Planejamento e desenvolvimento de novos compostos com atividade frente a Leishmania spp: Síntese, avaliação da atividade e toxicidade de derivados 5-nitro-2-furfurilidênicos e estudos de relações estrutura-atividade / Design of new compounds against Leishmania spp: synthesis, biological evaluation and toxicity of 5-nitro-2-furfurylidene derivates and structure-activity relationship studies

A leishmaniose é uma zoonose de ampla distribuição mundial, causada pelos parasitas tripanossomatídeos do gênero Leishmania. Infelizmente, o arsenal terapêutico disponível é precário, mas vê-se crescente o interesse científico pela busca do potencial de derivados nitroheterocíclicos como alternativas terapêuticas. Nesse contexto, este trabalho analisou o potencial de derivados 5-nitro-2-furfurilidênicos contra diferentes cepas de Leishmania, assim como investigou um possível modo de ação para esta classe de nitrocompostos. Para tal, a quimioteca foi sintetizada de acordo com publicações prévias do grupo. O potencial de inibição de crescimento das culturas de promastigotas de L. (L.) infantum (Linf) e L. (L.) major (Lmaj) foi determinado, utilizando miltefosina (MILT) (Linf - IC50: 8,28±0,33 µM), anfotericina B (AMB) (Linf - IC50: 0,02±0,002 µM) e nifurtimox (NFX) (Lmaj - IC50: 3,5±0,09 µM) como referência. A maioria dos compostos apresentaram maior potencial que as referênias, destacando o composto 40 (Linf - IC50: 0,2±0,019 µM/ Lmaj - IC50: 0,087 ± 0,001 µM) como mais eficaz. Contra as formas amastigotas intracelulares, para Linf os compostos 40, 13 e 15 foram mais eficazes em reduzir a carga parasitária dos macrófagos infectados que fármacos de referência. Para Lmajor, o composto 40 (IC50: 0,006 ± 0,0003 µM) foi mais ativo que o NFX (IC50: 2,15 ± 0,01 µM). Também foi determinada a atividade da quimioteca frente a enzima nitrorredutase (NTR1), utilizando cepas de T. brucei superexpressantes de NTR1, e os compostos analisados foram até 18 vezes mais eficazes que à cepa wild-type. Ademais, a partir da análise exploratória de dados por análise de componentes principais (PCA) e de grupamentos hierárquicos (HCA), foi reconhecida a influência das propriedades relacionadas com o equilíbrio hidrófilo-lipófilo e da natureza estérica/geométrica das moléculas para atividade anti-Leishmania

Leishmaniasis is a worldwide zoonosis caused by trypanosomatid parasites of the genus Leishmania. Unfortunately, the available therapeutic arsenal is precarious, but there is growing scientific interest in searching the potential of nitroheterocyclic derivatives as therapeutic alternatives. In this context, this work analyzed the potential of 5-nitro-2-furfurylidene derivatives against different Leishmania strains, as well as investigated the potential mode of action for this nitro compounds class. To this end, the chemolibrary was synthesized according to our group's previous publications. The growth inhibitory potential potential for promastigote cultures of L. (L.) infantum (Linf) and L. (L.) major (Lmaj) was determined using miltefosine (MILT) (Linf - IC50: 8.28±0.33 µM), amphotericin B (AMB) (Linf - IC50: 0.02±0.002 µM) and nifurtimox (NFX) (Lmaj - IC50: 3.5±0.09 µM) as reference. Most of the compounds were more potent than the references, highlighting compound 40 (Linf - IC50: 0.2±0.019 µM/ Lmaj - IC50: 0.087 ± 0.001 µM) as the most effective. Against intracellular amastigote, for Linf, compounds 40, 13 and 15 were more effective in reducing the parasite load of infected macrophages than reference drugs. For Lmajor, compound 40 (IC50: 0.006 ± 0.0003 µM) was more active than NFX (IC50: 2.15 ± 0.01 µM). The activity against nitroreductase (NTR1) enzyme was determined using overexpressing NTR1 mutant T. brucei strains, and the analyzed compounds were up to 18 times more effective than wild-type. Furthermore, exploratory data analysis using principal component analysis (PCA) and hierarchical clustering (HCA) methods were used. The influence of properties related to the hydrophiliclipophilic balance and the steric/geometric nature of the molecules was associated with the anti-Leishmanial activity

Diagnostic Potential of FT-IR Fingerprinting in Botanical Origin Evaluation of Laurus nobilis L. Essential Oil is Supported by GC-FID-MS Data.

