Effect of size, shape, and surface modification on cytotoxicity of gold nanoparticles to human Hep-2 and canine MDCK cells

There have been increasing interests in applying gold nanoparticles in biological research, drug delivery, and therapy. As the interaction of gold nanoparticles with cells relies on properties of nanoparticles, the cytotoxicity is complex and still under debating. In this work, we investigate the cytotoxicity of gold nanoparticles of different encapsulations, surface charge states, sizes and shapes to both human HEp-2 and canine MDCK cells. We found that cetyltrimethylammonium-bromide- (CTAB-) encapsulated gold nanorods (GNRs) were relatively higher cytotoxic than GNRs undergone further polymer coating and citrate stabilized gold nanospheres (GNSs). The toxicity of CTAB-encapsulated GNRs was mainly caused by CTAB on GNRs’ surface but not free CTAB in the solution. No obvious difference was found among GNRs of different aspect ratios. Time-lapse study revealed that cell death caused by GNRs occurred predominately within one hour through apoptosis, whereas cell death by free CTAB was in a time- and dose-dependent manner. Both positively and negatively surface-charged polymer-coated GNRs (PSS-GNRs and PAH-PSS-GNRs) showed similar levels of cytotoxic, suggesting the significance of surface functionality rather than surface charge in this case

Effect of size, shape, and surface modification on cytotoxicity of gold nanoparticles to human Hep-2 and canine MDCK cells

There have been increasing interests in applying gold nanoparticles in biological research, drug delivery, and therapy. As the interaction of gold nanoparticles with cells relies on properties of nanoparticles, the cytotoxicity is complex and still under debating. In this work, we investigate the cytotoxicity of gold nanoparticles of different encapsulations, surface charge states, sizes and shapes to both human HEp-2 and canine MDCK cells. We found that cetyltrimethylammonium-bromide- (CTAB-) encapsulated gold nanorods (GNRs) were relatively higher cytotoxic than GNRs undergone further polymer coating and citrate stabilized gold nanospheres (GNSs). The toxicity of CTAB-encapsulated GNRs was mainly caused by CTAB on GNRs’ surface but not free CTAB in the solution. No obvious difference was found among GNRs of different aspect ratios. Time-lapse study revealed that cell death caused by GNRs occurred predominately within one hour through apoptosis, whereas cell death by free CTAB was in a time- and dose-dependent manner. Both positively and negatively surface-charged polymer-coated GNRs (PSS-GNRs and PAH-PSS-GNRs) showed similar levels of cytotoxic, suggesting the significance of surface functionality rather than surface charge in this case

Effect of size, shape, and surface modification on cytotoxicity of gold nanoparticles to human Hep-2 and canine MDCK cells

There have been increasing interests in applying gold nanoparticles in biological research, drug delivery, and therapy. As the interaction of gold nanoparticles with cells relies on properties of nanoparticles, the cytotoxicity is complex and still under debating. In this work, we investigate the cytotoxicity of gold nanoparticles of different encapsulations, surface charge states, sizes and shapes to both human HEp-2 and canine MDCK cells. We found that cetyltrimethylammonium-bromide- (CTAB-) encapsulated gold nanorods (GNRs) were relatively higher cytotoxic than GNRs undergone further polymer coating and citrate stabilized gold nanospheres (GNSs). The toxicity of CTAB-encapsulated GNRs was mainly caused by CTAB on GNRs’ surface but not free CTAB in the solution. No obvious difference was found among GNRs of different aspect ratios. Time-lapse study revealed that cell death caused by GNRs occurred predominately within one hour through apoptosis, whereas cell death by free CTAB was in a time- and dose-dependent manner. Both positively and negatively surface-charged polymer-coated GNRs (PSS-GNRs and PAH-PSS-GNRs) showed similar levels of cytotoxic, suggesting the significance of surface functionality rather than surface charge in this case

Bayesian Learning of Impacts of Self-Exciting Jumps in Returns and Volatility

The paper proposes a new class of continuous-time asset pricing models where negative jumps play a crucial role. Whenever there is a negative jump in asset returns, it is simultaneously passed on to diffusion variance and the jump intensity, generating self-exciting co-jumps of prices and volatility and jump clustering. To properly deal with parameter uncertainty and in-sample over-fitting, a Bayesian learning approach combined with an efficient particle filter is employed. It not only allows for comparison of both nested and non-nested models, but also generates all quantities necessary for sequential model analysis. Empirical investigation using S&P 500 index returns shows that volatility jumps at the same time as negative jumps in asset returns mainly through jumps in diffusion volatility. We find substantial evidence for jump clustering, in particular, after the recent financial crisis in 2008, even though parameters driving dynamics of the jump intensity remain difficult to identify.Self-Excitation, Volatility Jump, Jump Clustering, Extreme Events, Parameter Learning, Particle Filters, Sequential Bayes Factor, Risk Management

Information Cascades on Arbitrary Topologies

In this paper, we study information cascades on graphs. In this setting, each node in the graph represents a person. One after another, each person has to take a decision based on a private signal as well as the decisions made by earlier neighboring nodes. Such information cascades commonly occur in practice and have been studied in complete graphs where everyone can overhear the decisions of every other player. It is known that information cascades can be fragile and based on very little information, and that they have a high likelihood of being wrong. Generalizing the problem to arbitrary graphs reveals interesting insights. In particular, we show that in a random graph $G(n,q)$, for the right value of $q$, the number of nodes making a wrong decision is logarithmic in $n$. That is, in the limit for large $n$, the fraction of players that make a wrong decision tends to zero. This is intriguing because it contrasts to the two natural corner cases: empty graph (everyone decides independently based on his private signal) and complete graph (all decisions are heard by all nodes). In both of these cases a constant fraction of nodes make a wrong decision in expectation. Thus, our result shows that while both too little and too much information sharing causes nodes to take wrong decisions, for exactly the right amount of information sharing, asymptotically everyone can be right. We further show that this result in random graphs is asymptotically optimal for any topology, even if nodes follow a globally optimal algorithmic strategy. Based on the analysis of random graphs, we explore how topology impacts global performance and construct an optimal deterministic topology among layer graphs