ObjectiveTo reveal the potential mechanism of cisplatin resistance in non-small cell lung cancer A549 cells by comparing the expression profiles of wild-type A549 cells and cisplatin-resistant A549 cells (A549/DPP) through whole transcriptome sequencing analysis.MethodsThe cisplatin resistant A549 (A549/DDP) cell line was first established. Then, the whole-transcriptome analysis was conducted both on A549 and A549/DDP cells. Next, the differentially expressed RNAs of lncRNA-seq, circRNA-seq, and miRNA-seq data were identified, respectively, followed by functional enrichment analysis. Finally, a comprehensive analysis based on the whole transcriptome data was performed and the construction of the ceRNA network was carried out.ResultsA total of 4 517 lncRNA, 123 circRNA, and 145 miRNA were differentially expressed in A549/DDP cells compared with the A549 cell line. These different RNAs were significantly enriched in cancer-related pathways. The ceRNA network contained 12 miRNAs, 4 circRNAs, 23 lncRNAs, and 9 mRNA nodes, of which hsa-miR-125a-5p and hsa-miR-125b-5p were important miRNAs based on the topological analysis.ConclusionTumor necrosis factor signaling pathway and p53 signaling pathway are involved in A549/DPP resistance. Hsa-miR-125a-5p and hsa-miR-125b-5p may be potential targets for reversing cisplatin resistance.
Because of the diversity and complexity of clinical indicators, it is difficult to establish a comprehensive and reliable prediction model for induction of labor (IOL) outcomes with existing methods. This study aims to analyze the clinical indicators related to IOL and to develop and evaluate a prediction model based on a small-sample of data. The study population consisted of a total of 90 pregnant women who underwent IOL between February 2023 and January 2024 at the Shanghai First Maternity and Infant Healthcare Hospital, and a total of 52 clinical indicators were recorded. Maximal information coefficient (MIC) was used to select features for clinical indicators to reduce the risk of overfitting caused by high-dimensional features. Then, based on the features selected by MIC, the support vector machine (SVM) model based on small samples was compared and analyzed with the fully connected neural network (FCNN) model based on large samples in deep learning, and the receiver operating characteristic (ROC) curve was given. By calculating the MIC score, the final feature dimension was reduced from 55 to 15, and the area under curve (AUC) of the SVM model was improved from 0.872 before feature selection to 0.923. Model comparison results showed that SVM had better prediction performance than FCNN. This study demonstrates that SVM successfully predicted IOL outcomes, and the MIC feature selection effectively improves the model’s generalization ability, making the prediction results more stable. This study provides a reliable method for predicting the outcome of induced labor with potential clinical applications.
Objective To identify and analyze risk factors associated with acute renal failure (ARF) after lung transplantation (LTx) and develop a predictive model. Methods Patients for this study were obtained from the United Network for Organ Sharing (UNOS) database who underwent unilateral or bilateral lung transplantation between 2015 and 2022. Preoperative and postoperative clinical characteristics of the patients were analyzed. A combined approach using random forest and Lasso regression was employed to identify key preoperative factors associated with the incidence of ARF following lung transplantation. Random forest was used to assess the importance of each feature variable, while Lasso regression further filtered the variables contributing most significantly to the model. The predictive performance of the constructed model was evaluated in both training and validation sets, with ROC curves and AUC values used to verify and compare model effectiveness. ResultsA total of 15 110 LTx patients were included in the study, comprising 6 041 males and 9 069 females, with a median age of 64 years. Findings indicated that preoperative lung diagnosis, estimated glomerular filtration rate (eGFR), mechanical ventilation, ICU admission prior to transplantation, extracorporeal membrane oxygenation (ECMO) support, infection within two weeks before transplantation, Karnofsky Performance Status (KPS) score, donor age, waitlist duration, double-lung transplantation, and ischemia time showed statistically significant differences between groups (P<0.05). Model evaluation results demonstrated that the constructed predictive model achieved high accuracy in both the training and validation sets, with favorable AUC values, confirming its validity and reliability. ConclusionThis study discusses common risk factors for ARF following lung transplantation and introduces an effective predictive model with potential clinical application.
