Is sensitization to stimulants time dependent and mediated via NMDA receptors?

Chronic administration of psychomotor stimulants has been reported to produce behavioral sensitization to its effects on motor activity. This adaptation may be related to the pathophysiology of recurrent psychiatric disorders. Since disturbances in circadian rhythms are also found in many of these disorders, the relationship between sensitization and chronobiological factors became of interest. Therefore, a computerized monitoring system investigated the following: whether repeated exposure to methylphenidate (MPD) and amphetamine (AMP) could produce sensitization to its locomotor effects in the rat; whether sensitization to MPD and AMP was dependent on the circadian time of drug administration; whether the baseline levels of locomotor activity would be effected by repeated exposure to MPD and AMP; whether the expression of a sensitized response could be affected by the photoperiod; and whether MK-801, a non-competitive NMDA antagonist, could disrupt the development of sensitization to MPD. Dawley rats were housed in test cages and motor activity was recorded continuously for 16 days. The first 2 days served as baseline for each rat, and on day 3 each rat received a saline injection. The locomotor response to 0.6, 2.5, or 10 mg/kg of MPD was tested on day 4, followed by five days of single injections of 2.5 mg/kg MPD (days 5–9). After five days without injection (days 10–14) rats were re-challenged (day 15) with the same doses they received on day 4. There were three separate dose groups ran at four different times of administration, 08:00, 14:00, 20:00, or 02:00 (i.e. 12 groups). The same protocol was conducted with AMP with the doses of 0.3, 0.6, and 1.2 mg/kg given on day 4 and 15, and 0.6 mg/kg AMP as the repeated dose on days 5 to 9. In the second set of experiments only sensitization to MPD was investigated. The expression of the sensitized response was dose-dependent and mainly observed with challenge of the lower dose groups. The development of sensitization to MPD and ANT was differentially time-dependent. For MPD, the most robust sensitization occurred during the light phase, with no sensitization during the middle of the dark phase. (Abstract shortened by UMI.) ^

A reduced model clarifies the role of feedback loops and time delays in the Drosophila circadian oscillator.

Although several detailed models of molecular processes essential for circadian oscillations have been developed, their complexity makes intuitive understanding of the oscillation mechanism difficult. The goal of the present study was to reduce a previously developed, detailed model to a minimal representation of the transcriptional regulation essential for circadian rhythmicity in Drosophila. The reduced model contains only two differential equations, each with time delays. A negative feedback loop is included, in which PER protein represses per transcription by binding the dCLOCK transcription factor. A positive feedback loop is also included, in which dCLOCK indirectly enhances its own formation. The model simulated circadian oscillations, light entrainment, and a phase-response curve with qualitative similarities to experiment. Time delays were found to be essential for simulation of circadian oscillations with this model. To examine the robustness of the simplified model to fluctuations in molecule numbers, a stochastic variant was constructed. Robust circadian oscillations and entrainment to light pulses were simulated with fewer than 80 molecules of each gene product present on average. Circadian oscillations persisted when the positive feedback loop was removed. Moreover, elimination of positive feedback did not decrease the robustness of oscillations to stochastic fluctuations or to variations in parameter values. Such reduced models can aid understanding of the oscillation mechanisms in Drosophila and in other organisms in which feedback regulation of transcription may play an important role.

Immunology and HIV expression in skin, in vitro, and in response to ultraviolet light

HIV can enter the body through Langerhans cells, dendritic cells, and macrophages in skin mucosa, and spreads by lysis or by syncytia. Since UVL induces of HIV-LTR in transgenic mice mid in cell lines in vitro, we hypothesized that UVB may affect HIV in people and may affect HIV in T cells in relation to dose, apoptosis, and cytokine expression. To determine whether HIV is induced by UVL in humans, a clinical study of HIV+ patients with psoriasis or pruritus was conducted during six weeks of UVB phototherapy, Controls were HIV-psoriasis patients receiving UVB and HIV+ KS subjects without UVB.Blood and skin biopsy specimens were collected at baseline, weeks 2 and 6, and 4 weeks after UVL. AIDS-related skin diseases showed unique cytokine profiles in skin and serum at baseline. In patients and controls on phototherapy, we observed the following: (1) CD4+ and CD8+ T cell numbers are not significantly altered during phototherapy, (2) p24 antigen levels, and also HIV plasma levels increase in patients not on antiviral therapy, (3) HIV-RNA levels in serum or plasma. (viral load) can either increase or decrease depending on the patient's initial viral load, presence of antivirals, and skin type, (4) HIV-RNA levels in the periphery are inversely correlated to serum IL-10 and (5) HIV+ cell in skin increase after UVL at 2 weeks by RT-PCR in situ hybridization mid we negatively correlated with peripheral load. To understand the mechanisms of UVB mediated HIV transcription, we treated Jurkat T cell lines stably transfected with an HIV-LTR-luciferase plasmid only or additionally with tat-SV-40 early promoter with UVB (2 J/m2 to 200 J/m2), 50 to 200 ng/ml rhIL-10, and 10 μg/ml PHA as control. HIV promoter activity was measured by luciferase normalized to protein. Time points up to 72 hours were analyzed for HIV-LTR activation. HIV-LTR activation had the following properties: (1) requires the presence of Tat, (2) occurs at 24 hours, and (3) is UVB dose dependent. Changes in viability by MTS (3-(4,5-dimethyhhiazol-2-y1)-5-(3-carboxymethoxyphonyl)-2-(4-sulfophenyl)-2H-tetrazolium) mixed with PMS (phenazine methosulfate) solution and apoptosis by propidium iodide and annexin V using flow cytometry (FC) were seen in irradiated Jurkat cells. We determined that (1) rhIL-10 moderately decreased HIV-LTR activation if given before radiation and greatly decreases it when given after UVB, (2) HIV-LTR activation was low at doses of greater than 70 J/m2, compared to activation at 50 J/m2. (Abstract shortened by UMI.)^

