Análise multivariada de características reprodutivas e produtivas de vacas da raça Holandesa

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise multivariada de características reprodutivas e produtivas de vacas da raça Holandesa

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Oral health profile in patients infected with HTLV-1: Clinical findings, proviral load, and molecular analysis from HTLV-1 in saliva

Human T-lymphotropic virus type 1 (HTLV-1) is associated with adult T-cell leukemia (ATL) and HTLV-1 associated myelopathy/tropical spastic paraparesis (HAM/TSP) and has also been implicated in several disorders, including periodontal disease. The proviral load is an important biological marker for understanding HTLV-1 pathogenesis and elucidating whether or not the virus is related to the clinical manifestation of the disease. This study describes the oral health profile of HTLV-1 carriers and HAM/TSP patients in order to investigate the association between the proviral load in saliva and the severity of the periodontal disease and to examine virus intra-host variations from peripheral blood mononuclear cells and saliva cells. It is a cross-sectional analytical study of 90 individuals carried out from November 2006 to May 2008. Of the patients, 60 were HTLV-1 positive and 30 were negative. Individuals from the HTLV-1 positive and negative groups had similar mean age and social-economic status. Data were analyzed using two available statistical software packages, STATA 8.0 and SPSS 11.0 to conduct frequency analysis. Differences of P?<?0.05 were considered statistically significant. HTLV-1 patients had poorer oral health status when compared to seronegative individuals. A weak positive correlation between blood and saliva proviral loads was observed. The mean values of proviral load in blood and saliva in patients with HAM/TSP was greater than those in HTLV-1 carriers. The HTLV-1 molecular analysis from PBMC and saliva specimens suggests that HTLV-1 in saliva is due to lymphocyte infiltration from peripheral blood. A direct relationship between the proviral load in saliva and oral manifestations was observed. J. Med. Virol. 84:1428-1436, 2012. (c) 2012 Wiley Periodicals, Inc.

Differential Expression with the Bioconductor Project

A basic, yet challenging task in the analysis of microarray gene expression data is the identification of changes in gene expression that are associated with particular biological conditions. We discuss different approaches to this task and illustrate how they can be applied using software from the Bioconductor Project. A central problem is the high dimensionality of gene expression space, which prohibits a comprehensive statistical analysis without focusing on particular aspects of the joint distribution of the genes expression levels. Possible strategies are to do univariate gene-by-gene analysis, and to perform data-driven nonspecific filtering of genes before the actual statistical analysis. However, more focused strategies that make use of biologically relevant knowledge are more likely to increase our understanding of the data.

Evidence-based diagnosis

Making an accurate diagnosis is essential to ensure that a patient receives appropriate treatment and correct information regarding their prognosis. Characteristics of diagnostic tests are quantified in test accuracy studies, but many such studies have methodological flaws. The HSRC evidence-based diagnosis programme has focused on methods for systematic reviews of test accuracy studies, and the wider context in which tests are ordered and interpreted. We carried out a range of projects relating to literature searching, quality assessment, meta-analysis, presentation of results, and interactions between doctors and patients during the diagnostic process. We have shown that systematic reviews of test accuracy studies should search a range of databases and that current diagnostic filters do not have sufficient accuracy to be used in test accuracy reviews. Summary quality scores should not be used in test accuracy reviews; the Quality Assessment of Studies of Diagnostic Accuracy included in Systematic Reviews (QUADAS) tool for assessing test accuracy studies is acceptable for quality assessment. We have shown that the hierarchical summary receiver operating characteristic (HSROC) and bivariate models for meta-analysis of test accuracy are statistically equivalent in many circumstances, and have developed an add-on module for the statistical software package Stata that enables these statistically rigorous models to be fitted by those without expert statistical knowledge. Three areas that would benefit from further research are literature searching, synthesis of results from individual patient data and presentation of results.

COEFPLOT: Stata module to plot regression coefficients and other results

coefplot plots results from estimation commands or Stata matrices. Results from multiple models or matrices can be combined in a single graph. The default behavior of coefplot is to draw markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce various other types of graphs.

