Benzocaine loaded biodegradable poly-(D,L-lactide-co-glycolide) nanocapsules: factorial design and characterization

Local anesthetics are able to induce pain relief since they bind to the sodium channel of excitable membranes, blocking the influx of sodium ions and the propagation of the nervous impulse. Benzocaine (BZC) is a local anesthetic that presents limited application in topical formulations due to its low water-solubility. This study aimed to develop polymeric nanocapsules as a drug delivery system for the local anesthetic benzocaine (BZC). To do so, BZC loaded poly(D,L-lactide-co-glycolide) (PLGA) nanocapsules were prepared using the nanoprecipitation method and were characterized. The factorial experimental design was used to study the influence of four different independent variables oil response to nanocapsules drug loading. The physical characteristics of PLGA nanocapsules were evaluated by analyzing the particle size, the polydispersion index and the zeta potential, using a particle size analyzer. The results of the optimized formulation showed a size distribution with a polydispersity index of 0.12. an average diameter of 123 nm, zeta potential of -33.6 mV and a drug loading of more than 69%. The release profiles showed a significant difference in the release behavior for the pure drug in solution when compared with that containing benzocaine loaded PLGA nanocapsules. Thus, the prepared nonocapsules described here may be of clinical importance in both the processes of stabilization and delivery of benzocaine for pain treatment. (c) 2009 Elsevier B.V. All rights reserved.

La distribución factorial del ingreso en el Nuevo Modelo Económico en México

Incluye Bibliografía

La distribución factorial del ingreso en el Nuevo Modelo Económico en México

Incluye Bibliografía

A useful empirical bayesian method to analyse industrial data from saturated factorial designs

The use of saturated two-level designs is very popular, especially in industrial applications where the cost of experiments is too high. Standard classical approaches are not appropriate to analyze data from saturated designs, since we could only get the estimates of the main factor effects and we would not have degrees of freedom to estimate the variance of the error. In this paper, we propose the use of empirical Bayesian procedures to get inferences for data obtained from saturated designs. The proposed methodology is illustrated assuming a simulated data set. © 2013 Growing Science Ltd. All rights reserved.

Treatment of Comorbid Migraine and Temporomandibular Disorders: A Factorial, Double-Blind, Randomized, Placebo-Controlled Study

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Ethanol production by Zymomonas mobilis during sucrose fermentation: Optimization of culture conditions using factorial design

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Factorial design and characterization studies for articaine hydrochloride loaded alginate/chitosan nanoparticles

Nowadays, articaine hydrochloride (ATC) is a local anesthetic widely used in dental procedures, but its side effects include paresthesia and nerve injury. Alginate/chitosan nanoparticles (AG/CSnano) can be used as carrier for drugs, overcoming the problems. The aim of this work was to evaluate the factors (Calcium/alginate [Ca2+:AG] and Chitosan/alginate [CS:AG] mass ratios) influence on the average size, polydispersity index, zeta potential and encapsulation efficiency of ATC. AG/CSnano containing ATC were prepared by ionic pregelation method. A three-level factorial design was carried out and the factors varied were Ca2+/AG mass ratio and CS/AG mass ratio. There were obtained nanoparticles with size range of 340–550 nm and polydispersity index between 0.2 and 0.5, zeta potential range –19 and –22 mV and encapsulation efficiency of ATC in AG/Csnano between 22 and 45%. According to the results, the average size, polydispersity index and encapsulation efficiency were significantly affected to the variation of Ca2+/AG and CS/AG mass ratio, but the zeta potential didn't change significantly with factor variations. The factorial design showed it was possible to identify formulations that presented better results for the parameters measured. The factor chosen for the suitable formulations was the encapsulation efficiency. Through this parameter, one formulation was chosen with highest encapsulation efficiency of ATC and presented good colloidal stability parameters aiming future clinical applications.

