Construction of multifunctional stimuli-responsive nanotherapeutics enabling improved intratumoral penetration of therapeutics and reversal of multiple-drug resistance (MDR) is potent to achieve effective cancer treatment. Herein, we report a general method to synthesize pH-dissociable calcium carbonate (CaCO3) hollow nanoparticles with amorphous CaCO3 as the template, gallic acid (GA) as the organic ligand, and ferrous ions as the metallic center via a one-pot coordination reaction. The obtained GA–Fe@CaCO3 exhibits high loading efficiencies to both oxidized cisplatin prodrug and doxorubicin, yielding drug loaded GA–Fe@CaCO3 nanotherapeutics featured in pH-responsive size shrinkage, drug release, and Fenton catalytic activity. Compared to nonresponsive GA–Fe@silica nanoparticles prepared with silica nanoparticles as the template, such GA–Fe@CaCO3 confers significantly improved intratumoral penetration capacity. Moreover, both types of drug-loaded GA–Fe@CaCO3 nanotherapeutics exhibit synergistic therapeutic efficacies to corresponding MDR cancer cells because of the GA–Fe mediated intracellular oxidative stress amplification that could reduce the efflux of engulfed drugs by impairing the mitochondrial-mediated production of adenosine triphosphate (ATP). As a result, it is found that the doxorubicin loaded GA–Fe@CaCO3 exhibits superior therapeutic effect towards doxorubicin-resistant 4T1 breast tumors via combined chemodynamic and chemo-therapies. This work highlights the preparation of pH-dissociable CaCO3-based nanotherapeutics to enable effective tumor penetration for enhanced treatment of drug-resistant tumors.
The identification of the Hammerstein–Wiener (H-W) systems based on the nonuniform input–output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled-data H-W system. In addition, the product terms of the parameters in the H-W system are inevitable. In order to solve the problem, the key-term separation is applied and two algorithms are proposed. One is the key-term-based forgetting factor stochastic gradient (KT-FFSG) algorithm based on the gradient search. The other is the key-term-based hierarchical forgetting factor stochastic gradient (KT-HFFSG) algorithm. Compared with the KT-FFSG algorithm, the KT-HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective. 相似文献
Self-assembled peptide hydrogels represent the realization of peptide nanotechnology into biomedical products. There is a continuous quest to identify the simplest building blocks and optimize their critical gelation concentration (CGC). Herein, a minimalistic, de novo dipeptide, Fmoc-Lys(Fmoc)-Asp, as an hydrogelator with the lowest CGC ever reported, almost fourfold lower as compared to that of a large hexadecapeptide previously described, is reported. The dipeptide self-assembles through an unusual and unprecedented two-step process as elucidated by solid-state NMR and molecular dynamics simulation. The hydrogel is cytocompatible and supports 2D/3D cell growth. Conductive composite gels composed of Fmoc-Lys(Fmoc)-Asp and a conductive polymer exhibit excellent DNA binding. Fmoc-Lys(Fmoc)-Asp exhibits the lowest CGC and highest mechanical properties when compared to a library of dipeptide analogues, thus validating the uniqueness of the molecular design which confers useful properties for various potential applications. 相似文献