Optimization of Pothomorphe umbellata (L.) Miquel topical formulations using experimental design

Pothomorphe umbellata is a native plant widely employed in the Brazilian popular medicine. This plant has been shown to exert a potent antioxidant activity on the skin and to delay the onset and reduce the incidence of UVB-induced skin damage and photoaging. The aim of this work was to optimize the appearance, the centrifuge stability and the permeation of emulsions containing R umbellata (0. 1% 4-nerolidylchatecol). Experimental design was used to study ternary mixtures models with constraints and graphical representation by phase diagrams. The constraints reduce the possible experimental domain, and for this reason, this methodology offers the maximum information while requiring the minimum investment. The results showed that the appearance follows a linear model, and that the aqueous phase was the principal factor affecting the appearance; the centrifuge stability parameter followed a mathernatic quadratic model and the interactions between factors produced the most stable emulsions; skin permeation was improved by the oil phase, following a linear model generated by data analysis. We propose as optimized P. umbellata formulation: 68.4% aqueous phase, 26.6% oil phase and 5.0% of self-emulsifying phase. This formulation displayed an acceptable compromise between factors and responses investigated. (c) 2007 Elsevier B.V. All rights reserved.

Optimization of preservative system in ophthalmic suspension with dexamethasone and polymyxin B

A simplex-lattice statistical project was employed to study an optimization method for a preservative system in an ophthalmic suspension of dexametasone and polymyxin B. The assay matrix generated 17 formulas which were differentiated by the preservatives and EDTA (disodium ethylene diamine-tetraacetate), being the independent variable: X-1 = chlorhexidine digluconate (0.010 % w/v); X-2 = phenylethanol (0.500 % w/v); X-3 = EDTA (0.100 % w/v). The dependent variable was the Dvalue obtained from the microbial challenge of the formulas and calculated when the microbial killing process was modeled by an exponential function. The analysis of the dependent variable, performed using the software Design Expert/W, originated cubic equations with terms derived from stepwise adjustment method for the challenging microorganisms: Pseudomonas aeruginosa, Burkholderia cepacia, Staphylococcus aureus, Candida albicans and Aspergillus niger. Besides the mathematical expressions, the response surfaces and the contour graphics were obtained for each assay. The contour graphs obtained were overlaid in order to permit the identification of a region containing the most adequate formulas (graphic strategy), having as representatives: X-1 = 0.10 ( 0.001 % w/v); X-2 = 0.80 (0.400 % w/v); X-3 = 0.10 (0.010 % w/v). Additionally, in order to minimize responses (Dvalue), a numerical strategy corresponding to the use of the desirability function was used, which resulted in the following independent variables combinations: X-1 = 0.25 (0.0025 % w/v); X-2 = 0.75 (0.375 % w/v); X-3 = 0. These formulas, derived from the two strategies (graphic and numerical), were submitted to microbial challenge, and the experimental Dvalue obtained was compared to the theoretical Dvalue calculated from the cubic equation. Both Dvalues were similar to all the assays except that related to Staphylococcus aureus. This microorganism, as well as Pseudomonas aeruginosa, presented intense susceptibility to the formulas independently from the preservative and EDTA concentrations. Both formulas derived from graphic and numerical strategies attained the recommended criteria adopted by the official method. It was concluded that the model proposed allowed the optimization of the formulas in their preservation aspect.

Processing optimization of probiotic yogurt containing glucose oxidase using response surface methodology

Exposure to oxygen may induce a lack of functionality of probiotic dairy foods because the anaerobic metabolism of probiotic bacteria compromises during storage the maintenance of their viability to provide benefits to consumer health. Glucose oxidase can constitute a potential alternative to increase the survival of probiotic bacteria in yogurt because it consumes the oxygen permeating to the inside of the pot during storage, thus making it possible to avoid the use of chemical additives. This research aimed to optimize the processing of probiotic yogurt supplemented with glucose oxidase using response surface methodology and to determine the levels of glucose and glucose oxidase that minimize the concentration of dissolved oxygen and maximize the Bifidobacterium longum count by the desirability function. Response surface methodology mathematical models adequately described the process, with adjusted determination coefficients of 83% for the oxygen and 94% for the B. longum. Linear and quadratic effects of the glucose oxidase were reported for the oxygen model, whereas for the B. longum count model an influence of the glucose oxidase at the linear level was observed followed by the quadratic influence of glucose and quadratic effect of glucose oxidase. The desirability function indicated that 62.32 ppm of glucose oxidase and 4.35 ppm of glucose was the best combination of these components for optimization of probiotic yogurt processing. An additional validation experiment was performed and results showed acceptable error between the predicted and experimental results.

