve;Use calibration curves and Hosmer Lemeshoe goodness of fit tests to evaluate the fit of the column chart model.Results 376 patients with esophageal cancer,93 patients with Clavien-Dindo grade ≥ Ⅲ had complications,accounting for 24.73％.The results of univariate analysis showed that chronic respiratory disease,intraoperative blood loss,surgical time,ASA grading,prognostic nutritional index,and Charlson comorbidities index were significantly correlated with postoperative complications in elderly esophageal cancer patients with Clavien Dundo grading ≥ grade Ⅲ(P＜0.05).The results of the logistic multiple regression model showed that intraoperative bleeding volume,surgical time,ASA grading,prognostic nutritional index,and Charlson Comorbidity Index were independent risk factors for postoperative complications in elderly esophageal cancer patients with Clavien Dundo grading≥grade Ⅲ(all P＜0.05),Based on five variables,a nomogram prediction model for postoperative complications in elderly patients with esophageal cancer with Clavien-Dindo grade ≥ Ⅲ was constructed.ROC curve analysis verified that the area under ROC curve was 0.835(0.794～0.876),indicating a good differentiation of the model.The calibration curve is drawn.The slope of the calibration curve is close to 1,and the predicted probability is basically consistent with the actual probability.Hosmer-Lemeshow goodness of fit test,x2=5.737,df=8,P=0.676.The analysis results of decision curve show that the threshold probability is less than 0.8 and the net benefit rate is greater than 0.Conclusion The prediction model based on 5 independent risks can reliably predict the probability of postoperative complications with Clavien-Dindo grade ≥ Ⅲ,which is helpful to provide targeted preventive measures for high-risk patients and reduce the occurrence of complications with Clavien-Dindo grade≥Ⅲ.

Objective To investigate the influencing factors of postoperative complications with Clavien-Dindo grade ≥ Ⅲ in elderly patients with esophageal cancer and construct a line graph prediction model.Methods 376 esophageal cancer patients who underwent radical esophagectomy in our hospital from January 2021 to December 2022 were selected as the study subjects.According to the Claven Mindo surgical complication grading system,the groups were divided into two groups:Clavien Mindo grading Grade Ⅲ group(93 cases)and Clavien Mindo grading＜Grade Ⅲ group(283 cases).Univariate analysis and multifactor Logistic regression were used to analyze the influencing factors of postoperative complications of esophageal cancer with Clavien-Dindo grade ≥ Ⅲ,and establish a risk prediction column chart model using R 3.5.1 software;The discrimination and calibration of the area verification model under the ROC curve;Use calibration curves and Hosmer Lemeshoe goodness of fit tests to evaluate the fit of the column chart model.Results 376 patients with esophageal cancer,93 patients with Clavien-Dindo grade ≥ Ⅲ had complications,accounting for 24.73％.The results of univariate analysis showed that chronic respiratory disease,intraoperative blood loss,surgical time,ASA grading,prognostic nutritional index,and Charlson comorbidities index were significantly correlated with postoperative complications in elderly esophageal cancer patients with Clavien Dundo grading ≥ grade Ⅲ(P＜0.05).The results of the logistic multiple regression model showed that intraoperative bleeding volume,surgical time,ASA grading,prognostic nutritional index,and Charlson Comorbidity Index were independent risk factors for postoperative complications in elderly esophageal cancer patients with Clavien Dundo grading≥grade Ⅲ(all P＜0.05),Based on five variables,a nomogram prediction model for postoperative complications in elderly patients with esophageal cancer with Clavien-Dindo grade ≥ Ⅲ was constructed.ROC curve analysis verified that the area under ROC curve was 0.835(0.794～0.876),indicating a good differentiation of the model.The calibration curve is drawn.The slope of the calibration curve is close to 1,and the predicted probability is basically consistent with the actual probability.Hosmer-Lemeshow goodness of fit test,x2=5.737,df=8,P=0.676.The analysis results of decision curve show that the threshold probability is less than 0.8 and the net benefit rate is greater than 0.Conclusion The prediction model based on 5 independent risks can reliably predict the probability of postoperative complications with Clavien-Dindo grade ≥ Ⅲ,which is helpful to provide targeted preventive measures for high-risk patients and reduce the occurrence of complications with Clavien-Dindo grade≥Ⅲ.

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 morbidity and mortality rate of cardiogenic stroke caused by atrial fibrillation is high,and anticoagulant therapy is the first therapeutic choice in the management of atrial fibrillation.International normalized ratio of warfarin should be strictly controlled at 2.0-3.0.The efficacy of direct oral anticoagulants for preventing embolism events is non-inferior to warfarin,but the risk of bleeding is significantly lower.Severe renal function decline has a great impact on the efficacy and safety of oral anticoagulants.The use of oral anticoagulants in patients with end-stage renal disease is now still controversial.This article reviews the research progress of oral anticoagulant therapy in patients with non-valvular atrial fibrillation complicated with end-stage renal disease.

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.

