Nanocages (NCs) have emerged as a new class of drug-carriers, with a wide range of possibilities in multi-modality medical treatments and theranostics. Nanocages can overcome such limitations as high toxicity caused by anti-cancer chemotherapy or by the nanocarrier itself, due to their unique characteristics. These properties consist of: (1) a high loading-capacity (spacious interior); (2) porous structure (analogous to openings between the bars of the cage); (3) enabling smart release (a key to unlock the cage); and (4) a low likelihood of unfavorable immune responses (the outside of the cage is safe). In this review, we cover different classes of NC structures such as virus-like particles (VLPs), protein NCs, DNA NCs, supramolecular nanosystems, hybrid metal-organic NCs, gold NCs, carbon-based NCs and silica NCs. Moreover, NC-assisted drug delivery including modification methods, drug immobilization, active targeting, and stimulus-responsive release mechanisms are discussed, highlighting advantages, disadvantages and challenges. Finally, translation of NCs into clinical applications, and an up-to-date assessment of the nanotoxicology considerations of NCs are presented.

Several methods utilizing common spatial pattern (CSP) algorithm have been presented for improving the identification of imagery movement patterns for brain computer interface applications. The present study focuses on improving a CSP-based algorithm for detecting the motor imagery movement patterns. A discriminative filter bank of CSP method using a discriminative sensitive learning vector quantization (DFBCSP-DSLVQ) system is implemented. Four algorithms are then combined to form three methods for improving the efficiency of the DFBCSP-DSLVQ method, namely the kernel linear discriminant analysis (KLDA), the kernel principal component analysis (KPCA), the soft margin support vector machine (SSVM) classifier and the generalized radial bases functions (GRBF) kernel. The GRBF is used as a kernel for the KLDA, the KPCA feature selection algorithms and the SSVM classifier. In addition, three types of classifiers, namely K-nearest neighbor (K-NN), neural network (NN) and traditional support vector machine (SVM), are employed to evaluate the efficiency of the classifiers. Results show that the best algorithm is the combination of the DFBCSP-DSLVQ method using the SSVM classifier with GRBF kernel (SSVM-GRBF), in which the best average accuracy, attained are 92.70% and 83.21%, respectively. Results of the Repeated Measures ANOVA shows the statistically significant dominance of this method at p < 0.05. The presented algorithms are then compared with the base algorithm of this study i.e. the DFBCSP-DSLVQ with the SVM-RBF classifier. It is concluded that the algorithms, which are based on the SSVM-GRBF classifier and the KLDA with the SSVM-GRBF classifiers give sufficient accuracy and reliable results.

No abstract

No abstract

K-complexes like spindles are hallmark patterns of stage 2 sleep. Due to correlation between these patterns and some diseases, it is necessary to develop algorithms to detect them. In this study, a new method is used to detect K-complexes automatically. 10 time-series and chaotic features were used in order to extract the K-complex waves from stage 2 sleep. To use the most effective features, feature space dimension is reduced with Sequential Forward Selection method. The reduced feature space is classified using Generalized Radial Basis Function Extreme Learning Machine (MELM-GRBF) algorithm. GRBFs make the modification of the RBF possible by adjusting a new parameter . We're applied this methodology to K-complex classification for the first time. The classifier gives noticeably better results compared to ELM-RBF method for sensitivity and accuracy .± . and . ± . , respectively.