## Other Nanotechnology Applications
### Empirical Bayes Estimates Using Jackknife and Bootstrap Methods  
[PERSON]  
Department of Mathematics and Statistics, University of South Florida, Tampa, FL 33312, USA  
In statistics or data science, our main goal is to develop and study perfect statistical models that can understand and address some natural phenomena. The problem we usually face is whether to use the existing model that may allow closed-form solutions or to describe the phenomenon more accurately, which would often impede the computation of explicit answers. Obtaining methods that result in useful qualitative and quantitative understanding of realistic complex systems is difficult, and obtaining exact analytical tools is not practical either. Because of this problem, researchers have relied on simulation-based methods. Computer simulation methods come into the picture to address the problem. In our project, we have used parametric Bayesian and also some empirical methods to fetch some information about the true state of nature. Given a set of observations, we will try to identify the probability density function (P.D.F) driven by data through the parametric analysis. We will then perform a Bayesian analysis and compare our Bayes estimate with the maximum likelihood estimate (MLE). Finally, we will use empirical Bayes methods and compare our estimates with the other estimates. In the era of big data with the invention of new improved numerical techniques, Bayesian analysis is gaining popularity. Bayesian statistics is a mathematical procedure that uses uncertainties in terms of probabilities to statistical problems. It provides people with the tools to update their beliefs in the evidence of new data. The classical approach to statistical inference is based on the random sample alone, whereas in the Bayesian approach, we combine any new information that is available with the prior information that we have to form the basis for the statistical procedure. Review of Targeted Therapy Approaches in the Application of Gold Nanoparticles for Lung Cancer Treatment  
[PERSON] 1, [PERSON] 2,3,4,5 and [PERSON] 2,3,4,5  
1 Department of Medical Engineering, College of Engineering, USF, Tampa, FL 33613, USA  
2 [PERSON] VA Hospital, Tampa, FL 33612, USA  
3 College of Pharmacy Graduate Programs  
4 Center for Research & Education in Nanobioengineering  
5 Dept of Internal Medicine, USF, Tampa, FL 33612, USA  
Nanomedicine is an emerging field in medical research that aims to offer new therapeutic approaches through its application in drug delivery systems. Nanoparticles are the core of nanomedicine and their use in the treatment of diseases such as cancer has been preponderant lately. Previously, nanoparticles-conjugated with specific drugs have demonstrated an increase in drug delivery perfusion, a reduction of drug dosage, and ultimately a decrease in toxicity including side effects to normal tissues. Among all forms of cancer studied and treated so far, public health data identify lung cancer as the deadliest. While scientists are discovering more promising avenues in the use of nanoparticles to deliver drugs in the treatment of various forms of cancers, lung cancer has become an excellent target for clinical research studies involving drug delivery. Recent discoveries have demonstrated that gold nanoparticles have the ability to effectively penetrate leaky blood vessels and tissue aggregate barriers in order to reach tumor loci. Indeed, gold nanoparticles have distinct physical and chemical properties that make them eligible as nanocarriers. As a distinguished feature, they can be modifiedin a variety of ways to facilitate their binding with specific receptors. Thus, their use has become suitable for anti-carcinogenic drug-delivering in both small cells lung cancer (SCLC) and non-small cell lung cancer (NSCLC). Scientists have recently conjugated doxorubicin (_Dox_) on the surfaces of gold nanoparticles to create a delivery system (Dox@PVP-AuNPs) for efficient treatment of lung cancer.