The relationship of thymulin activity, immune parameters of HIV disease progression and substance use in a cohort of HIV seropositive alcohol and drug users

Zinc is essential for the activity of thymulin, a thymic hormone involved in T-lymphocyte differentiation and activation. Zinc deficiency is widespread in populations with HIV infection, and HIV+ drug users are particularly susceptible to zinc deficiency and immune suppression. This dissertation explored the relationship of zinc-bound active thymulin to plasma zinc, CD4+ and CD8+ cell count, the CD4+/CD8+ ratio, and drug use in HIV-infected drug users. Zinc-bound active thymulin was assessed in plasma of HIV+ drug users who were participating in a 30 month zinc supplementation trial. Plasma from 80 participants at the 12 month visit, and 40 of these same participants, randomly selected, at the baseline visit were assessed for zinc-bound active thymulin levels using a modification of the rosette inhibition assay. Thymulin activity was directly associated with CD4+ cell count (β = 0.127, p = 0.002) and inversely associated with cocaine use (β = −0.908, p = 0.026; R2 = 0.188, p = 0.019) independent of HIV viral load, age, gender and antiretroviral use. An increase in thymulin activity was 1.4 times more likely when CD4+ cell count increased (OR = 1.402, 95%CI: 1.006–1.956), independent of change in viral load, antiretroviral use, and age. Participants who used cocaine consistently, were 7.6 times less likely to have an increase in thymulin activity (OR = 0.133, 95%CI: 0.017–1.061). There was a direct correlation between change in plasma zinc and change in zinc-bound active thymulin (r = 0.243, p = 0.13). Analysis of CD4+ cell count decline in 222 participants in the zinc supplementation trial across the 30 months showed that both crack cocaine use and heavy alcohol use accelerated CD4+ cell count decline. Thymulin activity is directly associated with HIV disease progression, measured by CD4+ cell count, and is depressed with cocaine use independent of antiretroviral use and HIV viral load. Cocaine and heavy alcohol accelerate CD4+ cell count decline. The effect of cocaine on thymic output requires further evaluation as a mechanism for the association of cocaine use with faster HIV disease progression.

Analysis and comparison of network performance with different network parameters

The purpose of this study was to analyze the network performance by observing the effect of varying network size and data link rate on one of the most commonly found network configurations. Computer networks have been growing explosively. Networking is used in every aspect of business, including advertising, production, shipping, planning, billing, and accounting. Communication takes place through networks that form the basis of transfer of information. The number and type of components may vary from network to network depending on several factors such as requirement and actual physical placement of the networks. There is no fixed size of the networks and they can be very small consisting of say five to six nodes or very large consisting of over two thousand nodes. The varying network sizes make it very important to study the network performance so as to be able to predict the functioning and the suitability of the network. The findings demonstrated that the network performance parameters such as global delay, load, router processor utilization, router processor delay, etc. are affected. The findings demonstrated that the network performance parameters such as global delay, load, router processor utilization, router processor delay, etc. are affected significantly due to the increase in the size of the network and that there exists a correlation between the various parameters and the size of the network. These variations are not only dependent on the magnitude of the change in the actual physical area of the network but also on the data link rate used to connect the various components of the network.

A Study on the Correlation of Bivariate And Trivariate Normal Models

Suppose two or more variables are jointly normally distributed. If there is a common relationship between these variables it would be very important to quantify this relationship by a parameter called the correlation coefficient which measures its strength, and the use of it can develop an equation for predicting, and ultimately draw testable conclusion about the parent population. This research focused on the correlation coefficient ρ for the bivariate and trivariate normal distribution when equal variances and equal covariances are considered. Particularly, we derived the maximum Likelihood Estimators (MLE) of the distribution parameters assuming all of them are unknown, and we studied the properties and asymptotic distribution of . Showing this asymptotic normality, we were able to construct confidence intervals of the correlation coefficient ρ and test hypothesis about ρ. With a series of simulations, the performance of our new estimators were studied and were compared with those estimators that already exist in the literature. The results indicated that the MLE has a better or similar performance than the others.