Objective To analyze the effect of releasing the lower pulmonary ligament on right residual lung expansion after right upper lobe resection under different body mass index (BMI) levels. Methods The clinical data of patients who underwent thoracoscopic right upper lobe resection in the First Affiliated Hospital with Nanjing Medical University from 2021 to 2022 were retrospectively analyzed. Patients were divided into a group A (17 kg/m2＜BMI≤23 kg/m2), a group B (23 kg/m2＜BMI≤29 kg/m2) and a group C (BMI＞29 kg/m2) according to BMI. The presence of residual cavity was judged by chest X-ray at 7-10 days after operation, the degree of compensation change of the right main bronchus angle was measured, and the changes in lung volume were determined by CT three-dimensional reconstruction. Results A total of 157 patients who underwent thoracoscopic right upper lobe resection were included, including 71 males and 86 females, with an average age of (59.7±11.2) years. There were 50 patients in the group A, 75 patients in the group B, and 32 patients in the group C. In the group A, compared with those without releasing the lower pulmonary ligament, patients with releasing had a lower incidence of postoperative residual cavity (P=0.016), greater changes in bronchus angle (P<0.001), and smaller changes in lung volume (P<0.001). In the group B and C, there was no significant effect of releasing the lower pulmonary ligament on postoperative residual cavity, bronchus angle, and lung volume changes (P>0.05). Conclusion For patients with thin and long body shape and low BMI, releasing the lower pulmonary ligament is helpful to promote the expansion of the residual lung after right upper lobe resection and reduce the occurrence of postoperative residual cavity in patients.

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effec-tive MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models' generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer po-tential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.

Objective To predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the screening criteria, a total of 651 patients were included. The training set consisted of 468 patients (181 males, 287 females) with an average age of (58.39±11.23) years, ranging from 29 to 78 years, the testing set included 140 patients (56 males, 84 females) with an average age of (58.81±10.70) years, ranging from 34 to 82 years, and the internal validation set comprised 43 patients (14 males, 29 females) with an average age of (60.16±10.68) years, ranging from 29 to 78 years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 0.952 [95%CI (0.87, 1.00)], an F1 score of 84.62%, and a precision-recall AUC of 0.892, outperforming the models based on the primary tumor and peritumoral regions. Conclusion The model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases. Accurately predicting microbe-disease interactions (MDIs) offers critical insights for disease intervention and pharmaceutical research. Current advanced AI-based technologies automatically generate robust representations of microbes and diseases, enabling effective MDI predictions. However, these models continue to face significant challenges. A major issue is their reliance on complex feature extractors and classifiers, which substantially diminishes the models' generalizability. To address this, we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs. Initially, we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation. Secondly, we employ decoupled representation learning technology, compelling the graph neural network (GNN) to independently learn the weights for each feature subspace, thus enhancing its expressive power. Finally, we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN, reducing information loss due to occlusion. Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models. This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research. Code and data are accessible at: https://github.com/shmildsj/MDI-IFDRL.

Objective:To unveil the dynamic molecular characteristics of colorectal cancer (CRC) progression, identify key molecules and signaling pathways driving disease development, and provide a theoretical basis for precision diagnosis and treatment.Methods:Differentially expressed genes (DEGs) were identified using DESeq2 based on the TCGA-CRC dataset (556 colorectal cancer samples) and three independent validation cohorts from the GEO database (GSE39582, GSE68468, GSE41258). Mfuzz time-series analysiswas applied to identify gene clusters with continuously upregulated expression during tumor progression. Functional enrichment analysis was performed using clusterProfiler, and protein-protein interaction (PPI) networks were constructed via the STRING online platform to pinpoint hub genes. Single-cell sequencing data (GSE132465/GSE144735) were integrated to resolve the cellular origins and intercellular communication of key genes. The prognostic value of genes was assessed using a univariate Cox proportional hazards model (likelihood ratio test), and single-cell sequencing data were analyzed using the Seurat pipeline with Wilcoxon rank-sum test to identify DEGs.Results:Time-series analysis identified Gene Cluster 4 (containing 186 genes) with a sustained upregulation trend across CRC stages from Ⅰ to Ⅳ. Functional enrichment revealed these genes were significantly involved in extracellular matrix (ECM) remodeling and pathways such as PI3K-Akt and MAPK signaling. PPI network analysis screened 10 hub genes ( COL10A1, THBS2, SPP1, etc.), whose high expression correlated significantly with poor patient prognosis. Single-cell sequencing demonstrated that these hub genes were predominantly expressed in fibroblast subpopulations, while SPP1 was enriched in macrophages. Cell-cell communication analysis confirmed that THBS2-CD47 and SPP1-CD44 were the primary pathways mediating fibroblast-immune/endothelial cell interactions. Conclusion:ECM-related genes are closely associated with the progression of CRC, in which the key molecules THBS2 and SPP1 may drive stromal-immune cell communication in the tumor microenvironment by mediating the THBS2-CD47 and SPP1-CD44 interaction pathways, thereby promoting the progression of CRC.

