Computational selection of inhibitors of Abeta aggregation and neuronal toxicity.

Alzheimer's disease (AD) is characterized by the cerebral accumulation of misfolded and aggregated amyloid-beta protein (Abeta). Disease symptoms can be alleviated, in vitro and in vivo, by 'beta-sheet breaker' pentapeptides that reduce plaque load. However the peptide nature of these compounds, made them biologically unstable and unable to penetrate membranes with high efficiency. The main goal of this study was to use computational methods to identify small molecule mimetics with better drug-like properties. For this purpose, the docked conformations of the active peptides were used to identify compounds with similar activities. A series of related beta-sheet breaker peptides were docked to solid state NMR structures of a fibrillar form of Abeta. The lowest energy conformations of the active peptides were used to design three dimensional (3D)-pharmacophores, suitable for screening the NCI database with Unity. Small molecular weight compounds with physicochemical features and a conformation similar to the active peptides were selected, ranked by docking and biochemical parameters. Of 16 diverse compounds selected for experimental screening, 2 prevented and reversed Abeta aggregation at 2-3microM concentration, as measured by Thioflavin T (ThT) fluorescence and ELISA assays. They also prevented the toxic effects of aggregated Abeta on neuroblastoma cells. Their low molecular weight and aqueous solubility makes them promising lead compounds for treating AD.

A qualitative analysis of leadership styles and the selection of quality improvement in primary care practice

In the Practice Change Model, physicians act as key stakeholders, people who have both an investment in the practice and the capacity to influence how the practice performs. This leadership role is critical to the development and change of the practice. Leadership roles and effectiveness are an important factor in quality improvement in primary care practices.^ The study conducted involved a comparative case study analysis to identify leadership roles and the relationship between leadership roles and the number and type of quality improvement strategies adopted during a Practice Change Model-based intervention study. The research utilized secondary data from four primary care practices with various leadership styles. The practices are located in the San Antonio region and serve a large Hispanic population. The data was collected by two ABC Project Facilitators from each practice during a 12-month period including Key Informant Interviews (all staff members), MAP (Multi-method Assessment Process), and Practice Facilitation field notes. This data was used to evaluate leadership styles, management within the practice, and intervention tools that were implemented. The chief steps will be (1) to analyze if the leader-member relations contribute to the type of quality improvement strategy or strategies selected (2) to investigate if leader-position power contributes to the number of strategies selected and the type of strategy selected (3) and to explore whether the task structure varies across the four primary care practices.^ The research found that involving more members of the clinic staff in decision-making, building bridges between organizational staff and clinical staff, and task structure are all associated with the direct influence on the number and type of quality improvement strategies implemented in primary care practice.^ Although this research only investigated leadership styles of four different practices, it will offer future guidance on how to establish the priorities and implementation of quality improvement strategies that will have the greatest impact on patient care improvement. ^

Selection of body fat distribution indices in adults and associations with plasma lipid metabolism variables

Body fat distribution is a cardiovascular health risk factor in adults. Body fat distribution can be measured through various methods including anthropometry. It is not clear which anthropometric index is suitable for epidemiologic studies of fat distribution and cardiovascular disease. The purpose of the present study was to select a measure of body fat distribution from among a series of indices (those traditionally used in the literature and others constructed from the analysis) that is most highly correlated with lipid-related variables and is independent of overall fatness. Subjects were Mexican-American men and women (N = 1004) from a study of gallbladder disease in Starr County, Texas. Multivariate associations were sought between lipid profile measures (lipids, lipoproteins, and apolipoproteins) and two sets of anthropometric variables (4 circumferences and 6 skinfolds). This was done to assess the association between lipid-related measures and the two sets of anthropometric variables and guide the construction of indices.^ Two indices emerged from the analysis that seemed to be highly correlated with lipid profile measures independent of obesity. These indices are: 2*arm circumference-thigh skinfold in pre- and post-menopausal women and arm/thigh circumference ratio in men. Next, using the sum of all skinfolds to represent obesity and the selected body fat distribution indices, the following hypotheses were tested: (1) state of obesity and centrally/upper distributed body fat are equally predictive of lipids, lipoproteins and apolipoproteins, and (2) the correlation among the lipid-related measures is not altered by obesity and body fat distribution.^ With respect to the first hypothesis, the present study found that most lipids, lipoproteins and apolipoproteins were significantly associated with both overall fatness and anatomical location of body fat in both sex and menopausal groups. However, within men and post-menopausal women, certain lipid profile measures (triglyceride and HDLT among post-menopausal women and apos C-II, CIII, and E among men) had substantially higher correlation with body fat distribution as compared with overall fatness.^ With respect to the second hypothesis, both obesity and body fat distribution were found to alter the association among plasma lipid variables in men and women. There was a suggestion from the data that the pattern of correlations among men and post-menopausal women are more comparable. Among men correlations involving apo A-I, HDLT, and HDL$\sb2$ seemed greatly influenced by obesity, and A-II by fat distribution; among post-menopausal women correlations involving apos A-I and A-II were highly affected by the location of body fat.^ Thus, these data point out that not only can obesity and fat distribution affect levels of single measures, they also can markedly influence the pattern of relationship among measures. The fact that such changes are seen for both obesity and fat distribution is significant, since the indices employed were chosen because they were independent of one another. ^