The role of drug use in welfare to work: Evidence from Houston, Texas

The purpose of this study is to examine the prevalence of drug abuse among welfare recipients in Houston, TX and compare the work activities and employment barriers of drug abusers in order to better understand the potential effects of welfare reform for this population. Four hypotheses were tested comparing the work activities and employment barriers of drug abusers to others on welfare and the relative importance of drug abuse and employment barriers in predicting work activity. ^ This cross-sectional study examined the characteristics and work activities of 447 welfare recipients (81 drug abusers and 366 non-abusers) who were surveyed between October 1998 and April 1999 in Houston, TX. Subjects were introduced and recruited to participate in the study through a flyer, door to door visits, and peer driven recruitment/referral. ^ About 18% were found to be drug abusers, which is consistent with the national average (10–33%) among welfare recipients. Compared to others on welfare, drug abusers were less involved in work activities, and had more employment barriers. Employment barriers were found to be more predictive of welfare to work activities than drug abuse. The results suggest that alleviating employment barriers should be stressed in programs aimed at welfare recipients with drug abuse problems. ^

Implementing the Global Plan of Action on workers' health: Components to protect health care workers

Health care workers are at risk for percutaneous injuries and infection with blood born pathogens due to needle stick injuries with contaminated needles. The most common pathogens transmitted are hepatitis B, and C and HIV/AIDS. According to the WHO Global Plan of Action (GPA) a large gap exist between and within countries with regards to the health status of workers and their exposure to occupational risk. Less than 15% of the world's work forces have access to occupational health services despite the availability of effective interventions that can prevent occupational hazards, or protect and promote health in the workplace. The 2006 World Health Report declared that there is a global crisis in the health care work force. 1 in 400 of the world's health care workers work in Sub-Saharan Africa. 1 in 3 work in the U.S or Canada. The shortage of health care workers is worst in Southeast Asia and Sub-Saharan Africa. These countries have the highest burden of exposure to contaminated sharps. They rarely, if ever monitor the exposure or health impact of occupational ailments and injuries on workers. Many injuries are unreported. Occupational health services in the developing world are virtually non existent. Many health care workers leave their home countries and go to work in other countries where the working conditions, occupational services included, are better. The inability of countries to provide the necessary numbers of health care workers to provide a high level of health coverage is a threat to national and international public health security. Immunizing health care workers against hepatitis B and providing them PEP, PPE, education and safety training is an essential part of increasing and maintaining the numbers of health care workers in the critical shortage areas. ^

Regression with autocorrelated data: A study of time trend of global infant mortality rate

The infant mortality rate (IMR) is considered to be one of the most important indices of a country's well-being. Countries around the world and other health organizations like the World Health Organization are dedicating their resources, knowledge and energy to reduce the infant mortality rates. The well-known Millennium Development Goal 4 (MDG 4), whose aim is to archive a two thirds reduction of the under-five mortality rate between 1990 and 2015, is an example of the commitment. ^ In this study our goal is to model the trends of IMR between the 1950s to 2010s for selected countries. We would like to know how the IMR is changing overtime and how it differs across countries. ^ IMR data collected over time forms a time series. The repeated observations of IMR time series are not statistically independent. So in modeling the trend of IMR, it is necessary to account for these correlations. We proposed to use the generalized least squares method in general linear models setting to deal with the variance-covariance structure in our model. In order to estimate the variance-covariance matrix, we referred to the time-series models, especially the autoregressive and moving average models. Furthermore, we will compared results from general linear model with correlation structure to that from ordinary least squares method without taking into account the correlation structure to check how significantly the estimates change.^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.