MicroRNA-34c-5p Reduces Malignant Properties of Lung Cancer Cells through Regulation of TBL1XR1/Wnt/ß-catenin Signaling.

INTRODUCTION: Lung cancer is common cancer with high mortality. A growing number of studies have focused on investigating the regulatory effects of microRNAs (miRs/miRNAs) during cancer progression. Nevertheless, the biological function of miR- 34c-5p in lung cancer and the underlying mechanism have not been determined. This study explored the effect of miR-34c-5p on the malignant behaviors of lung cancer cells. METHODS: In this study, we utilized diverse public databases to obtain differentially expressed miRNAs. Then, qRT-PCR and western blot were conducted to determine miR-34c-5p and transducin ß-like 1 X-linked receptor 1 (TBL1XR1) expression. Next, H1299 and H460 cells were transfected with miR-34c-5p-mimic and pcDNA3.1- TBL1XR1. To examine the anticancer effects of miR-34c-5p, CCK-8, scratch, and Matrigel-Transwell assays were conducted to test cell viability, migration, and invasion, respectively. The StarBase database and dual-luciferase reporter gene assay were used to predict and verify the relationship between miR-34c-5p and TBL1XR1. RESULTS: Finally, Wnt/ß-catenin signaling- and epithelial-mesenchymal transition (EMT)- related protein levels were detected using western blot. The results demonstrated that miR-34c-5p was poorly expressed in lung cancer cells, while TBL1XR1 was highly expressed. The findings also confirmed the direct interaction between miR-34c-5p and TBL1XR1. In H1299 and H460 cells, miR-34c-5p overexpression inhibited cell proliferation, migration, and invasion, Wnt/ß-catenin signaling activity, and EMT, while TBL1XR1 upregulation reversed these effects of miR-34c-5p overexpression. CONCLUSION: These findings illustrated that miR-34c-5p might repress the malignant behaviors of lung cancer cells via TBL1XR1, providing evidence for miR-34c-5p-based lung cancer therapy.

Application of Deep Learning in Lung Cancer Imaging Diagnosis.

Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the shape of nodules is different, and the risk of cancer will vary with the shape of nodules. Computer-aided diagnosis (CAD) is a very suitable method to solve this problem because the computer vision model can quickly scan every part of the CT image of the same quality for analysis and will not be affected by fatigue and emotion. The latest advances in deep learning enable computer vision models to help doctors diagnose various diseases, and in some cases, models have shown greater competitiveness than doctors. Based on the opportunity of technological development, the application of computer vision in medical imaging diagnosis of diseases has important research significance and value. In this paper, we have used a deep learning-based model on CT images of lung cancer and verified its effectiveness in the timely and accurate prediction of lungs disease. The proposed model has three parts: (i) detection of lung nodules, (ii) False Positive Reduction of the detected nodules to filter out "false nodules," and (iii) classification of benign and malignant lung nodules. Furthermore, different network structures and loss functions were designed and realized at different stages. Additionally, to fine-tune the proposed deep learning-based mode and improve its accuracy in the detection Lung Nodule Detection, Noudule-Net, which is a detection network structure that combines U-Net and RPN, is proposed. Experimental observations have verified that the proposed scheme has exceptionally improved the expected accuracy and precision ratio of the underlined disease.

A study of clinical implementation of ventricular septal defect closure using three transthoracic approaches.

OBJECTIVE: To investigate the clinical implementation of ventricular septal defect closure using the three transthoracic approaches. METHODS: A total of 70 children with septal defects admitted to our hospital from January 2017 to December 2020 were selected as the study subjects. Among them, 10 children with the left thorax-right ventricle-left ventricle approach were assigned to Group A, 8 children with the right thorax-atrium dextrum-right ventricle-left ventricle approach were assigned to Group B, and 52 children with the subxyphoid-right ventricle-left ventricle approach were assigned to Group C. The surgical indices were recorded, the success rates of closure and cardiopulmonary function indices were compared, electrocardiogram (ECG), echocardiogram and X-ray film were investigated at 1, 3 and 12 months after surgery, and the incidence of complications was recorded. RESULTS: There was no statistically significant difference in the success rate of closure among the three groups (P > 0.05). The duration of intracardiac operations in Groups A and C was remarkably shorter than that in Group B, and the duration of skin incision and suture and hospital stay in Groups A and B were noticeably shorter than those in Group C (P < 0.05). After surgery, there was statistically significant difference in the contents of creatine kinase MB (CK-MB) isoenzyme, lactate dehydrogenase (LDH), serum malondialdehyde (MDA) and superoxide dismutase (SOD) among the three groups (P > 0.05). Airway resistance (Raw), oxygenation index (OI), and alveolar-arterial oxygen gradient (AaDO2) indicated that the postoperative pulmonary function in Group C was more effectively protected. There was no statistically significant difference in the incidence of complications among the three groups (P > 0.05). CONCLUSION: Ventricular septal defect closure using the three transthoracic approaches exhibited a high success rate and a high safety profile.

Analysis of poor prognostic factors of cerebral nerve after deep hypothermic circulatory arrest in patients with type A aortic dissection.

OBJECTIVE: To investigate the influences of deep hypothermic circulatory arrest (DHCA) on postoperative cranial nerve function in patients undergoing surgery for type A aortic dissection. METHODS: A total of 100 patients undergoing DHCA during the surgery for type A aortic dissection in our hospital were selected as the study subjects. After surgery, 32 patients with neurological complications were assigned to Group A, and 68 patients without neurological complications were assigned to Group B. The clinical outcomes were compared between the two groups, and the risk factors of postoperative neurological complications were analyzed by univariate and multivariate logistic regression analysis. RESULTS: During the surgery, patients underwent cerebral perfusion at 5 min and 10 min during DHCA had remarkably decreased cerebral oxygen saturation (rSO2) and VmMCA than those before anesthesia induction (P<0.05). After recovery of CPB, rSO2 and mean velocity in middle cerebral artery (VmMCA) recovered to the preoperative levels. The correlation analysis revealed a positive correlation between rSO2 and VmMCA (P<0.05). The univariate analysis suggested that the history of hypertension, hydropericardium, surgical duration, duration of cardiopulmonary bypass (CPB), aortic occlusion, ICU, and ventilator-assisted respiration, and hypoxemia significantly affected postoperative cranial nerve function (P<0.05). The logistic multivariate regression analysis demonstrated that the duration of CPB and aortic occlusion and hypoxemia were independent risk factors for postoperative cranial nerve dysfunction (P<0.05). CONCLUSION: There were noticeable changes in hemodynamic and blood oxygen parameters in patients with type A aortic dissection undergoing DHCA during the perioperative period. The long duration of CPB and aortic occlusion and preoperative hypoxemia are the independent risk factors leading to postoperative impaired cranial nerve function.