Runaway and Throwaway Youth: Time for Policy Changes and Public Responsibility

The traditional American dream of owning a home, obtaining a college education, and working at a good, paying job is only that, a dream, for scores of homeless youth in America today. There is a growing street population of young people who have been thrown out of their homes by their caretakers or their families, and who face life-threatening situations each day. For these youth, the furthest thing in their lives is reaching the so-called “American Dream;” and their most immediate need is survival, simply living out the day in front of them. They have few options that lead to a decent and safe living environment. Their age, lack of work experience, and absence of a high school diploma make it most difficult to find a job. As a result, they turn to other means for survival; runaways and throwaways are most vulnerable to falling prey to the sex trade, selling drugs, or being lured into human trafficking, and some steal or panhandle. Street youth end up spending their nights in bus stations or finding a room in an abandoned building or an empty stairwell to sleep. Attempting to identify a specific number of homeless youth is difficult at best, but what is even more perplexing is our continued inability to effectively protect our children. We are left with a basic question framed by the fundamental tenets of justice: what is a community’s responsibility to its youth who, for whatever reason, end up living on the streets or in unsafe, abusive environments? The purpose of this paper is to briefly outline the characteristics of homeless youth, in particular differentiating between throwaways and runaways; explore the current federal response to homeless youth; and finally, address the nagging question that swirls around all children: can we aggressively aspire to be a community where every child is healthy and safe, and able to realize his or her fullest potential?

Factors influencing time to HIV disease progression in Romanian children

The purpose of this research was two-fold; to investigate the effect of institutionalization on death and CD4 decline in a cohort of 325 HIV-infected Romanian children, and to investigate the effect of disclosure of the child's own HIV status in this cohort. All children were treated with Kaletra-based highly active antiretroviral therapy, and were followed from November, 2001 through October, 2004. The mean age of the children included in the cohort is 13. The study found that children in biological families were more likely to experience disease progression through either death or CD4 decline than children in institutions (p=0.04). The family home-style institution may prove to be a replicable model for the safe and appropriate care of HIV-infected orphaned and abandoned children and teens. The study also found that children who do not know their own HIV infection status were more likely to experience disease progression through either death or CD4 decline than children who know their HIV diagnosis (p=0.03). This evidence suggests that, in the context of highly active anti retroviral therapy, knowledge of one's own HIV infection status is associated with delayed HIV disease progression. ^

New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis.^