Topliss method in the optimization of salicylic acid derivatives as potential antimycobacterial agents

The Topliss method was used to guide a synthetic path in support of drug discovery efforts toward the identification of potent antimycobacterial agents. Salicylic acid and its derivatives, p-chloro, p-methoxy, and m-chlorosalicylic acid, exemplify a series of synthetic compounds whose minimum inhibitory concentrations for a strain of Mycobacterium were determined and compared to those of the reference drug, p-aminosalicylic acid. Several physicochemical descriptors (including Hammett`s sigma constant, ionization constant, dipole moment, Hansch constant, calculated partition coefficient, Sterimol-L and -B-4 and molecular volume) were considered to elucidate structure-activity relationships. Molecular electrostatic potential and molecular dipole moment maps were also calculated using the AM1 semi-empirical method. Among the new derivatives, m-chlorosalicylic acid showed the lowest minimum inhibitory concentration. The overall results suggest that both physicochemical properties and electronic features may influence the biological activity of this series of antimycobacterial agents and thus should be considered in designing new p-aminosalicylic acid analogs.

Box-Behnken design for the optimization of an enantioselective method for the simultaneous analysis of propranolol and 4-hydroxypropranolol by CE

An experimental design optimization (Box-Behnken design, BBD) was used to develop a CE method for the simultaneous resolution of propranolol (Prop) and 4-hydroxypropranolol enantiomers and acetaminophen (internal standard). The method was optimized using an uncoated fused silica capillary, carboxymethyl-beta-cyclodextrin (CM-beta-CD) as chiral selector and triethylamine/phosphoric acid buffer in alkaline conditions. A BBD for four factors was selected to observe the effects of buffer electrolyte concentration, pH, CM-beta-CD concentration and voltage on separation responses. Each factor was studied at three levels: high, central and low, and three center points were added. The buffer electrolyte concentration ranged from 25 to 75 mM, the pH ranged from 8 to 9, the CM-beta-CD concentration ranged from 3.5 to 4.5%w/v, and the applied run voltage ranged from 14 to 20 W. The responses evaluated were resolution and migration time for the last peak. The obtained responses were processed by Minitab (R) to evaluate the significance of the effects and to find the optimum analysis conditions. The best results were obtained using 4%w/v CM-beta-CD in 25 mM triethylamine/H(3)PO(4) buffer at pH 9 as running electrolyte and 17 kV of voltage. Resolution values of 1.98 and 1.95 were obtained for Prop and 4-hydroxypropranolol enantiomers, respectively. The total analysis time was around of 15 min. The BBD showed to be an adequate design for the development of a CE method, resulting in a rapid and efficient optimization of the pH and concentration of the buffer, cyclodextrin concentration and applied voltage.

Three-dimensional electrical impedance, tomography: A topology optimization approach

Electrical impedance tomography is a technique to estimate the impedance distribution within a domain, based on measurements on its boundary. In other words, given the mathematical model of the domain, its geometry and boundary conditions, a nonlinear inverse problem of estimating the electric impedance distribution can be solved. Several impedance estimation algorithms have been proposed to solve this problem. In this paper, we present a three-dimensional algorithm, based on the topology optimization method, as an alternative. A sequence of linear programming problems, allowing for constraints, is solved utilizing this method. In each iteration, the finite element method provides the electric potential field within the model of the domain. An electrode model is also proposed (thus, increasing the accuracy of the finite element results). The algorithm is tested using numerically simulated data and also experimental data, and absolute resistivity values are obtained. These results, corresponding to phantoms with two different conductive materials, exhibit relatively well-defined boundaries between them, and show that this is a practical and potentially useful technique to be applied to monitor lung aeration, including the possibility of imaging a pneumothorax.

