Viviana Giampaoli

Influence of Dyslipidemia and Diabetes Mellitus on Chronic Periodontal Disease

BACKGROUND Periodontal disease is closely related to certain systemic conditions, such as type 2 diabetes mellitus (DM2), and, as recently described, dyslipidemia, a condition with alterations in blood lipids levels. However, more than acting as disease modifiers, these conditions commonly occur as comorbidities, possibly synergically affecting periodontal tissues. The aim of the current study is to identify whether DM2 and dyslipidemia are related to the occurrence and severity of chronic periodontitis. METHODS A total of 254 individuals participated: 56 were patients with DM2, 67 had dyslipidemia, 74 had DM2 and dyslipidemia, and 57 were systemically healthy individuals. The clinical examination included a full-mouth evaluation of periodontal probing depth, plaque score, bleeding on probing, and clinical attachment level (CAL). Blood samples were taken to assess fasting plasma glucose, low-density lipoprotein, high-density lipoprotein, and triglyceride levels. These parameters, as well as other medical conditions (i.e., smoking habits and body mass index), were considered in multiple regression analyses for data analyses (α = 5%). RESULTS Dyslipidemia was not related to periodontal disease (P >0.05). At the same time, DM2, age, and smoking showed a statistical and positive association, an increase in percentage of sites with CAL ≥3 and ≥5 mm. Regarding the percentage of sites presenting severe destruction (CAL ≥7 mm), only DM2 remained a significant risk factor (P <0.05). CONCLUSIONS It could be concluded that dyslipidemia did not influence periodontal conditions in participants with normal health or those with DM2. However, age, smoking habits, and especially DM2 were significantly associated with loss of CAL.

Stem cells labeled with superparamagnetic iron oxide nanoparticles in a preclinical model of cerebral ischemia: a systematic review with meta-analysis

IntroductionAlthough there is an increase in clinical trials assessing the efficacy of cell therapy in structural and functional regeneration after stroke, there are not enough data in the literature describing the best cell type to be used, the best route, and also the best nanoparticle to analyze these stem cells in vivo. This review analyzed published data on superparamagnetic iron oxide nanoparticle (SPION)-labeled stem cells used for ischemic stroke therapy.MethodWe performed a systematic review and meta-analysis of data from experiments testing the efficacy of cellular treatment with SPION versus no treatment to improve behavioral or modified neural scale outcomes in animal models of stroke by the Cochrane Collaboration and indexed in EMBASE, PubMed, and Web of Science since 2000. To test the impact of study quality and design characteristics, we used random-effects meta-regression. In addition, trim and fill were used to assess publication bias.ResultsThe search retrieved 258 articles. After application of the inclusion criteria, 24 reports published between January 2000 and October 2014 were selected. These 24 articles were analyzed for nanoparticle characteristics, stem cell types, and efficacy in animal models.ConclusionThis study highlights the therapeutic role of stem cells in stroke and emphasizes nanotechnology as an important tool for monitoring stem cell migration to the affected neurological locus.

Measurement of the nonlinear optical response of low-density lipoprotein solutions from patients with periodontitis before and after periodontal treatment: evaluation of cardiovascular risk markers

Abstract. The Z-Scan (ZS) technique in the thermal regime has been used to measure the nonlinear optical response of low-density lipoprotein (LDL). The ZS technique is carried out in LDL from 40 patients with chronic periodontitis before and after three, six, and 12 months of periodontal treatment. Clinical parameters such as probing depths, bleeding on probing, total and differential white blood cells counts, lipid profiles, cytokine levels, and antibodies against oxidized LDL are also determined and compared over time. Before the treatment, the ZS experimental results reveal that the LDL particles of these patients are heavily modified. Only after 12 months of the periodontal treatment, the ZS results obtained reveal behavioral characteristics of healthy particles. This conclusion is also supported by complementary laboratorial analysis showing that the periodontal treatment induces systemic changes in several inflammatory markers.

New prediction method for the mixed logistic model applied in a marketing problem

When units belong to a specific group, such as employees nested within companies, the data present a hierarchical structure that can be modeled by using mixed models. In addition to fixed effects, these models estimate the random effects for each group. The problem of assigning values to the random effects for new groups in order to predict the outcome in a future period, based on the parameters previously estimated, is the focus of this article. The empirical best predictor (EBP) has been applied to the logistic mixed model, but when there is more than one random effect, the processing time required to solve the multidimensional integrals is costly. A new methodology is proposed based on linear regression that considers the relationship among the random effects and the covariates aggregated at the group level. A comparison among the linear regression prediction method (LRPM), EBP, and the ordinary logistic model is provided through simulation studies and an application study with a mobile company. The results indicate that LRPM drastically reduced the computational effort, and at the same time, maintained a similar level of prediction in relation to EBP.

