Assessing the Interaction between Nanomaterials and Biological Systems in vivo to

纳米技术的应用正在融入我们的日常生活，但纳米材料使用的安全性仍有待全面评估。目前，人们对纳米材料与生物的相互作用知之甚少，为了弥合这一差距，我建议使用胚胎斑马鱼作为一种快速的体内系统，在分子水平上研究纳米材料的活性。高纯度，配体功能化的银纳米粒子（AgNP）可以被精确地设计为定制操纵的物理化学性质。我假设纳米材料的生物活性取决于初级粒径、粒径分布、表面基团的化学组成、表面电荷和团聚状态。为了验证这一假设，我将收集毒性数据，包括发病率和死亡率的剂量反应，摄取浓度，和纳米粒子的荧光诱导的基因表达的变化。此外，通过暴露胚胎斑马鱼银纳米粒子（AgNPs）工程表现出高度特定的物理化学性质，我将定义哪些属性是负责引起特定的生物效应。所有数据将记录在纳米材料-生物相互作用（NBI）知识库中。NBI知识库作为一个仓库，用于存储在生物组织的多个层次上定义的纳米材料表征、合成方法和纳米材料-生物相互作用的注释数据。我提交给NBI知识库的数据将有助于识别关键数据，以预测基于物理化学性质的纳米材料暴露的生物效应。

Nanotechnology applications are being incorporated into our daily lives, but the safety of nanomaterials usage still awaits thorough assessment. Currently, little is known about nanomaterial-biological interactions, and to bridge this gap, I propose using embryonic zebrafish as a rapid, in vivo system to investigate the activity of nanomaterials at the molecular level. High-purity, ligand-functionalized silver nanoparticles (AgNPs) can be precisely engineered to custom-manipulate physicochemical properties. I hypothesize that the biological activity of nanomaterials is dependent upon primary particle size, size distribution, chemical composition of surface groups, surface charge and state of agglomeration. To test this hypothesis, I will collect toxicity data including morbidity and mortality dose response, uptake concentration, and nanoparticle exposure-induced changes in gene expression. Additionally, by exposing embryonic zebrafish to silver nanoparticles (AgNPs) engineered to exhibit highly specific physicochemical properties, I will define which properties are responsible for causing specific biological effects. All data will be recorded in the Nanomaterial-Biological Interactions (NBI) knowledge base. The NBI knowledgebase serves as a warehouse for annotated data on nanomaterial characterization, synthesis methods, and nanomaterial-biological interactions defined at multiple levels of biological organization. The data I submit to the NBI knowledgebase will facilitate identification of key data for predicting the biological effects of nanomaterial exposure based on physicochemical properties.