OBJECTIVES: To explore the mechanism of Shuangshu Decoction (SSD) for inhibiting growth of gastric cancer xenografts in nude mice. METHODS: Network pharmacology analysis was conducted to identify the common targets of SSD and gastric cancer cell ferroptosis, and bioinformatics analysis and molecular docking were used to validate the core targets. In the cell experiment, AGS cells were treated with SSD-medicated serum, Fer-1 (a ferroptosis inhibitor), or both, and the changes in cell viability, ferroptosis markers (ROS, Fe²⁺ and GSH), expressions of P53, SLC7A11 and GPX4, and mitochondrial morphology were examined. In a nude mouse model bearing gastric cancer xenografts, the effects of gavage with SSD, intraperitoneal injection of Fer-1, or their combination on tumor volume/weight, histopathology, and expressions of P53, SLC7A11 and GPX4 levels were evaluated. RESULTS: The active components in SSD (quercetin and wogonin) showed strong binding affinities to P53. In AGS cells, SSD treatment dose-dependently inhibited cell proliferation, increased ROS and Fe²⁺ levels, upregulated P53 expression, and downregulated the expressions of SLC7A11 and GPX4, but these effects were effectively attenuated by Fer-1 treatment. SSD also induced mitochondrial shrinkage and increased the membrane density, which were alleviated by Fer-1. In the tumor-bearing mouse models, gavage with SSD significantly reduced tumor size and weight, caused tumor cell necrosis, upregulated P53 and downregulated SLC7A11 and GPX4 expression in the tumor tissue, and these effects were obviously mitigated by Fer-1 treatment. CONCLUSIONS SSD inhibits gastric cancer growth in nude mice by inducing cell ferroptosis via the P53/SLC7A11/GPX4 axis.
Ferroptosis/drug effects* ; Animals ; Stomach Neoplasms/metabolism* ; Tumor Suppressor Protein p53/metabolism* ; Mice, Nude ; Phospholipid Hydroperoxide Glutathione Peroxidase ; Drugs, Chinese Herbal/pharmacology* ; Humans ; Amino Acid Transport System y+/metabolism* ; Mice ; Cell Line, Tumor ; Cell Proliferation/drug effects* ; Xenograft Model Antitumor Assays

Objective:To investigate the feasibility of deep learning based on computed tomography(CT)radiomics in improving diagnostic accuracy for pulmonary nodules.Methods:A total of 500 patients with pulmonary nodules who admitted to our hospital from January 2023 to January 2024 were selected as study subjects,and they were randomly divided into a training set(350 patients)and a test set(150 patients)as 7:3 ratio.All patients underwent CT examination,and pathological diagnosis was used as gold standard to record pulmonary nodules that were judged by clinical judgment.The radiomics features were screened from the CT images of the patients,and these features were used to construct multiple machine learning models.The predictive value of different models in diagnosing pulmonary nodules was analyzed through confusion matrices and receiver operating characteristic(ROC)curve.Results:A total of 1,594 radiomics features,including 1,195 texture features(74.97%)that was the largest ratio,334 first-order histograms(20.95%),and 65 second-order histograms(4.08%),were extracted in this study.After least absolute shrinkage and selection operator(LASSO)regression analysis and ten-fold cross-validation processing,a total of six radiomics features were screened out.The screened radiomics features were incorporated respectively into four assembled models with machine learning,including ResNet50,DenseNet121,Inception_V3 and VGG19.The constructed models were evaluated respectively using the training set and the test set.The results showed that the assembled model had the highest accuracies in both training set and the test set(96.57%and 95.33%),which area under curve(AUC)values were 0.934 and 0.923,and specificities were 81.64%and 80.52%,and sensitivities were 90.25%and 88.71%,respectively.The results of consistency test indicated that the assembled model had the best classification consistency(Kappa=0.856,P<0.001)in the constructed diagnostic model for pulmonary nodule,which was the best-performing model.Conclusion:The deep learning technique based on CT radiomics has a certain feasibility in improving the diagnostic accuracy for pulmonary nodules,and the machine learning model that is included in this study has favorable predictive value in diagnosing pulmonary nodules.In them,the assembled model that is constructed on the basis of ResNet50,DenseNet121,Inception_V3,and VGG19 has better classification ability.