Development of Cationic Solid Lipid Nanoparticles with Factorial Design-Based Studies for Topical Administration of Doxorubicin

Topical chemotherapy using doxorubicin, a powerful anticancer drug, can be used as an alternative with reduced systemic toxicity when treating skin cancer. The aim of the present work was to use factorial design-based studies to develop cationic solid lipid nanoparticles containing doxorubicin; further investigations into the influence of these particles on the drug's cytotoxicity and cellular uptake in B16F10 murine melanoma cells were performed. A 3(2) full factorial design was applied for two different lipid phases; one phase used stearic acid and the other used a 1:2 mixture of stearic acid and glyceryl behenate. The two factors investigated included the ratio between the lipid and the water phase and the ratio between the surfactant (poloxamer) and the co-surfactant (cetylpyridinium chloride). It was observed that the studied factors did not affect the mean diameter or the polydispersity of the obtained nanoparticles; however, they did significantly affect the zeta potential values. Optimised formulations with particle sizes ranging from 251 to 306 nm and positive zeta potentials were selected for doxorubicin incorporation. High entrapment efficiencies were achieved (97%) in formulations with higher amounts of stearic acid, suggesting that cationic charges on doxorubicin molecules may interact with the negative charges in stearic acid. Melanoma culture cell experiments showed that cationic solid lipid nanoparticles without drug were not cytotoxic to melanoma cells. The encapsulation of doxorubicin significantly increased cytotoxicity, indicating the potential of these nanoparticles for the treatment of skin cancer.

A partial dependence factorial analysis to deal with selection bias in observational studis

This thesis presents a creative and practical approach to dealing with the problem of selection bias. Selection bias may be the most important vexing problem in program evaluation or in any line of research that attempts to assert causality. Some of the greatest minds in economics and statistics have scrutinized the problem of selection bias, with the resulting approaches – Rubin’s Potential Outcome Approach(Rosenbaum and Rubin,1983; Rubin, 1991,2001,2004) or Heckman’s Selection model (Heckman, 1979) – being widely accepted and used as the best fixes. These solutions to the bias that arises in particular from self selection are imperfect, and many researchers, when feasible, reserve their strongest causal inference for data from experimental rather than observational studies. The innovative aspect of this thesis is to propose a data transformation that allows measuring and testing in an automatic and multivariate way the presence of selection bias. The approach involves the construction of a multi-dimensional conditional space of the X matrix in which the bias associated with the treatment assignment has been eliminated. Specifically, we propose the use of a partial dependence analysis of the X-space as a tool for investigating the dependence relationship between a set of observable pre-treatment categorical covariates X and a treatment indicator variable T, in order to obtain a measure of bias according to their dependence structure. The measure of selection bias is then expressed in terms of inertia due to the dependence between X and T that has been eliminated. Given the measure of selection bias, we propose a multivariate test of imbalance in order to check if the detected bias is significant, by using the asymptotical distribution of inertia due to T (Estadella et al. 2005) , and by preserving the multivariate nature of data. Further, we propose the use of a clustering procedure as a tool to find groups of comparable units on which estimate local causal effects, and the use of the multivariate test of imbalance as a stopping rule in choosing the best cluster solution set. The method is non parametric, it does not call for modeling the data, based on some underlying theory or assumption about the selection process, but instead it calls for using the existing variability within the data and letting the data to speak. The idea of proposing this multivariate approach to measure selection bias and test balance comes from the consideration that in applied research all aspects of multivariate balance, not represented in the univariate variable- by-variable summaries, are ignored. The first part contains an introduction to evaluation methods as part of public and private decision process and a review of the literature of evaluation methods. The attention is focused on Rubin Potential Outcome Approach, matching methods, and briefly on Heckman’s Selection Model. The second part focuses on some resulting limitations of conventional methods, with particular attention to the problem of how testing in the correct way balancing. The third part contains the original contribution proposed , a simulation study that allows to check the performance of the method for a given dependence setting and an application to a real data set. Finally, we discuss, conclude and explain our future perspectives.

Factorial designs: an overview with applications to orthodontic clinical trials.

Factorial designs for clinical trials are often encountered in medical, dental, and orthodontic research. Factorial designs assess two or more interventions simultaneously and the main advantage of this design is its efficiency in terms of sample size as more than one intervention may be assessed on the same participants. However, the factorial design is efficient only under the assumption of no interaction (no effect modification) between the treatments under investigation and, therefore, this should be considered at the design stage. Conversely, the factorial study design may also be used for the purpose of detecting an interaction between two interventions if the study is powered accordingly. However, a factorial design powered to detect an interaction has no advantage in terms of the required sample size compared to a multi-arm parallel trial for assessing more than one intervention. It is the purpose of this article to highlight the methodological issues that should be considered when planning, analysing, and reporting the simplest form of this design, which is the 2 × 2 factorial design. An example from the field of orthodontics using two parameters (bracket type and wire type) on maxillary incisor torque loss will be utilized in order to explain the design requirements, the advantages and disadvantages of this design, and its application in orthodontic research.

