1,199 research outputs found

    Fitting the Means to the Ends: One School’s Experience with Quantitative and Qualitative Methods in Curriculum Evaluation During Curriculum Change

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    Curriculum evaluation plays an important role in substantive curriculum change. The experience of the University of Texas Medical Branch (UTMB) with evaluation processes developed for the new Integrated Medical Curriculum (IMC) illustrates how evaluation methods may be chosen to match the goals of the curriculum evaluation process. Quantitative data such as ratings of courses or scores on external exams are useful for comparing courses or assessing whether standards have been met. Qualitative data such as students’ comments about aspects of courses are useful for eliciting explanations of observed phenomena and describing relationships between curriculum features and outcomes. The curriculum evaluation process designed for the IMC used both types of evaluation methods in a complementary fashion. Quantitative and qualitative methods have been used for formative evaluation of the new IMC courses. They are now being incorporated into processes to judge the IMC against its goals and objectives

    Testosterone Influence on Gene Expression in Lacrimal Glands of Mouse Models of Sjögren Syndrome

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    Purpose: Sjögren syndrome is an autoimmune disorder that occurs almost exclusively in women and is associated with extensive inflammation in lacrimal tissue, an immune-mediated destruction and/or dysfunction of glandular epithelial cells, and a significant decrease in aqueous tear secretion. We discovered that androgens suppress the inflammation in, and enhance the function of, lacrimal glands in female mouse models (e.g., MRL/MpJ-Tnfrsf6lpr [MRL/lpr]) of Sjögren syndrome. In contrast, others have reported that androgens induce an anomalous immunopathology in lacrimal glands of nonobese diabetic/LtJ (NOD) mice. We tested our hypothesis that these hormone actions reflect unique, strain- and tissue-specific effects, which involve significant changes in the expression of immune-related glandular genes. Methods: Lacrimal glands were obtained from age-matched, adult, female MRL/lpr and NOD mice after treatment with vehicle or testosterone for up to 3 weeks. Tissues were processed for analysis of differentially expressed mRNAs using CodeLink Bioarrays and Affymetrix GeneChips. Data were analyzed with bioinformatics and statistical software. Results: Testosterone significantly influenced the expression of numerous immune-related genes, ontologies, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in lacrimal glands of MRL/lpr and NOD mice. The nature of this hormone-induced immune response was dependent upon the autoimmune strain, and was not duplicated within lacrimal tissues of nonautoimmune BALB/c mice. The majority of immune-response genes regulated by testosterone were of the inflammatory type. Conclusions: Our findings support our hypothesis and indicate a major role for the lacrimal gland microenvironment in mediating androgen effects on immune gene expression

    Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

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    A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data

    A planar calculus for infinite index subfactors

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    We develop an analog of Jones' planar calculus for II_1-factor bimodules with arbitrary left and right von Neumann dimension. We generalize to bimodules Burns' results on rotations and extremality for infinite index subfactors. These results are obtained without Jones' basic construction and the resulting Jones projections.Comment: 56 pages, many figure

    Determinants of polyp Size in patients undergoing screening colonoscopy

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    <p>Abstract</p> <p>Background</p> <p>Pre-existing polyps, especially large polyps, are known to be the major source for colorectal cancer, but there is limited available information about factors that are associated with polyp size and polyp growth. We aim to determine factors associated with polyp size in different age groups.</p> <p>Methods</p> <p>Colonoscopy data were prospectively collected from 67 adult gastrointestinal practice sites in the United States between 2002 and 2007 using a computer-generated endoscopic report form. Data were transmitted to and stored in a central data repository, where all asymptomatic white (n = 78352) and black (n = 4289) patients who had a polyp finding on screening colonoscopy were identified. Univariate and multivariate analysis of age, gender, performance site, race, polyp location, number of polyps, and family history as risk factors associated with the size of the largest polyp detected at colonoscopy.</p> <p>Results</p> <p>In both genders, size of the largest polyp increased progressively with age in all age groups (<it>P </it>< .0001). In subjects ≥ 80 years the relative risk was 1.55 (95% CI, 1.35-1.79) compared to subjects in the youngest age group. With the exception of family history, all study variables were significantly associated with polyp size (<it>P </it>< .0001), with multiple polyps (≥ 2 versus 1) having the strongest risk: 3.41 (95% CI, 3.29-3.54).</p> <p>Conclusions</p> <p>In both genders there is a significant increase in polyp size detected during screening colonoscopy with increasing age. Important additional risk factors associated with increasing polyp size are gender, race, polyp location, and number of polyps, with polyp multiplicity being the strongest risk factor. Previous family history of bowel cancer was not a risk factor.</p
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