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Cancer is caused by uncontrolled cell proliferation which has the potential to occur in different tissues and spread into surrounding and distant tissues. Despite the current advances in the field of anticancer agents, rapidly developing resistance against different chemotherapeutic drugs and significantly higher off-target effects cause millions of deaths every year. Osthol is a natural coumarin isolated from Apiaceaous plants which has demonstrated several pharmacological effects, such as antineoplastic, anti-inflammatory and antioxidant properties. We have attempted to summarize up-to-date information related to pharmacological effects and molecular mechanisms of osthol as a lead compound in managing malignancies. Electronic databases, including PubMed, Cochrane library, ScienceDirect and Scopus were searched for in vitro, in vivo and clinical studies on anticancer effects of osthol. Osthol exerts remarkable anticancer properties by suppressing cancer cell growth and induction of apoptosis. Osthol’s protective and therapeutic effects have been observed in different cancers, including ovarian, cervical, colon and prostate cancers as well as chronic myeloid leukemia, lung adenocarcinoma, glioma, hepatocellular, glioblastoma, renal and invasive mammary carcinoma. A large body of evidence demonstrates that osthol regulates apoptosis, proliferation and invasion in different types of malignant cells which are mediated by multiple signal transduction cascades. In this review, we set spotlights on various pathways which are targeted by osthol in different cancers to inhibit cancer development and progression.

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In this research, an emotion recognition system is developed based on valence/arousal model using electroencephalography (EEG) signals. EEG signals are decomposed into the gamma, beta, alpha and theta frequency bands using discrete wavelet transform (DWT), and spectral features are extracted from each frequency band. Principle component analysis (PCA) is applied to the extracted features by preserving the same dimensionality, as a transform, to make the features mutually uncorrelated. Support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN) are used to classify emotional states. The crossvalidated SVM with radial basis function (RBF) kernel using extracted features of 10 EEG channels, performs with 91.3% accuracy for arousal and 91.1% accuracy for valence, both in the beta frequency band. Our approach shows better performance compared to existing algorithms applied to the "DEAP" dataset.

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