Objective To evaluate the clinical effectiveness of laparoscopic cholecystectomy and laparoscopic common bile duct exploration （LC＋LCBDE） and endoscopic retrograde cholangiopancreatography/endoscopic sphincterectomy with LC（ERCP/EST＋LC） in treatment for cholecystolithiasis with choledocholithiasis. Methods From January 2008 to July 2011， 127 patients suffered from cholecystolithiasis with choledocholithiasis underwent either LC＋LCBDE（85 cases， LC＋LCBDE group） or ERCP/EST＋LC（42 cases， ERCP/EST＋LC group） were collected retrospectively. The clearance rate of calculus， hospital stay， hospitalization expenses， and the rate of postoperative complications were compared between two groups. Results Eighty-five patients were performed successfully in the LC＋LCBDE group， out of which 54 patients had primary closure of common bile duct （LC＋LCBDE primary closure group）， whereas in 28 patients common bile ducts were closed over T tube （LC＋LCBDE＋T tube group）. Forty-two patients were performed successfully in the ERCP/EST＋LC group. There were no differences in the clearance rate of calculus〔100%（82/82） versus 97.37%（37/38）， P=0.317〕 and postoperative complications rate 〔（4.71% （4/85） versus 4.76%（2/42）， P=1.000〕 between the LC＋LCBDE group and ERCP/EST＋LC group. The median （quartile） hospital stay in the LC＋LCBDE group was shorter than that in the ERCP/EST＋LC group 〔12 （6） d versus 17（9） d， P＜0.001〕. In the LC＋LCBDE primary closure group， both median （quartile）?hospital stay and median（quartile） hospitalization expenses were less than those of ERCP/EST＋LC〔hospital stay：11（5） d versus 17（9） d， P＜0.001；hospitalization expenses：27 054（8 452） yuan versus 31 595（11 743） yuan， P=0.005〕 . Conclusions In the management of patients suffered from cholecystolithiasis with choledocholithiasis， both LC＋LCBDE and ERCP/EST＋LC are safe and effective. LC＋LCBDE， especially primary closure after LCBDE， is associated with significantly less costs as compared with ERCP/EST＋LC. Moreover， patients can be cured by LC＋LCBDE through one-stage treatment with the protection of the papilla function and no limits to the amount or size of the choledocholithiasis. The LC＋LCBDE is a preferable choice for the appropriate cases of cholecystolithiasis with choledocholithiasis.
Objective To analyze features of color Doppler ultrasonography in gastrointestinal stromal tumors. Method The ultrasound images of gastrointestinal stromal tumors (51 cases) and gastrointestinal cancers (59 cases) confirmed by operation and pathology were compared and analyzed. Results The gastric stromal tumor mainly occurred at the bottom of the stomach and the body of the stomach (17 cases), the intestinal stromal tumor mainly occurred at the small intestine (24 cases). The gastric cancer mainly occurred at the gastric antrum (18 cases), the intestinal cancer all occurred at the colon (20 cases) and rectum (12 cases). Compared with the gastrointestinal cancers, the gastrointestinal cavity was not surrounded by tumor, the peripheral boundary was clear, the morphology was more regular, the internal echo was uneven, and there was no peripheral lymph node metastasis in the gastrointestinal stromal tumors, the differences were statistically significant (P<0.05). There were no significant differences in the degree of blood flow and tumor diameter between the gastrointestinal stromal tumors and the gastrointestinal cancers (P>0.05), but the blood flow of the intestinal stromal tumor was significantly more abundant as compared with the intestinal cancer (P<0.05). Conclusion Color Doppler ultrasonography, as a simple and rapid method, has a certain diagnostic value for differentiation of gastrointestinal stromal tumors and gastrointestinal cancers.
Lung cancer has the highest mortality rate among all malignant tumors. The key to reducing lung cancer mortality is the accurate diagnosis of pulmonary nodules in early-stage lung cancer. Computer-aided diagnostic techniques are considered to have potential beyond human experts for accurate diagnosis of early pulmonary nodules. The detection and classification of pulmonary nodules based on deep learning technology can continuously improve the accuracy of diagnosis through self-learning, and is an important means to achieve computer-aided diagnosis. First, we systematically introduced the application of two dimension convolutional neural network (2D-CNN), three dimension convolutional neural network (3D-CNN) and faster regions convolutional neural network (Faster R-CNN) techniques in the detection of pulmonary nodules. Then we introduced the application of 2D-CNN, 3D-CNN, multi-stream multi-scale convolutional neural network (MMCNN), deep convolutional generative adversarial networks (DCGAN) and transfer learning technology in classification of pulmonary nodules. Finally, we conducted a comprehensive comparative analysis of different deep learning methods in the detection and classification of pulmonary nodules.
The real physical image of the affected limb, which is difficult to move in the traditional mirror training, can be realized easily by the rehabilitation robots. During this training, the affected limb is often in a passive state. However, with the gradual recovery of the movement ability, active mirror training becomes a better choice. Consequently, this paper took the self-developed shoulder joint rehabilitation robot with an adjustable structure as an experimental platform, and proposed a mirror training system completed by next four parts. First, the motion trajectory of the healthy limb was obtained by the Inertial Measurement Units (IMU). Then the variable universe fuzzy adaptive proportion differentiation (PD) control was adopted for inner loop, meanwhile, the muscle strength of the affected limb was estimated by the surface electromyography (sEMG). The compensation force for an assisted limb of outer loop was calculated. According to the experimental results, the control system can provide real-time assistance compensation according to the recovery of the affected limb, fully exert the training initiative of the affected limb, and make the affected limb achieve better rehabilitation training effect.