Objective： We explored the mechanism of apoptosis in human esophageal cancer Ecal09 cells by resveratrol. Methods： The suppressive ratio of resveratrol on Ecal09 cells proliferation was evaluated by MTT colorimetric...Objective： We explored the mechanism of apoptosis in human esophageal cancer Ecal09 cells by resveratrol. Methods： The suppressive ratio of resveratrol on Ecal09 cells proliferation was evaluated by MTT colorimetric assay and morphology was observed by transmission electron microscope. The expression of survivin and bax was analyzed by RT-PCR and Flow Cytometry （FCM）. Results： Resveratrol inhibited the growth of Ecal09 calls in a dose-and time-dependent man- ner, and the suppressive ratio arrived at 76.42%. Morphological apoptosis could be observed after treated with resveratrol.The bulk of some drug-treated cells turned small and the nuclear chromatin became condensed and rnarginated. The results determined by RT-PCR and FCM showed that resveratrol could down-regulate surviving, while up-regulate bax. Conclusion： Resveratrol could induce the apoptosis of human esophageal cancer Ecal09 cells, and its possible molecular mechanisms might be related to modulation the expression of survivin and bax.展开更多
Automatic reading procedures in colon cells biopsies allow a faster and precise reading of microscopic biopsies. These procedures implement automatic image segmentation in order to classify cell types as cancerous or ...Automatic reading procedures in colon cells biopsies allow a faster and precise reading of microscopic biopsies. These procedures implement automatic image segmentation in order to classify cell types as cancerous or noncancerous. The authors have developed a new approach aiming to detect colon cancer cells derived from the ＂Snake＂ method but using a progressive division of the dimensions of the image to achieve rapid segmentation. The aim of the present paper was to classify different cancerous cell types based on nine morphological parameters and on probabilistic neural network. Three types of cells were used to assess the efficiency of our classifications models, including BH （Benign Hyperplasia）, IN （Intraepithelial Neoplasia） that is a precursor state for cancer, and Ca （Carcinoma） that corresponds to abnormal tissue proliferation （cancer）. Results showed that among the nine parameters used to classify cells, only three morphologic parameters （area, Xor convex and solidity） were found to be effective in distinguishing the three types of cells. In addition, classification of unknown cells was possible using this method.展开更多
基金Supported by a grant from the Science Research and Development Program of Hebei Province No 062761835
文摘Objective： We explored the mechanism of apoptosis in human esophageal cancer Ecal09 cells by resveratrol. Methods： The suppressive ratio of resveratrol on Ecal09 cells proliferation was evaluated by MTT colorimetric assay and morphology was observed by transmission electron microscope. The expression of survivin and bax was analyzed by RT-PCR and Flow Cytometry （FCM）. Results： Resveratrol inhibited the growth of Ecal09 calls in a dose-and time-dependent man- ner, and the suppressive ratio arrived at 76.42%. Morphological apoptosis could be observed after treated with resveratrol.The bulk of some drug-treated cells turned small and the nuclear chromatin became condensed and rnarginated. The results determined by RT-PCR and FCM showed that resveratrol could down-regulate surviving, while up-regulate bax. Conclusion： Resveratrol could induce the apoptosis of human esophageal cancer Ecal09 cells, and its possible molecular mechanisms might be related to modulation the expression of survivin and bax.
文摘Automatic reading procedures in colon cells biopsies allow a faster and precise reading of microscopic biopsies. These procedures implement automatic image segmentation in order to classify cell types as cancerous or noncancerous. The authors have developed a new approach aiming to detect colon cancer cells derived from the ＂Snake＂ method but using a progressive division of the dimensions of the image to achieve rapid segmentation. The aim of the present paper was to classify different cancerous cell types based on nine morphological parameters and on probabilistic neural network. Three types of cells were used to assess the efficiency of our classifications models, including BH （Benign Hyperplasia）, IN （Intraepithelial Neoplasia） that is a precursor state for cancer, and Ca （Carcinoma） that corresponds to abnormal tissue proliferation （cancer）. Results showed that among the nine parameters used to classify cells, only three morphologic parameters （area, Xor convex and solidity） were found to be effective in distinguishing the three types of cells. In addition, classification of unknown cells was possible using this method.