Semantic Verbal Fluency test in dementia: Preliminary retrospective analysis

The Semantic Verbal Fluency (SVF) test entails the generation of words from a given category within a pre-set time of 60 seconds. Objectives To verify whether socio-demographic and clinical data of individuals with dementia correlate with the performance on the SVF test and to ascertain whether differences among the criteria of number of answers, clusters and data spread over the intervals, predict clinical results. Methods This was a retrospective study of 49 charts of demented patients classified according to the Clinical Dementia Rating (CDR) scale. We correlated education, age and gender, as well as CDR and Mini-Mental State Exam (MMSE) scores with the number of answers, clustering and switching distributed over four 15-second intervals on the SVF test. Results The correlation between number of answers and quartiles was weak (r=0.407, p=0.004; r=0.484, p< 0.001) but correlation between the number of clusters and responses was strong (r=0.883, p< 0.001). The number of items on the SVF was statistically significant with MMSE score (p=0.01) and there was a tendency for significance on the CDR (p=0.06). The results indicated little activity regarding what we propose to call cluster recalling in the two groups. Discussion The SVF test, using number of items generated, was found to be more effective than classic screening tests in terms of speed and ease of application in patients with CDR 2 and 3.

Antifungal activity of lectins against yeast of vaginal secretion.

Lectins are carbohydrate-binding proteins of non-imune origin. This group of proteins is distributed widely in nature and they have been found in viruses, microorganisms, plants and animals. Lectins of plants have been isolated and characterized according to their chemical, physical-chemical, structural and biological properties. Among their biological activities, we can stress its fungicidal action. It has been previously described the effect of the lectins Dviol, DRL, ConBr and LSL obtained from the seeds of leguminous plants on the growth of yeasts isolated from vaginal secretions. In the present work the experiments were carried out in microtiter plates and the results interpreted by both methods: visual observations and a microplate reader at 530nm. The lectin concentrations varied from 0.5 to 256μg/mL, and the inoculum was established between 65-70% of trammitance. All yeast samples isolated from vaginal secretion were evaluated taxonomically, where were observed macroscopic and microscopic characteristics to each species. The LSL lectin did not demonstrate any antifungal activity to any isolate studied. The other lectins DRL, ConBr and DvioL, showed antifungal potential against yeast isolated from vaginal secretion. These findings offering offer a promising field of investigation to develop new therapeutic strategies against vaginal yeast infections, collaborating to improve womens health.

Análise proposicional quantitativa aplicada à pesquisa em administração

The analysis of interviews with open-ended questions is a common practice amongst researchers in the field of Management. The difficulty therein is to convert the linguistic data into categories or quantitative values for subsequent statistical treatment. Proposals made to this end generally entail counting lexical occurrences which, since they are founded on previously established meanings, fail to include semantic associations made by interviews. This article aims to present an analysis tool comprising a set of techniques apt to generate linguistic units that can be statistically described, compared, modeled and inferred: the Quantitative Propositional Analysis (QPA). Its main difference from other such methods lies in the choice of a proposition – and not the lexical unit – as analysis unit. We present the application of this method through a study about the international expansion of retail firms. keywords Quantitative propositional analysis, content analysis, correspondence analysis, research methods, research in management. Resumen El análisis de entrevistas con cuestiones abiertas es frecuente en el cotidiano del investigador en Administración. Una de sus dificultades es la conversión de lo datos de la elocución en categorías o valores cuantitativos para posterior tratamiento estadístico. Las propuestas en ese sentido, en general, están basadas en resultados de ocurrencias léxicas que, fundamentadas en significaciones previamente establecidas, dejan de lado las asociaciones semánticas hechas por el propio entrevistado en el contexto de la entrevista. El objetivo de este artículo es presentar un instrumento de análisis compuesto de un conjunto de procedimientos para la generación de unidades lingüísticas pasibles de descripción, comparación, modelo e inferencia estadísticas: el análisis proposicional cuantitativo (APC). Su principal diferencia en relación a otros métodos consiste en la elección de la proposición, y no del léxico, como unidad de análisis. La aplicación del método es presentada en un estudio sobre internacionalización de empresas al por menor. Palabras clave Análisis proposicional cuantitativo, análisis del contenido, análisis de correspondencia, métodos de investigación, investigación en administración. Adriana Beatriz Madeira adri.madeira@mackenzie.br Professora do Centro de Ciências Sociais e Aplicadas, Universidade Presbiteriana Mackenzie – São Paulo – SP, Brasil Marcos Lopes marcoslopes@usp.br Professor da Faculdade de Filosofia, Letras e Ciências Humanas, Universidade de São Paulo – São Paulo – SP, Brasil Viviana Giampaoli vivig@ime.usp.br Professora do Departamento de Estatística, Instituto de Matemática e Estatística, Universidade de São Paulo – São Paulo – SP, Brasil José A. G. da Silveira jags@usp.br Professor da Faculdade de Economia, Administração e Contabilidade, Universidade de São Paulo, e da Faculdade de Engenharia Industrial da Fundação de Ciências Aplicadas, Universidade de São Paulo – São Paulo Brasil ARTIGOS ISSN 0034-7590 396 w ©RAE n São Paulo n v. 51 n n.4 n ju l/ago. 2011 n 396-410 PALAVRAS-CHAVE Análise proposicional quantitativa, análise de conteúdo, análise de correspondência, métodos de pesquisa, pesquisa em administração. Recebido em 23.01.2011. Aprovado em 21.06.2011 Avaliado pelo sistema double blind review Editor Científico: Felipe ZambaldiThe analysis of interviews with open-ended questions is a common practice amongst researchers in the field of Management. The difficulty therein is to convert the linguistic data into categories or quantitative values for subsequent statistical treatment. Proposals made to this end generally entail counting lexical occurrences which, since they are founded on previously established meanings, fail to include semantic associations made by interviews. This article aims to present an analysis tool comprising a set of techniques apt to generate linguistic units that can be statistically described, compared, modeled and inferred: the Quantitative Propositional Analysis (QPA). Its main difference from other such methods lies in the choice of a proposition – and not the lexical unit – as analysis unit. We present the application of this method through a study about the international expansion of retail firms.