The morbidity and mortality rate of cardiogenic stroke caused by atrial fibrillation is high,and anticoagulant therapy is the first therapeutic choice in the management of atrial fibrillation.International normalized ratio of warfarin should be strictly controlled at 2.0-3.0.The efficacy of direct oral anticoagulants for preventing embolism events is non-inferior to warfarin,but the risk of bleeding is significantly lower.Severe renal function decline has a great impact on the efficacy and safety of oral anticoagulants.The use of oral anticoagulants in patients with end-stage renal disease is now still controversial.This article reviews the research progress of oral anticoagulant therapy in patients with non-valvular atrial fibrillation complicated with end-stage renal disease.

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.

Objective To explore the differences in scapular motion and shoulder function between patients suffering from rotator cuff tears(RCT)with and without type Ⅲ scapular dyskinesis(SD).Meth-ods Between September 2021 and March 2023,sixteen patients suffering from rotator cuff tears with SD(SD group)and 17 counterparts without SD(non-SD group)were recruited from the Sports Hospital of the General Administration of Sport of China.Their scapular motion was assessed by measuring three parameters in the X-rays,including scapular spine line(LSS),scapular upward rotation angle(SU-RA),and coracoid upward shift distance(CUSD).Moreover,their shoulder range of motion in flexion,abduction and external rotation were recorded,and further evaluated using the Pain Visual Analog Scale(VAS)and American Shoulder and Elbow Surgeons Score(ASES).Results No significant differenc-es were found between the two groups in the average score of SURA,CUSD and LSS at 0°～30° shoul-der abduction,or in that of CUSD and LSS at 60°～90°shoulder abduction.However,the average SU-RA score of the SD group at 60°～90°shoulder abduction was significantly greater than the other group(P<0.05).The shoulder ranges of motion during active flexion,abduction and external rotation were significantly smaller in the SD group than in the non-SD group(P<0.05).Moreover,the average VAS score in the SD group was significantly higher than the non-SD group(P<0.05),while the average ASES score was significantly lower than the latter group(P<0.05).Conclusions RCT patients type III SD exhibits greater scapular upward rotation during shoulder abduction compared to those without SD.Moreover,the former patients suffer from more severe pain and have worse shoulder range of motion and functional performance than the latter.

Objective:To evaluate the correlation between the degree of posterior longitudinal ligament (PLL) injury and various parameters of vertebral body injury in patients with thoracolumbar burst fractures.Methods:A total of 48 patients with thoracolumbar burst fractures were admitted to the Spine Surgery Center of the Second Affiliated Hospital of Inner Mongolia Medical University between December 2022 and January 2024. The cohort consisted of 31 males and 17 females, with a mean age of 44.1±11.8 years (range, 18-65 years). Based on the PLL injury grading method proposed by Sun Zhaoyun, patients were classified into three groups: mild, moderate, and severe. However, due to an insufficient number of patients in the severe group ( n=3), the moderate and severe groups were combined for statistical analysis, resulting in two groups: mild, and moderate-to-severe. Patient demographic and clinical data were collected. Local kyphosis (LK), inversion angle (IA), horizontal rotation angle (HRA), increased interspinous distance (IISD), anterior vertebral body compression ratio (AVBCR), posterior vertebral body compression ratio (PVBCR), middle vertebral body compression ratio (MVBCR), the ratio of height of bone fragment (RHBF), the ratio of width of bone fragment (RWBF), and mid-sagittal canal diameter compression ratio (MSDCR) were measured. Statistical analyses were performed using SPSS 25.0. Categorical variables were expressed as frequency (percentage) and analyzed using chi-square and Fisher exact tests. Continuous variables were tested for normality, with non-normally distributed data analyzed using the rank-sum test and expressed as median (interquartile range). Multivariate logistic regression analysis was performed to identify independent risk factors, and receiver operating characteristic (ROC) curves were plotted to calculate the area under the curve (AUC) to evaluate predictive performance. Results:Among the 48 patients, only 3 were found to have severe PLL injury, necessitating the combination of the moderate and severe groups for statistical purposes. Patients in the moderate-to-severe group demonstrated significantly higher AVBCR, PVBCR, RHBF, MVBCR, MSDCR, and IA compared to the mild group ( P<0.05). Multivariate logistic regression identified AVBCR, PVBCR, MSDCR, and IA as independent risk factors for moderate-to-severe PLL injury ( OR>1, P<0.05). ROC curve analysis revealed that AVBCR, PVBCR, MSDCR, IA, and their combined index could effectively predict moderate-to-severe PLL injury ( P<0.05). Notably, the combined index showed superior predictive performance (AUC=0.970) compared to individual parameters. Threshold values were determined as follows: AVBCR>45.30%, PVBCR>12.17%, MSDCR>27.13%, IA>5.90°, and the combined index >0.61, indicating PLL damage. Conclusion:AVBCR, PVBCR, MSDCR, IA, and their combined index are significantly associated with moderate-to-severe PLL injury in thoracolumbar burst fractures. The combined index demonstrates superior predictive ability compared to single parameters, providing a reliable tool for assessing PLL integrity.