ADDPLOT: Stata module to add twoway plot objects to an existing twoway graph

addplot adds twoway plot objects to an existing twoway graph. This is useful if you want to add additional objects such as titles or extra data points to a twoway graph after it has been created. Most of what addplot can do, can also be done by rerunning the original graph command including additional options or plot statements. addplot, however, might be useful if you have to modify a graph for which you cannot rerun the original command, for example, because you only have the graph file but not the data that were used to create the graph. Furthermore, addplot can do certain things that would be difficult to achieve in a single graph command (e.g. customizing individual subgraphs within a by-graph). addplot also provides a substitute for some of the functionality of the graph editor.

gems: An R Package for Simulating from Disease Progression Models

Mathematical models of disease progression predict disease outcomes and are useful epidemiological tools for planners and evaluators of health interventions. The R package gems is a tool that simulates disease progression in patients and predicts the effect of different interventions on patient outcome. Disease progression is represented by a series of events (e.g., diagnosis, treatment and death), displayed in a directed acyclic graph. The vertices correspond to disease states and the directed edges represent events. The package gems allows simulations based on a generalized multistate model that can be described by a directed acyclic graph with continuous transition-specific hazard functions. The user can specify an arbitrary hazard function and its parameters. The model includes parameter uncertainty, does not need to be a Markov model, and may take the history of previous events into account. Applications are not limited to the medical field and extend to other areas where multistate simulation is of interest. We provide a technical explanation of the multistate models used by gems, explain the functions of gems and their arguments, and show a sample application.

ROBREG: Stata module providing robust regression estimators

robreg provides a number of robust estimators for linear regression models. Among them are the high breakdown-point and high efficiency MM-estimator, the Huber and bisquare M-estimator, and the S-estimator, each supporting classic or robust standard errors. Furthermore, basic versions of the LMS/LQS (least median of squares) and LTS (least trimmed squares) estimators are provided. Note that the moremata package, also available from SSC, is required.

Body Height Estimation from Femur Measurements in Postmortem Computed Tomography

After attending this presentation, attendees will: (1) understand how body height from computed tomography data can be estimated; and, (2) gain knowledge about the accuracy of estimated body height and limitations. The presentation will impact the forensic science community by providing knowledge and competence which will enable attendees to develop formulas for single bones to reconstruct body height using postmortem Computer Tomography (p-CT) data. The estimation of Body Height (BH) is an important component of the identification of corpses and skeletal remains. Stature can be estimated with relative accuracy via the measurement of long bones, such as the femora. Compared to time-consuming maceration procedures, p-CT allows fast and simple measurements of bones. This study undertook four objectives concerning the accuracy of BH estimation via p-CT: (1) accuracy between measurements on native bone and p-CT imaged bone (F1 according to Martin 1914); (2) intra-observer p-CT measurement precision; (3) accuracy between formula-based estimation of the BH and conventional body length measurement during autopsy; and, (4) accuracy of different estimation formulas available.1 In the first step, the accuracy of measurements in the CT compared to those obtained using an osteometric board was evaluated on the basis of eight defleshed femora. Then the femora of 83 female and 144 male corpses of a Swiss population for which p-CTs had been performed, were measured at the Institute of Forensic Medicine in Bern. After two months, 20 individuals were measured again in order to assess the intraobserver error. The mean age of the men was 53±17 years and that of the women was 61±20 years. Additionally, the body length of the corpses was measured conventionally. The mean body length was 176.6±7.2cm for men and 163.6±7.8cm for women. The images that were obtained using a six-slice CT were reconstructed with a slice thickness of 1.25mm. Analysis and measurements of CT images were performed on a multipurpose workstation. As a forensic standard procedure, stature was estimated by means of the regression equations by Penning & Riepert developed on a Southern German population and for comparison, also those referenced by Trotter & Gleser “American White.”2,3 All statistical tests were performed with a statistical software. No significant differences were found between the CT and osteometric board measurements. The double p-CT measurement of 20 individuals resulted in an absolute intra-observer difference of 0.4±0.3mm. For both sexes, the correlation between the body length and the estimated BH using the F1 measurements was highly significant. The correlation coefficient was slightly higher for women. The differences in accuracy of the different formulas were small. While the errors of BH estimation were generally ±4.5–5.0cm, the consideration of age led to an increase in accuracy of a few millimetres to about 1cm. BH estimations according to Penning & Riepert and Trotter & Gleser were slightly more accurate when age-at-death was taken into account.2,3 That way, stature estimations in the group of individuals older than 60 years were improved by about 2.4cm and 3.1cm.2,3 The error of estimation is therefore about a third of the common ±4.7cm error range. Femur measurements in p-CT allow very accurate BH estimations. Estimations according to Penning led to good results that (barely) come closer to the true value than the frequently used formulas by Trotter & Gleser “American White.”2,3 Therefore, the formulas by Penning & Riepert are also validated for this substantial recent Swiss population.

