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.
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.
目的 探讨胰腺淋巴瘤和胰腺癌的鉴别诊断及治疗。方法 回顾性分析我院2000年1月至2008年4月期间经病理确诊的5例胰腺淋巴瘤患者的临床资料。结果 本组5例患者临床症状以腹痛、发热、黄疸为主，除1例外血浆CA19-9均正常，临床表现无法区分胰腺淋巴瘤和胰腺癌，CT等影像学有助于鉴别诊断。胰腺淋巴瘤CT增强扫描后均见强化表现，胰管不粗，和周围大血管关系紧密，邻近血管结构被推移，但无浸润、包绕现象。治疗以化疗为主，除1例失访外，其余4例分别存活24、14、14、13个月。结论 胰腺淋巴瘤是一种罕见的疾病，临床症状缺乏特异性，极易与胰腺癌混淆，治疗和预后却与胰腺癌不尽相同，临床上应该尽量通过CT或超声下穿刺明确诊断，以避免不必要的手术。
目的 报道10例血清CA19-9明显升高的胆管良性疾病病例。方法 回顾性分析2004年1月至2006年3月期间我院收治并经手术证实的10例血清CA19-9明显升高（gt;500 U/ml）的胆管良性病变病例。结果 患者中男4例，女6例，年龄30~85岁，CA19-9为532.32~12 000.00 U/ml，除1例患者CA125轻度升高外，其他患者血清CEA、CA125及AFP均正常。胆总管结石8例，肝内胆管结石1例，原发性硬化性胆管炎1例； 除1例外均存在不同程度阻塞性黄疸。经治疗后8例CA19-9水平在30 d内降至正常，另2例分别于术后2个月和3个月内降至正常。结论 CA19-9在胆管恶性肿瘤诊断方面的意义仍需进一步研究。