With the advancement of education,teaching methods have been continuously improved and optimized to en-hance students'learning experiences.In teaching the course of pathophysiology,a core discipline for medical students,integra-tion of Case-Based Learning(CBL)with the flipped classroom model can serve as a powerful pedagogical tool by stimulating students'interest,promoting collaborative learning,enhancing teacher-student interaction,and fostering a more active and en-gaging classroom environment.It also equips students with the confidence to better address real-world medical scenarios.This pa-per examines the application effect of the integrated teaching method on the teaching of pathophysiology and evaluates its pedagog-ical effectiveness.

Objective To identify novel biomarkers for predicting the risk of latent tuberculosis infection(LTBI)activation using bio-informatics and machine-learning algorithms and to establish a risk model.Methods The GSE112104 and GSE193777 datasets were obtained from the Gene Expression Omnibus.Differential gene expression and weighted gene co-expression network analyses were per-formed to identify ferroptosis-related differentially expressed genes(FRG-DEGs)associated with LTBI activation.Three machine-learning algorithms,least absolute shrinkage and selection operator,support vector machine-recursive feature elimination,and random forest,were used to identify ferroptosis-related hub genes(FRG-hubs).The reliability of these genes was validated using independent validation datasets and reverse transcription polymerase chain reaction(PCR).A risk model was established using R software.Results In the GSE 112104 dataset,296 genes were upregulated and 1 569 genes were downregulated in active tuberculosis compared to those in LTBI.Among the LTBI progressors,506 genes were upregulated and 1 132 genes were downregulated.Weighted correlation network analysis identified five gene modules,with the blue module showing the strongest correlation with LTBI activation(cor=0.62,P=0.000 04),con-taining 1 340 genes.Intersections with 728 ferroptosis-related genes resulted in eight FRG-DEGs.The machine-learning algorithms iden-tified four FRG-hubs:PLA2G6,GLS2,JUN,and AMN,whose expression decreased with LTBI activation.Reverse transcription PCR con-firmed this trend.A risk model based on these genes yielded an area under the curve of 0.98 to 1.00.Conclusion This study successfully identified novel biomarkers for predicting the risk of LTBI activation and developed an accurate predictive risk model.

With the advancement of education,teaching methods have been continuously improved and optimized to en-hance students'learning experiences.In teaching the course of pathophysiology,a core discipline for medical students,integra-tion of Case-Based Learning(CBL)with the flipped classroom model can serve as a powerful pedagogical tool by stimulating students'interest,promoting collaborative learning,enhancing teacher-student interaction,and fostering a more active and en-gaging classroom environment.It also equips students with the confidence to better address real-world medical scenarios.This pa-per examines the application effect of the integrated teaching method on the teaching of pathophysiology and evaluates its pedagog-ical effectiveness.

Objective To identify novel biomarkers for predicting the risk of latent tuberculosis infection(LTBI)activation using bio-informatics and machine-learning algorithms and to establish a risk model.Methods The GSE112104 and GSE193777 datasets were obtained from the Gene Expression Omnibus.Differential gene expression and weighted gene co-expression network analyses were per-formed to identify ferroptosis-related differentially expressed genes(FRG-DEGs)associated with LTBI activation.Three machine-learning algorithms,least absolute shrinkage and selection operator,support vector machine-recursive feature elimination,and random forest,were used to identify ferroptosis-related hub genes(FRG-hubs).The reliability of these genes was validated using independent validation datasets and reverse transcription polymerase chain reaction(PCR).A risk model was established using R software.Results In the GSE 112104 dataset,296 genes were upregulated and 1 569 genes were downregulated in active tuberculosis compared to those in LTBI.Among the LTBI progressors,506 genes were upregulated and 1 132 genes were downregulated.Weighted correlation network analysis identified five gene modules,with the blue module showing the strongest correlation with LTBI activation(cor=0.62,P=0.000 04),con-taining 1 340 genes.Intersections with 728 ferroptosis-related genes resulted in eight FRG-DEGs.The machine-learning algorithms iden-tified four FRG-hubs:PLA2G6,GLS2,JUN,and AMN,whose expression decreased with LTBI activation.Reverse transcription PCR con-firmed this trend.A risk model based on these genes yielded an area under the curve of 0.98 to 1.00.Conclusion This study successfully identified novel biomarkers for predicting the risk of LTBI activation and developed an accurate predictive risk model.

Objective Based on HPLC fingerprinting and chemometrics,to evaluate the quality of Schefflera kwangsiensis Merr.from Guangxi.Methods HPLC was used to establish fingerprints of Schefflera kwangsiensis Merr.from ten different origins,and gradient elution was carried out with methanol-0.1％phosphoric acid aqueous solution as mobile phase.Cluster analysis(CA),principal component analysis(PCA)and orthogonal partial least squares-discriminant analysis(OPLS-DA)were applied to evaluate quality.Results The fingerprints of Schefflera kwangsiensis Merr.from ten different origins were established by HPLC,a total of 22 common peaks were calibrated,with a similarity range of 0.922-0.999.Four chromatographic peaks were identified as rhodopsin,4,5-bis-O-caffeoylquinic acid,caffeic acid,and naringin.The samples were classified into four types according to the CA and OPLS-DA.PCA identified four principal components with a cumulative contribution rare of 95.39％.Conclusion The quality of Schefflera kwangsiensis Merr.can be comprehensively evaluated by fingerprinting combined with CA,PCA and OPLS-DA analysis.The Study can provide a reference for improving the quality control and assessment of Schefflera kwangsiensis Merr.