The last years, non-targeted fingerprinting by Fourier-transform infrared (FT-IR) spectroscopy has gained popularity as an alternative to classical gas chromatography (GC)-based methods because it may allow fast, green, non-destructive and cost-effective assessment of quality of essential oils (EOs) from single plant species. As the relevant studies for Laurus nobilis L. (bay laurel) EO are limited, the present one aimed at exploring the diagnostic potential of FT-IR fingerprinting for the identification of its botanical integrity. A reference spectroscopic dataset of 97 bay laurel EOs containing meaningful information about the intra-species variation was developed via principal component analysis (PCA). This dataset was used to train a one-class model via soft independent modelling class analogy (SIMCA). The model was challenged against commercial bay laurel and non-bay laurel EOs of non-traceable production history. Overall, the diagnostic importance of spectral bands at 3060, 1380-1360, 1150 and 1138 cm-1 was assessed using GC-FID-MS data. The findings support the introduction of FT-IR as a green analytical technique in the quality control of these often mislabeled and/or adulterated precious products. Continuous evaluation of the model performance against newly acquired authentic EOs from all producing regions is needed to ensure validity over time.

Group ICA for identifying biomarkers in schizophrenia: 'Adaptive' networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression.

Brain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio-temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG-ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject-specific networks using these two methods separately from resting-state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs). We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross-validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG-ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45 ±â€¯8.9%, sensitivity 0.74 ±â€¯0.11, specificity 0.79 ±â€¯0.11) than STR (accuracy 67.45 ±â€¯8.13%, sensitivity 0.65 ±â€¯0.11, specificity 0.71 ±â€¯0.11). Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG-ICA are more sensitive to group differences, and GIG-ICA is promising for identifying image-derived biomarkers of brain disease.

Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data.

Principal component analysis (PCA) has been used to study the pathogenesis of diseases. To enhance the interpretability of classical PCA, various improved PCA methods have been proposed to date. Among these, a typical method is the so-called sparse PCA, which focuses on seeking sparse loadings. However, the performance of these methods is still far from satisfactory due to their limitation of using unsupervised learning methods; moreover, the class ambiguity within the sample is high. To overcome this problem, this paper developed a new PCA method, which is named the supervised discriminative sparse PCA (SDSPCA). The main innovation of this method is the incorporation of discriminative information and sparsity into the PCA model. Specifically, in contrast to the traditional sparse PCA, which imposes sparsity on the loadings, here, sparse components are obtained to represent the data. Furthermore, via the linear transformation, the sparse components approximate the given label information. On the one hand, sparse components improve interpretability over the traditional PCA, while on the other hand, they are have discriminative abilities suitable for classification purposes. A simple algorithm is developed, and its convergence proof is provided. SDSPCA has been applied to the common-characteristic gene selection and tumor classification on multiview biological data. The sparsity and classification performance of SDSPCA are empirically verified via abundant, reasonable, and effective experiments, and the obtained results demonstrate that SDSPCA outperforms other state-of-the-art methods.

Prevalence and factors associated with recent intimate partner violence and relationships between disability and depression in post-partum women in one clinic in eThekwini Municipality, South Africa.

