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Abasic (AP) sites are one of the most common DNA lesions in cells. Aldehyde-reactive alkoxyamines capture AP sites and block the activity of APE1, the enzyme responsible for initiating their repair. Blocking the APE1 repair of AP sites leads to cell death, and it is an actively investigated approach for treating cancer. However, unselective AP site capture in different cells produces side effects and limits the application of alkoxyamines in chemotherapy. Herein we take advantage of the higher glutathione (GSH) concentration in cancer cells over normal cells to develop GSH-inducible agents that selectively kill cancer cells. 2,4-Dinitrobenzenesulfonamide caged coumarin-based alkoxyamines 1 and 2 are selectively revealed by GSH to release SO2 and fluorescent coumarin-based alkoxyamines 3 and 4 that trap AP sites in cells. GSH-directed AP site trapping and SO2 release result in selective cytotoxicity (defined as IC50WI38/IC50H1299) against H1299 lung cancer cells over normal WI38 lung cells, ranging from 1.8 to 2.8 for 1 and 2. The alkylating agent methylmethanesulfonate (MMS) promotes the formation of AP sites in cells and enhances the cytotoxicity of agent 1 in a dose-dependent way. Moreover, the comet assay and γH2AX assay suggest that AP adducts form a highly toxic DNA interstrand cross-link (ICL) upon photolysis, leading to further cell death. DNA flow cytometric analysis showed that 1 promoted cell apoptosis in the early stage and induced G2/M phase cell-cycle arrest. The 2,4-dinitrobenzenesulfonamide-caged alkoxyamines exhibited selective antitumor activity and photocytotoxicity in cancer cells, illuminating their potential as GSH-directed chemotherapeutic agents.

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In order to classify the quality of corn kernels in an affordable, convenient, and accurate manner, a method based on image analysis and support vector machine is proposed. A total of 129 corn kernels with Grade A, Grade B, and Grade C are used for the experiments. Six typical characteristic parameters of samples are extracted as the characteristic groups. Four different classifiers are applied and compared: support vector machine-genetic algorithm, support vector machine-particle swarm optimization, support vector machine-grid search optimization, and back-propagation neural networks. Experimental results show that the support vector machine and back-propagation neural networks without parameter optimization have the same classification accuracy rates of 92.31%. The classification accuracies are improved using the support vector machine optimization algorithms. The average correct classification rates of support vector machine-genetic algorithm and support vector machine-particle swarm optimization are all 97.44%, while the correct classification rate of support vector machine-grid search achieves 94.87%. It is concluded that the support vector machine algorithm based on parameter optimization is superior to back-propagation neural networks algorithm, and the parameter optimization effects of genetic algorithm and particle swarm optimization are better than grid search method. With a relatively small number of samples, the support vector machine-genetic algorithm and support vector machineparticle swarm optimization algorithms can improve the grading accuracy of corn kernels.