Endostatin- and interleukin-2-expressing retroviral bicistronic vector for gene therapy of metastatic renal cell carcinoma

Background Metastatic renal cell carcinoma (mRCC) is one of the most treatment-resistant malignancies. Despite all new therapeutic advances, almost all patients develop resistance to treatment and cure is rarely seen. In the present study, we evaluated the antitumor effect of a bicistronic retrovirus vector encoding both endostatin (ES) and interleukin (IL)-2 using an orthotopic metastatic RCC mouse model. Methods Balb/C-bearing Renca cells were treated with NIH/3T3-LendIRES-IL-2-SN cells. In the survival studies, mice were monitored daily until they died. At the end of the in vivo experiment, serum levels of IL-2 and ES were measured, the lung was weighed, and the number of metastatic nodules, nodule area, tumor vessels and proliferation of tumor-infiltrating Renca cells were determined. Results Inoculation of NIH/3T3-LendIRES-IL-2-SN cells resulted in an increase in ES and IL-2 levels in the treated group (p < 0.05). There was a significant decrease in lung wet weight, lung nodule area and tumor vessels in the treated group compared to the control group (p < 0.001). The proliferation of Renca cells in the bicistronic-treated group was significantly reduced compared to the control group (p < 0.05). Kaplan-Meier survival curves showed that the probability of survival was significantly higher for mice submitted to bicistronic therapy (log-rank test, p = 0.0016). Bicistronic therapy caused an increase in the infiltration of CD4, CD4 interferon (IFN)gamma-producing, CD8, CD8 IFN gamma-producing and natural killer (CD49b) cells. Conclusions Retroviral bicistronic gene transfer led to the secretion of functional ES and IL-2 that was sufficiently active to: (i) inhibit tumor angiogenesis and tumor cell proliferation and (ii) increase the infiltration of immune cells (C) Copyright. 2011 John Wiley & Sons, Ltd.

An fMRI Normative Database for Connectivity Networks Using One-Class Support Vector Machines

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal pattern, and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy Subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored. Hum Brain Mapp 30:1068-1076, 2009. (C) 2008 Wiley-Liss. Inc.

The impact of functional connectivity changes on support vector machines mapping of fMRI data

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called ""mass-univariate"" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate `approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM`s power to detect discriminative voxels. (C) 2008 Elsevier B.V. All rights reserved.

A hypothesis for explaining single outbreaks (like the Black Death in European cities) of vector-borne infections

We propose a mechanism by which single outbreaks of vector-borne infections can happen even when the value of the basic reproduction number, R(o), of the infection is below one. With this hypothesis we have shown that dynamical models simulations demonstrate that the arrival of a relatively small (with respect to the host population) number of infected vectors can trigger a short-lived epidemic but with a huge number of cases. These episodes are characterized by a sudden outbreak in a previously virgin area that last from weeks to a few months, and then disappear without leaving vestiges. The hypothesis proposed in this paper to explain those single outbreaks of vector-borne infections, even when total basic reproduction number, Ro, is less than one (which explain the fact that those infections fail to establish themselves at endemic levels), is that the vector-to-host component of Ro is greater than one and that a sufficient amount of infected vectors are imported to the vulnerable area, triggering the outbreak. We tested the hypothesis by performing numerical simulations that reproduce the observed outbreaks of chikungunya in Italy in 2007 and the plague in Florence in 1348. The theory proposed provides an explanation for isolated outbreaks of vector-borne infections, ways to calculate the size of those outbreaks from the number of infected vectors arriving in the affected areas. Given the ever-increasing worldwide transportation network, providing a high degree of mobility from endemic to virgin areas, the proposed mechanism may have important implications for public health planning. (C) 2009 Elsevier Ltd. All rights reserved.

