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彻底评估。目前，人们对纳米材料与生物的相互作用知之甚少，为了弥补这一差距，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颗粒(AgNPs)可以被精确地设计成定制操纵物理化学性质。我假设纳米材料的生物活性取决于初级颗粒的大小、尺寸分布、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、剂量反应、摄取浓度和纳米颗粒暴露诱导的基因表达变化。此外，通过将胚胎斑马鱼暴露在银纳米颗粒(AgNPs)中，这些纳米颗粒被设计成具有高度特异性的物理化学特性，我将定义哪些特性是导致特定生物效应的原因。所有数据都将记录在纳米材料-生物相互作用(NBI)知识库中。NBI知识库是关于纳米材料表征、合成方法和在生物组织的多个层次上定义的纳米材料-生物相互作用的注释数据的仓库。我提交给NBI知识库的数据将有助于识别基于物理化学特性预测纳米材料暴露的生物效应的关键数据。

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.