Engineering H2O2 Self-Supplying Nanotheranostic Platform for Targeted and Imaging-Guided Chemodynamic Therapy.

Developing highly efficient chemodynamic therapy (CDT)-based theranostic technology for cancer treatment is highly desired but still challenging. A novel nanotheranostic platform is constructed for enhanced CDT by engineering hybrid CaO2 and Fe3O4 nanoparticles with a hyaluronate acid (HA) stabilizer and NIR fluorophore label. This design not only enables the nanotheranostic agent to afford highly efficient CDT against tumor cells but also confers NIR fluorescence (NIRF) and magnetic resonance (MR) bimodal imaging for in vivo visualization of CDT. Moreover, the use of the HA stabilizer allows for the facile synthesis of the nanotheranostic agent with excellent biocompatibility and active targetability. The nanotheranaostic agent possesses a high capacity of self-supplying H2O2 and producing •OH in acidic conditions, while retaining the desired stability under physiological conditions. It also demonstrates high selectivity to tumor cells via CDT with minimized toxicity to normal cells. In vivo studies reveal that our nanotheranaostic agent exhibits efficacious tumor growth inhibition via a CDT mechanism with favorable biosafety. Moreover, in vivo visualization of the CDT progress via NIRF and MR bimodal imaging demonstrates specific targeting and treatment of tumors. The developed H2O2 self-supplying, active targeting, and bimodal imaging nanotheranostic platform holds the potential as a highly efficient strategy for CDT of cancer.

Construction of an efficacious model for a nondestructive identification of traditional Chinese medicines Liuwei Dihuang pills from different manufacturers using near-infrared spectroscopy and moving window partial least-squares discriminant analysis.

This paper reports on the construction of an efficacious model for a non-invasive identification of traditional Chinese medicines, Liuwei Dihuang pills from different manufacturers, on the basis of near-infrared spectra (NIRS) coupled with moving window partial least-squares discriminant analysis (MWPLSDA). Considering the continuity of near-infrared spectral measurements, MWPLSDA is used to identify continuous and highly classification-related information intervals, a simple, yet effective classification model that can be developed for identifying accurate 150 Liuwei Dihuang pills from five different manufacturers. Meanwhile, the method is compared with some traditional pattern-recognition methods including principal component analysis (PCA), linear discriminant analysis (LDA) and partial least-squares discriminant analysis (PLSDA). The obtained results show that the method not only can reduce the operation time, but also significantly improves the classification accuracy. Hence, the nondestructive method can be expected to be promising for more practical applications on quality control and the discrimination of traditional Chinese medicine.

Chemometric classification of traditional Chinese medicines by their geographical origins using near-infrared reflectance spectra.

Some raw materials that have different places of production for the plant sources of the drugs Astragalus membranaceus and ginseng have been studied, based on their near-infrared reflectance spectra. The experimentally recorded spectra represent heavily ill-posed and highly correlative data sets. Three related methods, i.e. the Fisher linear discriminant analysis (FLDA), the ridge-type linear discriminant analysis (RLDA) and a newly proposed penalized ridge-type linear discriminant analysis (PRLDA), have been investigated. FLDA over-fits for the training objects of the two data sets to a high extent and is unstable for the predictive objects of the two data sets. RLDA shows obvious improvement in terms of over-fitting and unstability, but the stability for the predictive objects of the two data sets is too sensitive to their ridge-type penalized weights, tending to produce erroneous discrimination results. The proposed PRLDA can circumvent the two aforementioned problems with a large domain of penalized weights for correct discriminant analysis of the two data sets studied. The combination of the PRLDA method and near infrared reflectance spectroscopy can be adapted for the discrimination of the production places of plant sources of these drugs.