Intimate partner violence (IPV) experienced by pregnant and post-partum women has negative health effects for women, as well as the foetus, and the new-born child. In this study we sought to assess the prevalence and factors associated with recent IPV amongst post-partum women in one clinic in eThekwini Municipality, South Africa, and explore the relationship between IPV, depression and functional limitations/disabilities. Past 12 month IPV-victimisation was 10.55%. Logistic regression modelled relationships between IPV, functional limitations, depressive symptoms, socio-economic measures, and sexual relationship power. In logistic regression models, overall severity of functional limitations were not associated with IPV-victimisation when treated as a continuous overall score. In this model relationship power (aOR0.22, p = 0.001) and depressive symptoms (aOR1.26, p = 0.001) were significant. When the different functional limitations were separated out in a second model, significant factors were relationship power (aOR0.20, p = 0.001), depressive symptoms (aOR1.20, p = 0.011) and mobility limitations (aOR2.96, p = 0.024). The study emphasises that not all functional limitations are associated with IPV-experience, that depression and disability while overlapping can also be considered different drivers of vulnerability, and that women's experience of IPV is not dependent on pregnancy specific factors, but rather wider social factors that all women experience.

Blood profiling of proteins and steroids during weight maintenance with manipulation of dietary protein level and glycaemic index.

Weight regain after weight loss is common. In the Diogenes dietary intervention study, a high-protein and low-glycaemic index (GI) diet improved weight maintenance. The objective of the present study was to identify (1) blood profiles associated with continued weight loss and weight regain (2) blood biomarkers of dietary protein and GI levels during the weight-maintenance phase. Blood samples were collected at baseline, after 8 weeks of low-energy diet-induced weight loss and after a 6-month dietary intervention period from female continued weight losers (n 48) and weight regainers (n 48), evenly selected from four dietary groups that varied in protein and GI levels. The blood concentrations of twenty-nine proteins and three steroid hormones were measured. The changes in analytes during weight maintenance largely correlated negatively with the changes during weight loss, with some differences between continued weight losers and weight regainers. Increases in leptin (LEP) and C-reactive protein (CRP) were significantly associated with weight regain (P < 0·001 and P = 0·005, respectively), and these relationships were influenced by the diet. Consuming a high-protein and high-GI diet dissociated the positive relationship between the change in LEP concentration and weight regain. CRP increased during the weight-maintenance period only in weight regainers with a high-protein diet (P < 0·001). In addition, testosterone, luteinising hormone, angiotensinogen, plasminogen activator inhibitor-1, resistin, retinol-binding protein 4, insulin, glucagon, haptoglobin and growth hormone were also affected by the dietary intervention. The blood profile reflects not only the weight change during the maintenance period, but also the macronutrient composition of the dietary intervention, especially the protein level.

Feature extraction and selection from volatile compounds for analytical classification of Chinese red wines from different varieties.

This work was undertaken to evaluate whether it is possible to determine the variety of a Chinese wine on the basis of its volatile compounds, and to investigate if discrimination models could be developed with the experimental wines that could be used for the commercial ones. A headspace solid-phase microextraction gas chromatographic (HS-SPME-GC) procedure was used to determine the volatile compounds and a blind analysis based on Ac/Ais (peak area of volatile compound/peak area of internal standard) was carried out for statistical purposes. One way analysis of variance (ANOVA), principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were used to process data and to develop discriminant models. Only 11 peaks enabled to differentiate and classify the experimental wines. SLDA allowed 100% recognition ability for three grape varieties, 100% prediction ability for Cabernet Sauvignon and Cabernet Gernischt wines, but only 92.31% for Merlot wines. A more valid and robust way was to use the PCA scores to do the discriminant analysis. When we performed SLDA this way, 100% recognition ability and 100% prediction ability were obtained. At last, 11 peaks which selected by SLDA from raw analysis set had been identified. When we demonstrated the models using commercial wines, the models showed 100% recognition ability for the wines collected directly from winery and without ageing, but only 65% for the others. Therefore, the varietal factor was currently discredited as a differentiating parameter for commercial wines in China. Nevertheless, this method could be applied as a screening tool and as a complement to other methods for grape base liquors which do not need ageing and blending procedures.