LncRNA PCED1B-AS1 mediates miR-3681-3p/MAP2K7 axis to promote metastasis, invasion and EMT in gastric cancer.

BACKGROUND: LncRNA PCED1B-AS1 is abnormally expressed in multiple cancers and has been confirmed as an oncogene. Our study aimed to investigate the regulatory mechanism of lncRNA PCED1B-AS1 in gastric cancer. METHODS: TCGA database was used to analyze the abnormal expression of lncRNA PCED1B-AS1 in gastric cancer. By database prediction and mass spectrometric analysis, miR-3681-3p and MAP2K7 are potential downstream target molecules of lncRNA PCED1B-AS1 and verified by dual-luciferase report assay. RT-qPCR analysis and western blot were performed to detect the expressions of PCED1B-AS1 and MAP2K7 in gastric cancer cell lines and tissues. CCK-8 kit was applied to measure the cell viability. Wound healing and Transwell experiment were used to detect the migration and invasion. Western blot and immunohistochemical staining were performed to detect the expressions of EMT-related proteins in tissues. The changes of tumor proliferation were detected by xenograft experiment in nude mice. RESULTS: PCED1B-AS1 expression was higher but miR-3681-3 expression was lower in gastric cancer cell lines or tissues, compared to normal group. Function analysis verified PCED1B-AS1 promoted cell proliferation and inhibited cell apoptosis in gastric cancer cells in vitro and in vivo. LncRNA PCED1B-AS1 could bind directly to miR-3681-3p, and MAP2K7 was found to be a downstream target of miR-3681-3p. MiR-3681-3p mimics or si-MAP2K7 could partly reverse the effect of PCED1B-AS1 on gastric cancer cells. CONCLUSION: PCED1B-AS1 accelerated cell proliferation and inhibited cell apoptosis through sponging miR-3681-3p to upregulate MAP2K7 expression in gastric cancer, which indicated PCED1B-AS1/miR-3681-3p/MAP2K7 axis may serve as a potential therapeutic target for gastric cancer.

Smartphone-enabled colorimetric immunoassay for deoxynivalenol based on Mn2+-mediated aggregation of AuNPs.

Deoxynivalenol (DON) is a common mycotoxin in food that mainly pollutes grain crops and feeds, such as barley, wheat and corn. DON has caused widespread concern in the field of food and feed safety. In this study, a colorimetric immunoassay was proposed based on the aggregation of gold nanoparticles (AuNPs) due to the decomposition of Mn2+ from gold-coated manganese dioxide (AuNP@MnO2) nanosheets. In this study, 2-(dihydrogen phosphate)-l-ascorbic acid (AAP) was hydrolyzed by alkaline phosphatase (ALP) and converted to ascorbic acid (AA). Then, AuNP@MnO2 was reduced to Mn2+ and AuNPs aggregation occurred. Using the unique optical characteristics of AuNPs and AuNP@MnO2, visible color changes realized simple detection of DON with high sensitivity and portability. With increasing DON content, the color changed more obviously. To quantitatively detect DON, pictures can be taken and the blue value can be read by a smartphone. The detection limit (Ic10) of this method was 0.098 ng mL-1, which was 326 times higher than that of traditional competitive ELISA, and the detection range was 0.177-6.073 ng mL-1. This method exhibited high specificity with no cross-reaction in other structural analogs. The average recovery rate of DON in corn flour samples was 89.1 %-110.2 %, demonstrating the high accuracy and stability of this assay in actual sample detection. Therefore, the colorimetric immunoassay can be used for DON-related food safety monitoring.

Artificial intelligence-assisted system for the assessment of Forrest classification of peptic ulcer bleeding: a multicenter diagnostic study.

BACKGROUND: Inaccurate Forrest classification may significantly affect clinical outcomes, especially in high risk patients. Therefore, this study aimed to develop a real-time deep convolutional neural network (DCNN) system to assess the Forrest classification of peptic ulcer bleeding (PUB). METHODS: A training dataset (3868 endoscopic images) and an internal validation dataset (834 images) were retrospectively collected from the 900th Hospital, Fuzhou, China. In addition, 521 images collected from four other hospitals were used for external validation. Finally, 46 endoscopic videos were prospectively collected to assess the real-time diagnostic performance of the DCNN system, whose diagnostic performance was also prospectively compared with that of three senior and three junior endoscopists. RESULTS: The DCNN system had a satisfactory diagnostic performance in the assessment of Forrest classification, with an accuracy of 91.2% (95%CI 89.5%-92.6%) and a macro-average area under the receiver operating characteristic curve of 0.80 in the validation dataset. Moreover, the DCNN system could judge suspicious regions automatically using Forrest classification in real-time videos, with an accuracy of 92.0% (95%CI 80.8%-97.8%). The DCNN system showed more accurate and stable diagnostic performance than endoscopists in the prospective clinical comparison test. This system helped to slightly improve the diagnostic performance of senior endoscopists and considerably enhance that of junior endoscopists. CONCLUSION: The DCNN system for the assessment of the Forrest classification of PUB showed satisfactory diagnostic performance, which was slightly superior to that of senior endoscopists. It could therefore effectively assist junior endoscopists in making such diagnoses during gastroscopy.