Electroencephalogram (EEG), as a direct response to brain activity, can be used to detect mental states and physical conditions. Among various EEG-based emotion recognition studies, due to the nonlinear, non-stationary and the individual difference of EEG signals, traditional recognition methods still have the disadvantages of complicated feature extraction and low recognition rates. Thus, this paper first proposes a novel concept of electrode-frequency distribution maps (EFDMs) with short-time Fourier transform (STFT). Residual block based deep convolutional neural network (CNN) is proposed for automatic feature extraction and emotion classification with EFDMs. Aim at the shortcomings of the small amount of EEG samples and the challenge of differences in individual emotions, which makes it difficult to construct a universal model, this paper proposes a cross-datasets emotion recognition method of deep model transfer learning. Experiments carried out on two publicly available datasets. The proposed method achieved an average classification score of 90.59% based on a short length of EEG data on SEED, which is 4.51% higher than the baseline method. Then, the pre-trained model was applied to DEAP through deep model transfer learning with a few samples, resulted an average accuracy of 82.84%. Finally, this paper adopts the gradient weighted class activation mapping (Grad-CAM) to get a glimpse of what features the CNN has learned during training from EFDMs and concludes that the high frequency bands are more favorable for emotion recognition. Proposed a novel concept of EFDMs with STFT based on multiple channel EEG signals. Constructed four residual blocks based CNN for emotion recognition. Performed cross-datasets emotion recognition based on deep model transfer learning. Studied the number of training samples used for cross-datasets emotion recognition. Obtained the key EEG information automatically based on EFDMs and Grad-CAM.

Most present drug-phospholipid delivery systems were based on a water-insoluble drug-phospholipid complex but rarely water-soluble drug-phospholipid complex. Mitomycin C (MMC) is a water-soluble anticancer drug extensively used in first-line chemotherapy but is limited by its poor aqueous stability in vitro, rapid elimination from the body, and lack of target specificity. In this article, we report the MMC-soybean phosphatidylcholine complex-loaded PEG-lipid-PLA hybrid nanoparticles (NPs) with Folate (FA) functionalization (FA-PEG-PE-PLA NPs@MMC-SPC) for targeted drug delivery and dual-controlled drug release. FA-PEG-PE-PLA NPs@MMC-SPC comprise a hydrophobic core (PLA) loaded with MMC-SPC, an amphiphilic lipid interface layer (PE), a hydrophilic shell (PEG), and a targeting ligand (FA) on the surface, with a spherical shape, a nanoscaled particle size, and high drug encapsulation efficiency of almost 95%. The advantage of the new drug delivery systems is the early phase controlled drug release by the drug-phospholipid complex and the late-phase controlled drug release by the pH-sensitive polymer-lipid hybrid NPs. In vitro cytotoxicity and hemolysis assays demonstrated that the drug carriers were cytocompatible and hemocompatible. The pharmacokinetics study in rats showed that FA-PEG-PE-PLA NPs@MMC-SPC significantly prolonged the blood circulation time compared to that of the free MMC. More importantly, FA-PEG-PE-PLA NPs@MMC-SPC presented the enhanced cell uptake/cytotoxicity in vitro and superior tumor accumulation/therapeutic efficacy in vivo while reducing the systemic toxicity. A significant accumulation of MMC in the nuclei as the site of MMC action achieved in FA-PEG-PE-PLA NPs@MMC-SPC made them ideal for MMC drug delivery. This study may provide an effective strategy for the design and development of the water-soluble drug-phospholipid complex-based targeted drug delivery and sustained/controlled drug release.

Integrating advantages of mitomycin C (MMC)-phospholipid complex for increased drug encapsulation efficiency and reduced premature drug release, DSPE-PEG-folate (DSPE-PEG-FA) for specific tumor targeting, we reported a simple one-pot self-assembly route to prepare the MMC-phospholipid complex-loaded DSPE-PEG-based nanoparticles (MP-PEG-FA NPs). Both confocal imaging and flow cytometry demonstrated that MMC was distributed into nuclei after cellular uptake and intracellular drug delivery. More importantly, the systemically administered MP-PEG-FA NPs led to increased blood persistence and enhanced tumor accumulation in HeLa tumor-bearing nude mice. This study introduces a simple and effective strategy to design the anticancer drug-phospholipid complex-based targeted drug delivery system for sustained/controlled drug release.

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