A model for the functional assessment of elderly with myeloid neoplasms

Objective Myeloid neoplasms are heterogeneous diseases that are more incident in the elderly. The goals of this study were to aggregate a geriatric approach to the patient assessment, to show the impact of gender, age, hemoglobin concentration and comorbidities on the functionality of elderly with myeloid neoplasms and to better understand how the instruments of functional assessment work according to the aggressiveness of the disease. Methods Elderly patients (≥60 years old) with myeloid neoplasms were assessed using the Karnofsky scale, Eastern Cooperative Oncologic Group scale, and basic and instrumental activities of daily living scales. The hematopoietic cell transplantation-comorbidity index assessed the comorbidities. A mixed logistical regression model was fitted to estimate the impact of gender, age, hemoglobin concentration and the hematopoietic cell transplantation-comorbidity index on patients’ functionality. Results Eighty-two patients with a mean age of 72.8 years (range: 60–92 years) were evaluated. Eighty percent had good Karnofsky and Eastern Cooperative Oncologic Group scales and 39% were independent according to the daily living activity scales. All of the patients with poor Karnofsky and Eastern Cooperative Oncologic Group scales were classified as dependent by the daily living activity scales. The mixed logistic regression models showed that age, gender, hemoglobin concentration and the comorbidity index impacted on the daily living activity scales. Karnofsky and Eastern Cooperative Oncologic Group scales were affected by hemoglobin and the comorbidity index. The model hypothesized the hemoglobin concentration at which there was a higher risk of poor Karnofsky and Eastern Cooperative Oncologic Group scales. This hemoglobin concentration depended on comorbidities and on the aggressiveness of the myeloid neoplasm. Conclusion The geriatric approach improved the sensitivity and specificity of the patients’ assessment. Hemoglobin concentration associated to the risk of poor Karnofsky and Eastern Cooperative Oncologic Group scales depended on the comorbidity score and on the disease aggressiveness. The Karnofsky and Eastern Cooperative Oncologic Group scales had higher sensitivity in patients with more aggressive diseases.

Hypothesis testing in the unrestricted and restricted parametric spaces of structural models

Hypothesis for testing the equality of slopes in measurement error models, incorporating the additional assumption that the slopes lie in a closed interval is considered. Wald type statistics based on the maximum likelihood estimators are considered under both, restricted and unrestricted parametric spaces. Their asymptotic behaviors are analyzed through different simulation studies. The statistics performance is analyzed in terms of power. Analysis of the results suggests that the statistics considered under the restricted parametric space has shown a better performance since the tests based on these statistics are more powerful. An example illustrates the proposal.

Prediction in Multilevel Logistic Regression

The purpose of this article is to present a new method to predict the response variable of an observation in a new cluster for a multilevel logistic regression. The central idea is based on the empirical best estimator for the random effect. Two estimation methods for multilevel model are compared: penalized quasi-likelihood and Gauss–Hermite quadrature. The performance measures for the prediction of the probability for a new cluster observation of the multilevel logistic model in comparison with the usual logistic model are examined through simulations and an application.