Objective:To investigate the feasibility of deep learning based on computed tomography(CT)radiomics in improving diagnostic accuracy for pulmonary nodules.Methods:A total of 500 patients with pulmonary nodules who admitted to our hospital from January 2023 to January 2024 were selected as study subjects,and they were randomly divided into a training set(350 patients)and a test set(150 patients)as 7:3 ratio.All patients underwent CT examination,and pathological diagnosis was used as gold standard to record pulmonary nodules that were judged by clinical judgment.The radiomics features were screened from the CT images of the patients,and these features were used to construct multiple machine learning models.The predictive value of different models in diagnosing pulmonary nodules was analyzed through confusion matrices and receiver operating characteristic(ROC)curve.Results:A total of 1,594 radiomics features,including 1,195 texture features(74.97%)that was the largest ratio,334 first-order histograms(20.95%),and 65 second-order histograms(4.08%),were extracted in this study.After least absolute shrinkage and selection operator(LASSO)regression analysis and ten-fold cross-validation processing,a total of six radiomics features were screened out.The screened radiomics features were incorporated respectively into four assembled models with machine learning,including ResNet50,DenseNet121,Inception_V3 and VGG19.The constructed models were evaluated respectively using the training set and the test set.The results showed that the assembled model had the highest accuracies in both training set and the test set(96.57%and 95.33%),which area under curve(AUC)values were 0.934 and 0.923,and specificities were 81.64%and 80.52%,and sensitivities were 90.25%and 88.71%,respectively.The results of consistency test indicated that the assembled model had the best classification consistency(Kappa=0.856,P<0.001)in the constructed diagnostic model for pulmonary nodule,which was the best-performing model.Conclusion:The deep learning technique based on CT radiomics has a certain feasibility in improving the diagnostic accuracy for pulmonary nodules,and the machine learning model that is included in this study has favorable predictive value in diagnosing pulmonary nodules.In them,the assembled model that is constructed on the basis of ResNet50,DenseNet121,Inception_V3,and VGG19 has better classification ability.

Objective:To explore the application value of computed tomography (CT) plain scan and dynamic enhanced scan in the diagnosis of solitary pulmonary nodules.Methods:The clinical data of 120 patients with solitary pulmonary nodules detected by physical examination in Baoding First Central Hospital from January 2018 to December 2020 were retrospectively reviewed. All patients were confirmed by surgery and pathology, including 77 benign lesions and 43 malignant lesions; All patients underwent CT plain scan and dynamic enhanced scan before operation. The accuracy of the two examination methods in the diagnosis of benign and malignant lesions of solitary pulmonary nodules was analyzed and compared. The detection rate of CT dynamic enhanced scan imaging characteristics (vacuole sign, ground glass sign, spinous sign, lobulation sign, hair prick sign, blood vessel cluster, pleural depression) of benign and malignant lesions of solitary pulmonary nodules was compared, and the diagnostic value of CT plain scan and dynamic enhanced scan in the differential diagnosis of benign and malignant solitary pulmonary nodules was evaluated based on the results of surgical pathological diagnosis. The manifestations and characteristic curves of CT dynamic enhanced scan of solitary pulmonary nodules was analyzed.Results:The diagnostic accuracy of CT dynamic enhanced scan for solitary pulmonary nodules was 80.00% (96/120), which was higher than that of CT plain scan (63.33%) (76/120) ( P<0.05). The sensitivity, specificity, and negative predictive value of CT dynamic enhanced scan for the diagnosis of benign and malignant lesions of solitary pulmonary nodules were higher than those of CT plain scan (all P<0.05). Among the imaging characteristics of CT dynamic enhanced scans of malignant lesions, the ground glass sign, spinous process sign, lobulation sign, spiculation sign, vascular clustering and pleural indentation were detected more frequently than those of benign lesions (all P<0.05). Benign lesions usually showed homogeneous enhancement, and a few showed heterogeneous enhancement; Malignant nodules often showed uneven enhancement, and a few had even enhancement. The time density curves of dynamic enhanced CT values in the regions of interest of benign and malignant solitary pulmonary nodules were different. Conclusions:The value of dynamic enhanced CT scan in the differential diagnosis of benign and malignant lesions of solitary pulmonary nodules is higher than that of CT plain scan, and the imaging features are obvious, with higher sensitivity and specificity, which is worthy of application.

0.05).At T9,the Ppeak and PETCO2 in LMA group were significantly higher than those in ET group [(19.0?5.0) mm Hg and(44.7?3.1) mm Hg vs.(13.0?3.0) mm Hg and(34.1?1.9) mm Hg respectively,t=5.636,P=0.000 and t=15.968,P=0.000].In LMA group,5 patients developed adverse reaction,while in the ET group,13 patients showed the reaction(?2=3.774,P=0.052).Conclusions Ventilation with LMA is safe and reliable for elderly patients undergoing laparoscopic surgery with a low rate of adverse reaction.It has slight effect on the hemodynamics of the patients.