Acetyl-11-keto-β-boswellic acid inhibits proliferation and induces apoptosis of gastric cancer cells through the phosphatase and tensin homolog/Akt/cyclooxygenase-2 signaling pathway

BACKGROUND Gastric cancer is one of the most common malignant tumors of the digestive system worldwide,posing a serious danger to human health.Cyclooxygenase(COX)-2 plays an important role in the carcinogenesis and progression of gastric cancer.Acetyl-11-keto-β-boswellic acid(AKBA)is a promising drug for cancer therapy,but its effects and mechanism of action on human gastric cancer remain unclear.AIM To evaluate whether the phosphatase and tensin homolog(PTEN)/Akt/COX-2 signaling pathway is involved in the anti-tumor effect of AKBA in gastric cancer.METHODS Human poorly differentiated BGC823 and moderately differentiated SGC7901 gastric cancer cells were routinely cultured in Roswell Park Memorial Institute 1640 medium supplemented with 10%fetal bovine serum and 1%penicillin/streptomycin.Gastric cancer cell proliferation was determined by methyl thiazolyl tetrazolium colorimetric assay.Apoptosis was measured by flow cytometry.Cell migration was assessed using the wound-healing assay.Expression of Bcl-2,Bax,proliferating cell nuclear antigen,PTEN,p-Akt,and COX-2 were detected by Western blot analysis.A xenograft nude mouse model of human gastric cancer was established to evaluate the anti-cancer effect of AKBA RESULTS AKBA significantly inhibited the proliferation of gastric cancer cells in a dose-and time-dependent manner,inhibited migration in a time-dependent manner,and induced apoptosis in a dose-dependent manner in vitro;it also inhibited tumor growth in vivo.AKBA up-regulated the expression of PTEN and Bax,and downregulated the expression of proliferating cell nuclear antigen,Bcl-2,p-Akt,and COX-2 in a dose-dependent manner.The PTEN inhibitor bpv(Hopic)reversed the high expression of PTEN and low expression of p-Akt and COX-2 that were induced by AKBA.The Akt inhibitor MK2206 combined with AKBA downregulated the expression of p-Akt and COX-2,and the combined effect was better than that of AKBA alone.CONCLUSION AKBA inhibits the proliferation and migration and promotes the apoptosis of gastric cancer cells through the PTEN/Akt/COX-2 signaling pathway.

Application of endoscopic ultrasonography for detecting esophageal lesions based on convolutional neural network

BACKGROUND A convolutional neural network(CNN) is a deep learning algorithm based on the principle of human brain visual cortex processing and image recognition.AIM To automatically identify the invasion depth and origin of esophageal lesions based on a CNN.METHODS A total of 1670 white-light images were used to train and validate the CNN system.The method proposed in this paper included the following two parts:(1)Location module,an object detection network,locating the classified main image feature regions of the image for subsequent classification tasks;and(2) Classification module,a traditional classification CNN,classifying the images cut out by the object detection network.RESULTS The CNN system proposed in this study achieved an overall accuracy of 82.49%,sensitivity of 80.23%,and specificity of 90.56%.In this study,after follow-up pathology,726 patients were compared for endoscopic pathology.The misdiagnosis rate of endoscopic diagnosis in the lesion invasion range was approximately 9.5%;41 patients showed no lesion invasion to the muscularis propria,but 36 of them pathologically showed invasion to the superficial muscularis propria.The patients with invasion of the tunica adventitia were all treated by surgery with an accuracy rate of 100%.For the examination of submucosal lesions,the accuracy of endoscopic ultrasonography(EUS) was approximately 99.3%.Results of this study showed that EUS had a high accuracy rate for the origin of submucosal lesions,whereas the misdiagnosis rate was slightly high in the evaluation of the invasion scope of lesions.Misdiagnosis could be due to different operating and diagnostic levels of endoscopists,unclear ultrasound probes,and unclear lesions.CONCLUSION This study is the first to recognize esophageal EUS images through deep learning,which can automatically identify the invasion depth and lesion origin of submucosal tumors and classify such tumors,thereby achieving good accuracy.In future studies,this method can provide guidance and help to clinical endoscopists.