An optimization model for antibiotic use

There is a positive correlation between the intensity of use of a given antibiotic and the prevalence of resistant strains. The more you treat, more patients infected with resistant strains appears and, as a consequence, the higher the mortality due to the infection and the longer the hospitalization time. In contrast, the less you treat, the higher the mortality rates and the longer the hospitalization time of patients infected with sensitive strains that could be successfully treated. The hypothesis proposed in this paper is an attempt to solve such a conflict: there must be an optimum treatment intensity that minimizes both the additional mortality and hospitalization time due to the infection by both sensitive and resistant bacteria strains. In order to test this hypothesis we applied a simple mathematical model that allowed us to estimate the optimum proportion of patients to be treated in order to minimize the total number of deaths and hospitalization time due to the infection in a hospital setting. (C) 2007 Elsevier Inc. All rights reserved.

A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.

Neonatal BCG Immunization Followed by DNAhsp65 Boosters: Highly Immunogenic but not Protective Against Tuberculosis - a Paradoxical Effect of the Vector?

A new tuberculosis vaccine is urgently needed. Prime-boost strategies are considered very promising and the inclusion of BCG is highly desirable. In this investigation, we tested the protective efficacy of BCG delivered in the neonatal period followed by boosters in the adult phase with a DNA vaccine containing the hsp65 gene from Mycobacterium leprae (pVAXhsp65). Immune responses were characterized by serum anti-hsp65 antibody levels and IFN-gamma and IL-5 production by the spleen. Amounts of these cytokines were also determined in lung homogenates. Protective efficacy was established by the number of colony-forming units (CFU) and histopathological analysis of the lungs after challenge with Mycobacterium tuberculosis. Immunization with BCG alone triggered a significant reduction of CFU in the lungs and also clearly preserved the pulmonary parenchyma. BCG priming also increased the immunogenicity of pVAXhsp65. However, boosters with pVAXhsp65 or the empty vector abolished the protective efficacy of BCG. Also, higher IL-5 levels were produced by spleen and lungs after DNA boosters. These results demonstrated that neonatal BCG immunization followed by DNAhsp65 boosters is highly immunogenic but is not protective against tuberculosis.

Integration pattern of HIV-1 based lentiviral vector carrying recombinant coagulation factor VIII in Sk-Hep and 293T cells

293T and Sk-Hep-1 cells were transduced with a replication-defective self-inactivating HIV-1 derived vector carrying FVIII cDNA. The genomic DNA was sequenced to reveal LTR/human genome junctions and integration sites. One hundred and thirty-two sequences matched human sequences, with an identity of at least 98%. The integration sites in 293T-FVIIIDB and in Sk-Hep-FVIIIDB cells were preferentially located in gene regions. The integrations in both cell lines were distant from the CpG islands and from the transcription start sites. A comparison between the two cell lines showed that the lentiviral-transduced DNA had the same preferred regions in the two different cell lines.

Reference distribution of the bioelectrical impedance vector in healthy term newborns

Bioelectrical impedance vector analysis (BIVA) is a new method that is used for the routine monitoring of the variation in body fluids and nutritional status with assumptions regarding body composition values. The aim of the present study was to determine bivariate tolerance intervals of the whole-body impedance vector and to describe phase angle (PA) values for healthy term newborns aged 7-28 d. This descriptive cross-sectional study was conducted on healthy term neonates born at a low-risk public maternity. General and anthropometric neonatal data and bioelectrical impedance data (800 mu A-50 kHz) were obtained. Bivariate vector analysis was conducted with the resistance-reactance (RXc) graph method. The BIVA software was used to construct the graphs. The study was conducted on 109 neonates (52.3% females) who were born at term, adequate for gestational age, exclusively breast-fed and aged 13 (SD 3.6) d. We constructed one standard, reference, RXc-score graph and RXc-tolerance ellipses (50, 75 and 95 %) that can be used with any analyser. Mean PA was 3.14 (SD 0.43)degrees (3.12 (SD 0.39)degrees for males and 3.17 (SD 0.48)degrees for females). Considering the overlapping of ellipses of males and females with the general distribution, a graph for newborns aged 7-28 d with the same reference tolerance ellipse was defined for boys and girls. The results differ from those reported in the literature probably, in part, due to the ethnic differences in body composition. BIVA and PA permit an assessment without the need to know body weight and the prediction error of conventional impedance formulas.