ABSTRACT
Substantial progress in the use of chemo-photodynamic nano-drug delivery systems (nano-DDS) for the treatment of the malignant breast cancer has been achieved. The inability to customize precise nanostructures, however, has limited the therapeutic efficacy of the prepared nano-DDS to date. Here, we report a structurally defined tandem-responsive chemo-photosensitive co-nanoassembly to eliminate primary breast tumor and prevent lung metastasis. This both-in-one co-nanoassembly is prepared by assembling a biocompatible photosensitive derivative (pheophorbide-diphenylalanine peptide, PPA-DA) with a hypoxia-activated camptothecin (CPT) prodrug [(4-nitrophenyl) formate camptothecin, N-CPT]. According to computational simulations, the co-assembly nanostructure is not the classical core-shell type, but consists of many small microphase regions. Upon exposure to a 660 nm laser, PPA-DA induce high levels of ROS production to effectively achieve the apoptosis of normoxic cancer cells. Subsequently, the hypoxia-activated N-CPT and CPT spatially penetrate deep into the hypoxic region of the tumor and suppress hypoxia-induced tumor metastasis. Benefiting from the rational design of the chemo-photodynamic both-in-one nano-DDS, these nanomedicines exhibit a promising potential in the inhibition of difficult-to-treat breast tumor metastasis in patients with breast cancer.
ABSTRACT
Hypoxia, a salient feature of most solid tumors, confers invasiveness and resistance to the tumor cells. Oxygen-consumption photodynamic therapy (PDT) suffers from the undesirable impediment of local hypoxia in tumors. Moreover, PDT could further worsen hypoxia. Therefore, developing effective strategies for manipulating hypoxia and improving the effectiveness of PDT has been a focus on antitumor treatment. In this review, the mechanism and relationship of tumor hypoxia and PDT are discussed. Moreover, we highlight recent trends in the field of nanomedicines to modulate hypoxia for enhancing PDT, such as oxygen supply systems, down-regulation of oxygen consumption and hypoxia utilization. Finally, the opportunities and challenges are put forward to facilitate the development and clinical transformation of PDT.
ABSTRACT
Sleep quality is closely related to human health. It is very important to correctly discriminate the sleep stages for evaluating sleep quality, diagnosing and analyzing the sleep-related disorders. Polysomnography (PSG) signals are commonly used to record and analyze sleep stages. Effective feature extraction and representation is one of the most important steps to improve the performance of sleep stage classification. In this work, a collaborative representation (CR) algorithm was adopted to re-represent the original extracted features from electroencephalogram sig- nal, and then the kernel entropy component analysis (KECA) algorithm was further used to reduce the feature dimension of CR-feature. To evaluate the performance of CR-KECA, we compared the original feature, CR feature and readied CR feature (CR-PCA) after principal component analysis (PCA). The experimental results of sleep stage classification indicated that the CR-KECA method achieved the best performance compared with the original feature, CR feature, and CR-PCA feature with the classification accuracy of 68.74 +/- 0.46%, sensitivity of 68.76 +/- 0.43% and specificity of 92.19 +/- 0.11%. Moreover, CR algorithm had low computational complexity, and the feature dimension after KECA was much smaller, which made CR-KECA algorithm suitable for the analysis of large-scale sleep data.