MRI is the gold standard for confirming a pelvic lymph node metastasis diagnosis. Traditionally, medical radiologists have analyzed MRI image features of regional lymph nodes to make diagnostic decisions based on their subjective experience; this diagnosis lacks objectivity and accuracy. This study trained a faster region-based convolutional neural network (Faster R-CNN) with 28,080 MRI images of lymph node metastasis, allowing the Faster R-CNN to read those images and to make diagnoses. For clinical verification, 414 cases of rectal cancer at various medical centers were collected, and Faster R-CNN-based diagnoses were compared with radiologist diagnoses using receiver operating characteristic curves (ROC). The area under the Faster R-CNN ROC was 0.912, indicating a more effective and objective diagnosis. The Faster R-CNN diagnosis time was 20 s/case, which was much shorter than the average time (600 s/case) of the radiologist diagnoses. Faster R-CNN enables accurate and efficient diagnosis of lymph node metastases. .

Long non-coding RNAs (lncRNAs) recently have been implicated in many biological processes and diseases. Atherosclerosis is a major risk factor for cardiovascular disease. However, the functional role of lncRNAs in atherosclerosis is largely unknown. Here we identified LOC285194 as a key regulator of cell proliferation and apoptosis during atherosclerosis. The expression of LOC285194 was dramatically down-regulated in a aortic atherosclerotic plaques of well-defined model of apolipoprotein-E knockout (ApoE −/-) mice. Moreover, we found that targeting LOC285194 results in neointimal hyperplasia in vivo in carotid artery injury model. We also showed that targeting LOC285194 promotes cell proliferation and inhibits apoptosis in vascular smooth muscle cells (VSMCs) in vitro, and vice versa. In addition, targeting LOC285194 promotes cell invasion and migration in vitro. Our studies identify LOC285194 as a novel regulator of cell proliferation and apoptosis and suggest that this lncRNA could serve as a therapeutic target to treat atherosclerosis and related cardiovascular disorders.

Chen (2020) miR-25-3p inhibition impairs tumorigenesis and invasion in gastric cancer cells invitro

Recent focus has turned to secretory clusterin (sCLU) as a key contributor to chemoresistance of anticancer agents, but the role of sCLU on chemotherapy drug response to gastric cancer cells is not fully understood. Previous research found that sCLU was overexpressed in the induced multidrug-resistant MGC-803/5-FU cell line, suggesting that sCLU upregulation was closely related to chemoresistance to anticancer agents. In the present study, we aimed to clarify the role and mechanisms of sCLU in regulating the chemoresistance of gastric cancer cells. Cell apoptosis and cell viability were evaluated by annexin V/propidium iodide staining and CCK8. Expression of sCLU and miR-195-5P was detected using quantitative RT-PCR assays. The expression of sCLU in gastric cancer tissues was detected by RT-PCR assays. Upregulating or downregulating sCLU or miR-195-5P in gastric cancer cells was used to evaluate the mechanisms of chemoresistance. We found that sCLU was significantly elevated in the MGC-803/5-FU and SGC-7901 cells, and the downregulating sCLU sensitized MGC-803/5-FU and SGC-7901 cells to cisplatin and Docetaxel by upregulation of miR-195-5P. Upregulating sCLU in MGC-803 and HGC-27 cells was resistant to cisplatin and Docetaxel by downregulating miR-195-5p. Targeting miR-195-5P reduced the sensitivity of MGC-803 cells to 5-FU, and miR-195-5P overexpression enhanced the sensitivity of MGC-803/5-FU cells to 5-FU. The overexpression of sCLU in gastric cancer tissues was associated with chemoresistance. Our findings suggest that overexpression of sCLU induced chemoresistance in gastric cancer cells by downregulating miR-195-5p, thus providing a potential target for the development of agents that targeting sCLU for gastric cancer therapy.

Background: The accurate prediction of the tumor infiltration depth in the gastric wall based on enhanced CT images of gastric cancer is crucial for screening gastric cancer diseases and formulating treatment plans. Convolutional neural networks perform well in image segmentation. In this study, a convolutional neural network was used to construct a framework for automatic tumor recognition based on enhanced CT images of gastric cancer for the identification of lesion areas and the analysis and prediction of T staging of gastric cancer. Methods: Enhanced CT venous phase images of 225 patients with advanced gastric cancer from January 2017 to June 2018 were retrospectively collected. Ftable LabelImg software was used to identify the cancerous areas consistent with the postoperative pathological T stage. The training set images were enhanced to train the Faster RCNN detection model. Finally, the accuracy, specificity, recall rate, F1 index, ROC curve, and AUC were used to quantify the classification performance of T staging on this system. Results: The AUC of the Faster RCNN operating system was 0.93, and the recognition accuracies for T2, T3, and T4 were 90, 93, and 95%, respectively. The time required to automatically recognize a single image was 0.2 s, while the interpretation time of an imaging expert was ∼10 s. Conclusion: In enhanced CT images of gastric cancer before treatment, the application of Faster RCNN to diagnosis the T stage of gastric cancer has high accuracy and feasibility.