Enhanced Nuclear Accumulation of Doxorubicin Delivered by pH-Triggered Polydopamine-Shelled Mesoporous Silica for Chemo-Photothermal Therapy.

Adequate delivery of therapeutic agents to their intended molecular targets is crucial in tumor therapy. Versatile drug carriers need to overcome the challenges coming from the systemic circulation, membrane barriers, and endo-lysosomal degradation. Herein, hyaluronic acid-conjugated polydopamine (HA-PDA)-shelled mesoporous silica nanoparticles encapsulated with doxorubicin (MSNs-DOX) were successfully fabricated for targeted tumor therapy. Compared with reported studies focusing on the pH-sensitive release in tumors, we especially revealed the significant role of lysosomal release in DOX nuclear accumulation. After active targeting and CD44-mediated endocytosis in tumor cells, the PDA layer of the nanoparticles would be peeled off to trigger drug release owing to MSNs gatekeeper in acidic lysosomes. Subsequently, DOX molecules passively diffused into nuclei. The intracellular DOX transportation was evidenced by DOX accumulation in nuclei, lysosomal location of nanoparticles, and lysosome acidification inhibition test. After discharging of the cargoes from nanoparticles, PDA shells from residual nanoparticles were able to produce localized hyperthermia under NIR irradiation entrapped in lysosomes, inducing synergistic chemo-photothermal effect. Under NIR treatment, HA-PDA@MSNs-DOX presented a prominent tumor inhibition rate without obvious side effects. This study indicated the potent nuclear delivery and synergetic chemo-photothermal therapy achieved by HA-PDA-shelled MSNs.

[Classification of hyperspectral imagery based on ant colony compositely optimizing SVM in spatial and spectral features].

A novel classification algorithm of hyperspectral imagery based on ant colony compositely optimizing support vector machine in spatial and spectral features was proposed. Two types of virtual ants searched for the bands combination with the maximum class separation distance and heterogeneous samples in spatial and spectral features alternately. The optimal characteristic bands were extracted, and bands redundancy of hyperspectral imagery decreased. The heterogeneous samples were eliminated form the training samples, and the distribution of samples was optimized in feature space. The hyperspectral imagery and training samples which had been optimized were used in classification algorithm of support vector machine, so that the class separation distance was extended and the accuracy of classification was improved. Experimental results demonstrate that the proposed algorithm, which acquires an overall accuracy 95.45% and Kappa coefficient 0.925 2, can obtain greater accuracy than traditional hyperspectral image classification algorithms.