TIME DEPENDENT ALTERATIONS IN CENTRAL NERVOUS SYSTEM DOPAMINE RECEPTORS INDUCED BY DOPAMINE AGONIST TREATMENT

Levodopa, the precursor of dopamine, is currently the drug of choice in the treatment of Parkinson's disease. Recently, two direct dopamine agonists, bromocriptine and pergolide, have been tested for the treatment of Parkinson's disease because of reduced side effects compared to levodopa. Few studies have evaluated the effects of long-term treatment of dopamine agonists on dopamine receptor regulation in the central nervous system. Thus, the purpose of this study was to determine whether chronic dopamine agonist treatment produces a down-regulation of striatal dopamine receptor function and to compare the results of the two classes of dopaminergic drugs.^ Levodopa with carbidopa, a peripheral decarboxylase inhibitor, was administered orally to rats whereas bromocriptine and pergolide were injected intraperitoneally once daily. Several neurochemical parameters were examined from 1 to 28 days.^ Levodopa minimally decreased striatal D-1 receptor activity but increased the number of striatal D-2 binding sites. Levodopa increased the V(,max) of tyrosine hydroxylase (TH) in all brain regions tested. Protein blot analysis of striatal TH indicated a significant increase in the amount of TH present. Dopamine-beta-hydroxylase (DBH) activity was markedly decreased in all brain regions studied and mixing experiments of control and drug-treated cortices did not show the presence of an increased level of endogenous inhibitors.^ Bromocriptine treatment decreased the number of D-2 binding sites. Striatal TH activity was decreased and protein blot analysis indicated no change in TH quantity. The specificity of bromocriptine for striatal TH suggested that bromocriptine preferentially interacts with dopamine autoreceptors.^ Combination levodopa-bromocriptine was administered for 12 days. There was a decrease in both D-1 receptor activity and D-2 binding sites, and a decrease in brain HVA levels suggesting a postsynaptic receptor action. Pergolide produced identical results to the combination levodopa-bromocriptine studies.^ In conclusion, combination levodopa-bromocriptine and pergolide treatments exhibited the expected down-regulation of dopamine receptor activity. In contrast, levodopa appeared to up-regulate dopamine receptor activity. Thus, these data may help to explain, on a biochemical basis, the decrease in the levodopa-induced side effects noted with combination levodopa-bromocriptine or pergolide therapies in the treatment of Parkinson's disease. ^

The impact of a child's unexpected hospitalization upon Taiwanese parents' situational anxiety

Background. The parents of a sick child likely experience situational anxiety due to their young child being unexpectedly hospitalized. The emotional upheaval may be great enough that their anxiety inhibits them in providing positive support to their hospitalized child. Because anxiety affects psychological distress as well as behavioral distress, identifying parental distress helps parents improving their coping mechanisms. ^ Purpose. The study compared situational anxiety levels between Taiwanese fathers and mothers and focused on differences between parental anxiety levels at the beginning of the child's unplanned hospitalization and at time of discharge. The study also identified factors related to the parents' distress and use of coping mechanisms. ^ Methods. A descriptive, comparative research design was used to determine the difference between the anxiety levels of 62 Taiwanese father-mother dyads during the situational crisis of their child's unexpected hospitalization. The Mandarin version (M) of Visual Analog Scale (VAS-M), State-Trait Anxiety Inventory (STAI-M), and the Index of Parent Participation/Hospitalized Child (IPP/HC-M) were used to differentiate maternal and paternal anxiety levels and identify factors related to the parents' distress. Questionnaires were completed by parents within 24-36 hours of the child's hospital admission and within 24 hours prior to discharge. A paired t-test, two sample t-test, and linear mixed regression model were used to test and support the study hypothesis. ^ Results. The findings reveal that the mothers' anxiety levels did not significantly differ from the fathers' anxiety level when their child had a sudden admission to the hospital. In particular, parental state anxiety levels did not decrease during the child's hospital stay and subsequent discharge. Moreover, anxiety levels did not differ between parents regardless of whether the child's disease was acute or chronic. The most effective factor related to parental situational anxiety was parental perception of the severity of the child's illness. ^ Conclusions. Parental anxiety was found to be significantly related to changes in their perception of the severity of their child's illness. However, the study was not able to illustrate how parental involvement in the child's hospital care was related to parental perception of the severity of their child's illness. Future studies, using a qualitative approach to gamer more information as to what variables influence parental anxiety during a situational crisis, may provide a richer database from which to modify key variables as well as the instruments used to improve the quality of the data obtained. ^

Characterization of the East Houston particulate matter <2.5 atmosphere; a novel method for desorbing organic constituents from airborne particulates

I have developed a novel approach to test for toxic organic substances adsorbed onto ultra fine particulate particles present in the ambient air in Northeast Houston, Texas. These particles are predominantly carbon soot with an aerodynamic diameter (AD) of <2.5 μm. If present in the ambient air, many of the organic substances will be absorbed to the surface of the particles (which act just like a charcoal air filter), and may be adducted into the respiratory system. Once imbedded into the lungs these particles may release the adsorbed toxic organic substances with serious health consequences. I used a Airmetrics portable Minivol air sampler time drawing the ambient air through collection filters samples from 6 separate sites in Northeast Houston, an area known for high ambient PM 2.5 released from chemical plants and other sources (e.g. vehicle emissions).(1) In practice, the mass of the collected particles were much less than the mass of the filters. My technique was designed to release the adsorbed organic substances on the fine carbon particles by heating the filter samples that included the PM 2.5 particles prior to identification by gas chromatography/mass spectrometry (GCMS). The results showed negligible amounts of target chemicals from the collection filters. However, the filters alone released organic substances and GCMS could not distinguish between the organic substances released from the soot particles from those released from the heated filter fabric. However, an efficacy tests of my method using two wax burning candles that released soot revealed high levels of benzene. This suggests that my method has the potential to reveal the organic substances adsorbed onto the PM 2.5 for analysis. In order to achieve this goal, I must refine the particle collection process which would be independent of the filters; the filters upon heating also release organic substances obscuring the contribution from the soot particles. To obtain pure soot particles I will have to filter more air so that the soot particles can be shaken off the filters and then analyzed by my new technique. ^

Expanding Learning Time in Schools: Considering the Challenges of Implementation and the Potential Impact

Considering the broader context of school reform that is seeking education strategies that might deliver substantial impact, this article examines four questions related to the policy and practice of expanding learning time: (a) why do educators find the standard American school calendar insufficient to meet students’ educational needs, especially those of disadvantaged students? (b) how do educators implement a longer day and/or year, addressing concerns about both educational quality and costs? (c) what does research report about outcomes of expanding time in schools? and (d) what are the future prospects for increasing the number of expanded-time schools? The paper examines these questions by considering research, policy, and practice at the national level and, throughout, by drawing upon additional evidence from Massachusetts, one of the leading states in the expanded-time movement. In considering the latter two questions, the article explores the knowns and unknowns related to expanded learning time and offers suggestions for further research.

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.