PSHARE: Stata module to compute and graph percentile shares

-pshare- computes and graphs percentile shares from individual level data. Percentile shares are often used in inequality research to study the distribution of income or wealth. They are defined as differences between Lorenz ordinates of the outcome variable. Technically, the observations are sorted in increasing order of the outcome variable and the specified percentiles are computed from the running sum of the outcomes. Percentile shares are then computed as differences between percentiles, divided by total outcome. pshare requires moremata to be installed on the system; see ssc describe moremata.

An analysis of the effects of a peer mentor program and other factors on HIV patients' retention in care and clinical outcomes at Thomas Street Health Clinic, Houston Texas

Research examining programs designed to retain patients in health care focus on repeated interactions between outreach workers and patients (Bradford et al. 2007; Cheever 2007). The purpose of this study was to determine if patients who are peer-mentored at their intake exam remain in care longer and attend more physicians' visits than those who were not mentored. Using patients' medical records and a previously created mentor database, the study determined how many patients attended their intake visit but subsequently failed to establish regular care. The cohort study examined risk factors for establishing care, determined if patients lacking a peer mentor failed to establish care more than peer mentor assisted patients, and subsequently if peer mentored patients had better health outcomes. The sample consists of 1639 patients who were entered into the Thomas Street Patient Mentor Database between May 2005 and June 2007. The assignment to the mentored group was haphazardly conducted based on mentor availability. The data from the Mentor Database was then analyzed using descriptive statistical software (SPSS version 15; SPSS Inc., Chicago, Illinois, USA). Results indicated that patients who had a mentor at intake were more likely to return for primary care HIV visits at 90 and 180 days. Mentored patients also were more likely to be prescribed ART within 180 days from intake. Other risk factors that impacted remaining in care included gender, previous care status, time from diagnosis to intake visit, and intravenous drug use. Clinical health outcomes did not differ significantly between groups. This supports that mentoring did improve outcomes. Continuing to use peer-mentoring programs for HIV care may help in increasing retention of patients in care and improving patients' health in a cost effective manner. Future research on the effects of peer mentoring on mentors, and effects of concordance of mentor and patient demographics may help to further improve peer-mentoring programs. ^

Relationship between parental feeding practices and weight status in a lower socioeconomic pre-school population

High prevalence of overweight and obesity among preschool children in the low income population is consistently documented in research with one of every seven low-income, preschool-aged children classified as obese. Parental feeding practices have the potential to be contributing factors to the obesity epidemic. However, the impact of parental feeding practices on obesity in preschool age children has not been well explored. The purpose of this study was to determine relationships between the parental feeding practices of using dessert, sweets or candy as a reward for finishing foods, restricting dessert if the child does not finish their plate at dinner, asking the child to consume everything on their plate at dinner, and having family dinners to obesity in low income, preschool age children.^ A cross-sectional secondary data analysis was completed using the STATA 11 statistical software. Descriptive statistics were completed to summarize demographic and BMI data of participants, as well as parental feeding behavior variables. Pearson’s correlation was implemented to determine a correlation between parental feeding behavior variables and BMI z scores. Predictive relationships between the variables were explored through multivariable linear regression analysis. Regression analyses were also completed factoring in the confounders of gender, age, and ethnicity.^ Results revealed (1) no significant correlations or predictive trends between the use of rewards, forced consumption, or family dinner and BMI in low income preschool age children, and (2) a significant negative correlation and predictive trend between restriction of desserts and BMI in low income preschool age children. Since the analysis supported the null hypothesis for the practices of reward use, forced consumption, and family dinner, these practices are not considered risk factors for obese level BMIs. The inverse association found for practice of restriction and BMI suggests it is unnecessary to discourage parents from using restriction. Limitations of the study included the sample size, reliability of the answers provided on the Healthy Home Survey by participant guardians, and generalizability of the sample to the larger population.^