Tetrazole, which is usually used as the bioisoster of carboxylic acid, is metabolically stable to many of the biological transformations that the functionality of carboxylic acid is susceptible to in the liver "

BackgroundMitochondrial fragmentation drastically regulates the viability of pancreatic cancer through a poorly understood mechanism. The present study used erlotinib to activate mitochondrial fragmentation and then investigated the downstream events that occurred in response to mitochondrial fragmentation.MethodsCell viability and apoptosis were determined via MTT assay, TUNEL staining and ELISA. Mitochondrial fragmentation was measured via an immunofluorescence assay and qPCR. siRNA transfection and pathway blockers were used to perform the loss-of-function assays.ResultsThe results of our study demonstrated that erlotinib treatment mediated cell apoptosis in the PANC-1 pancreatic cancer cell line via evoking mitochondrial fragmentation. Mechanistically, erlotinib application increased mitochondrial fission and reduced mitochondrial fusion, triggering mitochondrial fragmentation. Subsequently, mitochondrial fragmentation caused the overproduction of mitochondrial ROS (mROS). Interestingly, excessive mROS induced cardiolipin oxidation and mPTP opening, finally facilitating HtrA2/Omi liberation from the mitochondria into the cytoplasm, where HtrA2/Omi activated caspase-9-dependent cell apoptosis. Notably, neutralization of mROS or knockdown of HtrA2/Omi attenuated erlotinib-mediated mitochondrial fragmentation and favored cancer cell survival.ConclusionsTogether, our results identified the mROS-HtrA2/Omi axis as a novel signaling pathway that is activated by mitochondrial fragmentation and that promotes PANC-1 pancreatic cancer cell mitochondrial apoptosis in the presence of erlotinib.Electronic supplementary materialThe online version of this article (10.1186/s12935-018-0665-1) contains supplementary material, which is available to authorized users.

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The quality of Innovation and Entrepreneurship Education (IEE) in higher institutions is closely related to the degree to which the undergraduates (UGs) absorb relevant innovation and entrepreneurship knowledge and their entrepreneurial motivation. Thus, an effective Evaluation of Educational Quality (EEQ) is essential. In particular, fault tree analysis (FTA), a common EEQ approach, has some disadvantages, such as fault data reliance and insufficient uncertainties handleability. Thereupon, this article first puts forward a theoretical model based on the deep learning (DL) method to analyze the factors of IEE quality; consequently, based on the traditional FTA, fuzzy fault tree analysis (FFTA) is proposed to evaluate the reliability of IEE classroom teaching for college teachers and students. Finally, based on the top event of entrepreneurial teaching failure, the hyper-ellipsoid model is implemented to restrict the interval probability of basic events and describe the deviation of uncertain events. Furthermore, the model accuracy is verified by a questionnaire survey (QS), based upon which the factors of IEE quality are analyzed. The results show that the designed QS has good reliability, validity, and fitness; the path coefficients of cooperative ability to critical thinking and innovative thinking are 0.9 and 0.66, respectively, indicating that the students’ cooperative ability plays a vital role in the classroom teaching. By calculation, the probability of “teaching failure” in entrepreneurial classroom teaching is 0.395, 3, 0.462, and 5. To sum up, the proposed method can effectively and quantitatively evaluate the quality of IEE in higher institutions, thus providing a certain basis for formulating relevant improvement strategies. The purpose is to provide important technical support for improving the IEE quality.