BACKGROUND AND PURPOSEThe expression of P-glycoprotein (P-gp), encoded by the multidrug resistance 1 (MDR1) gene, is associated with the emergence of the MDR phenotype in cancer cells. We investigated whether metformin (1,1-dimethylbiguanide hydrochloride) down-regulates MDR1 expression in MCF-7/adriamycin (MCF-7/adr) cells. EXPERIMENTAL APPROACHMCF-7 and MCF-7/adr cells were incubated with metformin and changes in P-gp expression were determined at the mRNA, protein and functional level. Transient transfection assays were performed to assess its gene promoter activities, and immunoblot analysis to study its molecular mechanisms of action. KEY RESULTSMetformin significantly inhibited MDR1 expression by blocking MDR1 gene transcription. Metformin also significantly increased the intracellular accumulation of the fluorescent P-gp substrate rhodamine-123. Nuclear factor-kB (NF-kB) activity and the level of IkB degradation were reduced by metformin treatment. Moreover, transduction of MCF-7/adr cells with the p65 subunit of NF-kB induced MDR1 promoter activity and expression, and this effect was attenuated by metformin. The suppression of MDR1 promoter activity and protein expression was mediated through metformin-induced activation of AMP-activated protein kinase (AMPK). Small interfering RNA methods confirmed that reduction of AMPK levels attenuates the inhibition of MDR1 activation associated with metformin exposure. Furthermore, the inhibitory effects of metformin on MDR1 expression and cAMP-responsive element binding protein (CREB) phosphorylation were reversed by overexpression of a dominant-negative mutant of AMPK. CONCLUSIONS AND IMPLICATIONSThese results suggest that metformin activates AMPK and suppresses MDR1 expression in MCF-7/adr cells by inhibiting the activation of NF-kB and CREB. This study reveals a novel function of metformin as an anticancer agent. AbbreviationsACC, acetyl-CoA carboxylase; aicar, 5-aminoimidazole-4-carboxamide-1-b-D-ribofuranoside; AMPK, adenosine 5′-monophosphate-activated protein kinase; CRE, cAMP-responsive element; CREB, cAMP-responsive element binding protein; DN-AMPK, dominant negative AMPK; GSK-3b, glycogen synthase kinase-3b; MDR1, multidrug resistance 1; NF-kB, nuclear factor-kB; P-gp, P-glycoprotein; PKA, protein kinase A; Rh-123, rhodamine 123; TNF-a, tumour necrosis factor a

Background Visual inspection, lesion detection, and differentiation between malignant and benign features are key aspects of an endoscopist’s role. The use of machine learning for the recognition and differentiation of images has been increasingly adopted in clinical practice. This study aimed to establish convolutional neural network (CNN) models to automatically classify gastric neoplasms based on endoscopic images. Methods Endoscopic white-light images of pathologically confirmed gastric lesions were collected and classified into five categories: advanced gastric cancer, early gastric cancer, high grade dysplasia, low grade dysplasia, and non-neoplasm. Three pretrained CNN models were fine-tuned using a training dataset. The classifying performance of the models was evaluated using a test dataset and a prospective validation dataset. Results A total of 5017 images were collected from 1269 patients, among which 812 images from 212 patients were used as the test dataset. An additional 200 images from 200 patients were collected and used for prospective validation. For the five-category classification, the weighted average accuracy of the Inception-Resnet-v2 model reached 84.6 %. The mean area under the curve (AUC) of the model for differentiating gastric cancer and neoplasm was 0.877 and 0.927, respectively. In prospective validation, the Inception-Resnet-v2 model showed lower performance compared with the endoscopist with the best performance (five-category accuracy 76.4 % vs. 87.6 %; cancer 76.0 % vs. 97.5 %; neoplasm 73.5 % vs. 96.5 %; P  < 0.001). However, there was no statistical difference between the Inception-Resnet-v2 model and the endoscopist with the worst performance in the differentiation of gastric cancer (accuracy 76.0 % vs. 82.0 %) and neoplasm (AUC 0.776 vs. 0.865). Conclusion The evaluated deep-learning models have the potential for clinical application in classifying gastric cancer or neoplasm on endoscopic white-light images.

No abstract

No abstract

No abstract

No abstract

No abstract

Capsaicin inhibited the EGF-induced invasion and migration of human fibrosarcoma cells via EGFR-dependent FAK/Akt, PKC/Raf/ERK, p38 mitogen-activated protein kinase (MAPK), and AP-1 signaling, leading to the down-regulation of MMP-9 expression. These results indicate the role of capsaicin as a potent anti-metastatic agent, which can markedly inhibit the metastatic and invasive capacity of fibrosarcoma cells.