Using Factorial Surveys and Stated Choice Experiments to Investigate Discriminatory Attitudes and Preferences

Empirical research on discriminatory attitudes and behaviour grapples with the social undesirability of its object. In many studies using regular survey methods, estimates are biased, and the social context of discrimination is not taken into account. Several methods have been developed, especially to deal with the first problem. In this regard, the estimation of the ‘true value’ of discriminatory attitudes is at the centre of interest. However, methodological contributions focusing on the social context of attitude communication and discriminatory behaviour, as well as the correlation between both, are rare. We present two experimental methods which address those issues: factorial surveys and stated choice experiments. In a first study, the usefulness of factorial surveys is demonstrated with data on German anti-Semitism (N=279). We show that the rate of approval with anti-Semitic statements increases if (a) respondents are told that the majority of fellows agree with such statements, (b) the term “Jews” is replaced by the term “Israelis”, and (c) reference to the Holocaust is made. Apart from the main effects of these experimental factors, significant interaction effects regarding the political attitudes and social status of respondents are observed. In a second study, a stated choice experiment on the purchase of olive oil and tomatoes was conducted in Germany (N=440). We find that respondents prefer Italian and Dutch products (control treatment) compared to Israeli and Palestinian ones (discrimination treatments). There are no significant differences between preferences for a so called ‘Peace product’ (which is produced jointly by Israelis and Palestinians) and products from Italy as well as the Netherlands. Yet, taking discriminatory attitudes (anti-Semitic and anti-Arabic attitudes) into account, a strong correlation between those attitudes and stated behaviour (purchase of Israeli, Palestinian and jointly produced products) can be found. This adds support to the hypothesis that discriminatory attitudes hold behavioural consequences.

Predictors of healthcare professionals' attitudes towards family involvement in safety-relevant behaviours: a cross-sectional factorial survey study.

OBJECTIVES To investigate predictors of healthcare professionals' (HCPs) attitudes towards family involvement in safety-relevant behaviours. DESIGN A cross-sectional fractional factorial survey that assessed HCPs' attitudes towards family involvement in two error scenarios relating to hand hygiene and medication safety. Each survey comprised two randomised vignettes that described the potential error, how the family member communicated with the HCP about the error and how the HCP responded to the family member's question. SETTING 5 teaching hospitals in London, the Midlands and York. HCPs were approached on a range of medical and surgical wards. PARTICIPANTS 160 HCPs (73 doctors; 87 nurses) aged between 21 and 65 years (mean 37) 102 were female. OUTCOME MEASURES HCP approval of family member's behaviour; HCP reaction to the family member; anticipated effects on the family member-HCP relationship; HCP support for being questioned about hand hygiene/medication; affective rating responses. RESULTS HCPs supported family member's intervening (88%) but only 41% agreed this would have positive effects on the family member/HCP relationship. Across vignettes and error scenarios the strongest predictors of attitudes were how the HCP (in the scenario) responded to the family member and whether an error actually occurred. Doctors (vs nurses) provided systematically more positive affective ratings to the vignettes. CONCLUSIONS Important predictors of HCPs' attitudes towards family members' involvement in patient safety have been highlighted. In particular, a discouraging response from HCP's decreased support for family members being involved and had strong perceived negative effects on the family member/HCP relationship.

The impact of the position effect on the factorial structure of the Culture Fair Test (CFT)

The Culture Fair Test (CFT) is a psychometric test of fluid intelligence consisting of four subtests; Series, Classification, Matrices, and Topographies. The four subtests are only moderately intercorrelated, doubting the notion that they assess the same construct (i.e., fluid intelligence). As an explanation of these low correlations, we investigated the position effect. This effect is assumed to reflect implicit learning during testing. By applying fixed-links modeling to analyze the CFT data of 206 participants, we identified position effects as latent variables in the subtests; Classification, Matrices, and Topographies. These position effects were disentangled from a second set of latent variables representing fluid intelligence inherent in the four subtests. After this separation of position effect and basic fluid intelligence, the latent variables representing basic fluid intelligence in the subtests Series, Matrices, and Topographies could be combined to one common latent variable which was highly correlated with fluid intelligence derived from the subtest Classification (r=.72). Correlations between the three latent variables representing the position effects in the Classification, Matrices, and Topographies subtests ranged from r=.38 to r=.59. The results indicate that all four CFT subtests measure the same construct (i.e., fluid intelligence) but that the position effect confounds the factorial structure