The utility of rapid participatory appraisal for community assessment, diagnosis, and policy development

The selection of a model to guide the understanding and resolution of community problems is an important issue relating to the foundation of public health practice: assessment, policy development, and assurance. Many assessment models produce a diagnosis of community weaknesses, but fail to promote planning and interventions. Rapid Participatory Appraisal (RPA) is a participatory action research model which regards assessment as the first step in the problem solving process, and claims to achieve assessment and policy development within limited resources of time and money. Literature documenting the fulfillment of these claims, and thereby supporting the utility of the model, is relatively sparse and difficult to obtain. Very few articles discuss the changes resulting from RPA assessments in urban areas, and those that do describe studies conducted outside the U.S.A. ^ This study examines the utility of the RPA model and its underlying theories: systems theory, grounded theory, and principles of participatory change, as illustrated by the case study of a community assessment conducted for the Texas Diabetes Institute (TDI), San Antonio, Texas, and subsequent outcomes. Diabetes has a high prevalence and is a major issue in San Antonio. Faculty and students conducted the assessment by informal collaboration between two nursing and public health assessment courses, providing practical student experiences. The study area was large, and the flexibility of the model tested by its use in contiguous sub-regions, reanalyzing aggregated results for the study area. Official TDI reports, and a mail survey of agency employees, described policy development resulting from community diagnoses revealed by the assessment. ^ The RPA model met the criteria for utility from the perspectives of merit, worth, efficiency, and effectiveness. The RPA model best met the agencies' criteria (merit), met the data needs of TDI in this particular situation (worth), provided valid results within budget, time, and personnel constraints (efficiency), and stimulated policy development by TDI (effectiveness). ^ The RPA model appears to have utility for community assessment, diagnosis, and policy development in circumstances similar to the TDI diabetes study. ^

Selection bias and the cross-validation of regression models for prediction

Strategies are compared for the development of a linear regression model with stochastic (multivariate normal) regressor variables and the subsequent assessment of its predictive ability. Bias and mean squared error of four estimators of predictive performance are evaluated in simulated samples of 32 population correlation matrices. Models including all of the available predictors are compared with those obtained using selected subsets. The subset selection procedures investigated include two stopping rules, C$\sb{\rm p}$ and S$\sb{\rm p}$, each combined with an 'all possible subsets' or 'forward selection' of variables. The estimators of performance utilized include parametric (MSEP$\sb{\rm m}$) and non-parametric (PRESS) assessments in the entire sample, and two data splitting estimates restricted to a random or balanced (Snee's DUPLEX) 'validation' half sample. The simulations were performed as a designed experiment, with population correlation matrices representing a broad range of data structures.^ The techniques examined for subset selection do not generally result in improved predictions relative to the full model. Approaches using 'forward selection' result in slightly smaller prediction errors and less biased estimators of predictive accuracy than 'all possible subsets' approaches but no differences are detected between the performances of C$\sb{\rm p}$ and S$\sb{\rm p}$. In every case, prediction errors of models obtained by subset selection in either of the half splits exceed those obtained using all predictors and the entire sample.^ Only the random split estimator is conditionally (on $\\beta$) unbiased, however MSEP$\sb{\rm m}$ is unbiased on average and PRESS is nearly so in unselected (fixed form) models. When subset selection techniques are used, MSEP$\sb{\rm m}$ and PRESS always underestimate prediction errors, by as much as 27 percent (on average) in small samples. Despite their bias, the mean squared errors (MSE) of these estimators are at least 30 percent less than that of the unbiased random split estimator. The DUPLEX split estimator suffers from large MSE as well as bias, and seems of little value within the context of stochastic regressor variables.^ To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample be used for model development, and a leave-one-out statistic (e.g. PRESS) be used for assessment. ^

A review of the Malaria Chemoprophylaxis Compliance Program at ExxonMobil Corporation

ExxonMobil, a Fortune 500 oil and gas corporation, has a global workforce with employees assigned to projects in areas at risk for infectious diseases, particularly malaria. As such, the corporation has put in place a program to protect the health of workers and ensure their safety in malaria endemic zones. This program is called the Malaria Control Program (MCP). One component of this program is the more specific Malaria Chemoprophylaxis Compliance Program (MCCP), in which employees enroll following consent to random drug testing for compliance with the company's chemoprophylaxis requirements. Each year, data is gathered on the number of employees working in these locations and are selected randomly and tested for chemoprophylaxis compliance. The selection strives to test each eligible worker once per year. Test results that come back positive for the chemoprophylaxis drug are considered "detects" and tests that are negative for the drug and therefore show the worker is non-compliant at risk for severe malaria infection are considered "non-detect". ^ The current practice report used aggregate data to calculate statistics on test results to reflect compliance among both employees and contractors in various malaria-endemic areas. This aggregate, non-individualized data has been compiled and reflects the effectiveness and reach of ExxonMobil's Malaria Chemoprophylaxis Compliance Program. In order to assess compliance, information on the number of non-detect test results was compared to the number of tests completed per year. The data shows that over time, non-detect results have declined in both employee and contractor populations, and vary somewhat by location due to size and scope of the MCCP implemented in-country. Although the data indicate a positive trend for the corporation, some recommendations have been made for future implementation of the program.^