智能型金纳米棒荧光探针的制备及其肿瘤细胞成像研究

开发能特异和灵敏检测基质金属蛋白酶-2(MMP-2)的智能型荧光分子探针,对于癌症的早期精准诊断与治疗至关重要。本文基于MMP-2特异性识别的多肽底物,利用一价铜离子催化的"点击"化学反应将荧光素(FITC)和多肽底物GPLGVRGY相偶联,与SH-PEG-COOH修饰的金纳米棒在EDC和Sulfo-NHS的作用下通过酰胺化反应,制备得到一种新的MMP-2特异性响应的荧光共振能量转移(FRET)金纳米棒探针GNR@FITC。体外重组酶检测实验、MTT实验及细胞共聚焦显微成像结果表明,该探针对MMP-2过表达小鼠乳腺癌细胞4T1表现出较好的特异性和检测灵敏度,具有进一步开发应用于乳腺癌早期诊断的极大潜力。     

A novel stable value iteration-based approximate dynamic programming algorithm for discrete-time nonlinear systems

The convergence and stability of a value-iteration-based adaptive dynamic programming(ADP) algorithm are considered for discrete-time nonlinear systems accompanied by a discounted quadric performance index. More importantly than sufficing to achieve a good approximate structure, the iterative feedback control law must guarantee the closed-loop stability. Specifically, it is firstly proved that the iterative value function sequence will precisely converge to the optimum.Secondly, the necessary and sufficient condition of the optimal value function serving as a Lyapunov function is investigated. We prove that for the case of infinite horizon, there exists a finite horizon length of which the iterative feedback control law will provide stability, and this increases the practicability of the proposed value iteration algorithm. Neural networks(NNs) are employed to approximate the value functions and the optimal feedback control laws, and the approach allows the implementation of the algorithm without knowing the internal dynamics of the system. Finally, a simulation example is employed to demonstrate the effectiveness of the developed optimal control method.