Examination of factors that influence students' average client transactions in a small-animal primary care clinical environment.

The purpose of this study was to describe the average client transaction (ACT) of fourth-year veterinary students in a university community practice setting at the University of Georgia (UGA) and to investigate variables that may affect the students' ACT. The revenue generated by each student was assessed to determine whether gender, ethnicity, academic class rank, area of emphasis, and UGA versus non-UGA student could affect the ACT of the students. Two hundred one students were evaluated over 19 continuous 3-week-long clinical rotations. For all students, the M±SD gross revenue was $2,836±$1,051, the total number of client transactions was 18±6, and the ACT was $154±$35 per student. During the study, hospital fees (price class) increased four times. No student-related factors were significantly associated with the ACT in the univariate analyses. No factors except price class were found to be significant in the two-factor analyses. Generating an ACT equivalent to the national average demonstrates that the typical student at the community practice clinic should provide a level of productivity to the practice owners who hire these students. The factors measured demonstrated little influence on the student's revenue-generating ability at the community practice clinic. Mentorship provided to students for each appointment might have affected the study outcome. Other variables, such as communication style, may affect the ACT more than those investigated in this study and warrant further study.

Evaluation of a primary-care setting at a veterinary teaching hospital by a student business group: implementing business training within the curriculum.

This article provides an introduction to the use of students' business skills in optimizing teaching opportunities, student learning, and client satisfaction in a primary health care setting at a veterinary teaching hospital. Seven veterinary-student members of the local chapter of the Veterinary Business Management Association (VBMA) evaluated the primary-care service at the University of Georgia (UGA) veterinary teaching hospital and assessed six areas of focus: (1) branding and marketing, (2) client experience, (3) staff and staffing, (4) student experience, (5) time management, and (6) standard operating procedures and protocols. For each area of focus, strengths, weaknesses, opportunities, and threats were identified. Of the six areas, two were identified as areas in need of immediate improvement, the first being the updating of standard operating protocols and the second being time management and the flow of appointments. Recommendations made for these two areas were implemented. Overall, the staff and students provided positive feedback on the recommended changes. Through such a student-centered approach to improving the quality of their education, students are empowered and are held accountable for their learning environment. The fact that the VBMA functions without a parent organization and that the primary-care service at UGA functions primarily as a separate entity from the specialty services at the College of Veterinary Medicine allowed students to have a direct impact on their learning environment. We hope that this model for advancing business education will be studied and promoted to benefit both veterinary education and business practice within academia.

A multi PDZ-domain protein Pdzd2 contributes to functional expression of sensory neuron-specific sodium channel Na(V)1.8.

The voltage-gated sodium channel Na(V)1.8 is expressed exclusively in nociceptive sensory neurons and plays an important role in pain pathways. Na(V)1.8 cannot be functionally expressed in non-neuronal cells even in the presence of beta-subunits. We have previously identified Pdzd2, a multi PDZ-domain protein, as a potential interactor for Na(V)1.8. Here we report that Pdzd2 binds directly to the intracellular loops of Na(V)1.8 and Na(V)1.7. The endogenous Na(V)1.8 current in sensory neurons is inhibited by antisense- and siRNA-mediated downregulation of Pdzd2. However, no marked change in pain behaviours is observed in Pdzd2-decificent mice. This may be due to compensatory upregulation of p11, another regulatory factor for Na(V)1.8, in dorsal root ganglia of Pdzd2-deficient mice. These findings reveal that Pdzd2 and p11 play collaborative roles in regulation of Na(V)1.8 expression in sensory neurons.

Identification of binding domains in the sodium channel Na(V)1.8 intracellular N-terminal region and annexin II light chain p11.

The interaction of p11 (annexin II light chain) with the N-terminal domain of Na(V)1.8, a tetrodotoxin-resistant sodium channel, is essential for the functional expression of the channel. Here we show that p11 binds to Na(V)1.8 but not to sodium channel isoforms Na(V)1.2, 1.5, 1.7 or Na(V)1.9. The binding of amino acids 74-103 of Na(V)1.8 to p11 residues 33-78 occurs in a random coiled region flanked by two EF hand motifs whose crystal structure has been established. As Na(V)1.8 channel expression is associated with pain pathways, drugs that disrupt the Na(V)1.8-p11 interaction and down-regulate channel expression may have analgesic activity.

Sensory neuron proteins interact with the intracellular domains of sodium channel NaV1.8.

Voltage-gated sodium channels initiate and propagate action potentials in excitable cells. The tetrodotoxin-resistant Na(+) channel (Na(V)1.8/SNS) is expressed in damage-sensing neurons (nociceptors) and plays an important role in pain pathways. Expression of high levels of functional Na(V)1.8 in heterologous cells has proved problematic, even in the presence of known sodium channel accessory beta-subunits. This suggests that other regulatory proteins are required for normal levels of Na(V)1.8 expression. Here we report the use of a yeast two-hybrid system and a rat dorsal root ganglion cDNA library to identify 28 different clones encoding proteins which interact with intracellular domains of Na(V)1.8. Many clones are expressed at high levels in small diameter DRG neurons as judged by in situ hybridization. Interacting proteins include cytoplasmic elements and linker proteins (e.g. beta-actin and moesin), enzymes (e.g. inositol polyphosphate 5-phosphatase and TAO2 thousand and one protein kinase), channels and membrane-associated proteins (voltage-dependent anion channel VDAC3V and tetraspanin), as well as motor proteins (dynein intermediate and light chain) and transcripts encoding previously undescribed proteins. Immunoprecipitation (pull-down) assays confirm that some of the proteins interact with, and may hence regulate, Na(V)1.8 in vivo.

Annexin II light chain regulates sensory neuron-specific sodium channel expression.

The tetrodotoxin-resistant sodium channel Na(V)1.8/SNS is expressed exclusively in sensory neurons and appears to have an important role in pain pathways. Unlike other sodium channels, Na(V)1.8 is poorly expressed in cell lines even in the presence of accessory beta-subunits. Here we identify annexin II light chain (p11) as a regulatory factor that facilitates the expression of Na(V)1.8. p11 binds directly to the amino terminus of Na(V)1.8 and promotes the translocation of Na(V)1.8 to the plasma membrane, producing functional channels. The endogenous Na(V)1.8 current in sensory neurons is inhibited by antisense downregulation of p11 expression. Because direct association with p11 is required for functional expression of Na(V)1.8, disrupting this interaction may be a useful new approach to downregulating Na(V)1.8 and effecting analgesia.

Conditional local influence in case-weights linear regression.

The local influence approach proposed by Cook (1986) makes use of the normal curvature and the direction achieving the maximum curvature to assess the local influence of minor perturbation of statistical models. When the approach is applied to the linear regression model, the result provides information concerning the data structure different from that contributed by Cook's distance. One of the main advantages of the local influence approach is its ability to handle the simultaneous effect of several cases, namely, the ability to address the problem of 'masking'. However, Lawrance (1995) points out that there are two notions of 'masking' effects, the joint influence and the conditional influence, which are distinct in nature. The normal curvature and the direction of maximum curvature are capable of addressing effects under the category of joint influences but not conditional influences. We construct a new measure to define and detect conditional local influences and use the linear regression model for illustration. Several reported data sets are used to demonstrate that new information can be revealed by this proposed measure.

A local influence approach to identifying multiple multivariate outliers.

We make use of Cook's local influence approach and its recent modification by Poon and Poon to develop measures for detecting multivariate outliers. The motivation and the foundation of the theory are geometrical and are different from classical approaches; however, whilst the proposed measure exhibits a form similar to those in the literature, it still has a considerable advantage in having transformed the classical measures to the unit interval. The new approach unifies outlier identification measures using geometrical concepts. It involves no distributional assumption or large-sample properties, and allows the flexibility of identifying outliers with respect to different metrics. The approach therefore provides a valid reason for using the various measures in complicated situations, such as in non-normal cases and in small-sample problems.

Bayesian analysis of square ordinal-ordinal tables.

We analyse square contingency tables with ordered categories. Assuming that the observed ordinal categorical variables are manifestations of underlying continuous variables, we formulate a model which allows the comparisons of locations and dispersions between variables. We identify the model by imposing stochastic constraints on the thresholds that define the relationship between the observed and the underlying variables. As a result, the underlying continuous variables' location and dispersion parameters which were not estimable before can be estimated by the Bayesian approach. Illustrative examples are given based on several reported data sets.

A two-stage estimation of structural equation models with continuous and polytomous variables.

This paper develops a computationally efficient procedure for analysis of structural equation models with continuous and polytomous variables. A partition maximum likelihood approach is used to obtain the first stage estimates of the thresholds and the polyserial and polychoric correlations in the underlying correlation matrix. Then, based on the joint asymptotic distribution of the first stage estimator and an appropriate weight matrix, a generalized least squares approach is employed to estimate the structural parameters in the correlation structure. Asymptotic properties of the estimators are derived. Some simulation studies are conducted to study the empirical behaviours and robustness of the procedure, and compare it with some existing methods.

Two-level analysis of covariance structures for unbalanced designs with small level-one samples.

The main purpose of this paper is to develop basic statistical theory for two-level analysis of covariance structures. Major asymptotic results for statistical inference, such as the asymptotic distributions of the estimator and the goodness-of-fit test statistic are derived, based on an unbalanced design with only small numbers of level-one units. Computationally, it is shown that the solution can be obtained via standard programs, such as LISREL, EQS and COSAN. The behaviour of the estimates is illustrated by an artificial example and a real-life example. Some possibilities of extending the results to a more general two-level model, and to situations with arbitrary distributions and elliptical distributions are also investigated.

A two-phase linear regression model for biologic half-life data.

In estimating the biologic half-life of an infused drug or biologic agent, one very frequently used model is the biexponential, which reflects a two-compartment physiologic model. The difficulty in using this model is that it is nonlinear in the parameters and requires relatively sophisticated analysis. Furthermore, an interval estimate is not usually reported. We propose the use of a two-phase linear regression approach, which is tantamount to breaking down the model into two straight lines based on the selection of the logarithm of concentration as the ordinate and time as the abscissa. We show how to determine the joint or changeover point for the two lines using a simple iterative procedure, how to select between a one-phase and a two-phase model, and how to provide a simple confidence interval estimate for half-life when it exists. An example using data from a study of Factor VIII pharmacokinetics is given.

In vivo changes in the composition of cattle skin surface lipid with time.

The skin surface lipid composition was examined in cattle over a period of 13 days after cleaning the skin at 15 degrees C and 35 degrees C. Little change was observed in the concentrations and fatty acid compositions of all the major sebum fractions other than the unesterified fatty acids. Changes which occurred in the proportions of fatty acids present in the unesterified fatty acid fraction were similar at both environments. At 15 degrees C the total amounts per unit area of unesterified fatty acids on the skin did not alter significantly with time. However, at 35 degrees C there was a significant increase in the total amounts of unesterified fatty acids per unit area and concomitant increases in the amounts of individual fatty acids, in particular linoleic acid.