Methods for the development and validation for determination of iron and zinc in human serum and hair samples by graphite furnace atomic absorption spectrometry (GF AAS) were performed. Solubilization was immediate by manual agitation in both samples with a 10 mL volume sample plus deionized water. Optimum pyrolysis and atomization temperatures were obtained by pyrolysis and atomization temperature curves in both matrices. For serum samples, the best temperatures were 1400 and 2500 °C (Fe) and 700 and 1600 °C (Zn), respectively. For hair samples, the best temperatures were 1000 and 2400 °C (Fe) and 800 and 1600 °C (Zn), respectively. Permanent modifiers and zirconium presented best the results for Zn in both matrices and for Fe in serum. Permanent modifier Nb was best for hair Zn. Serum and hair values were in agreement with the certified values for analytes and certified samples. The determined values for serum Fe and hair certified samples were 1.40 ± 0.2 and 114 ± 18 mg L-1, respectively. For Zn certified reference materials (CRMs), the certified samples values were 1720 ± 32 µg L-1 and 172 ± 5 mg kg-1. Simple, accurate and precise, this method represents a cost-effective detection protocol suitable for sample analysis for diagnosis of micronutrient malnutrition.

O objetivo deste trabalho foi desenvolver uma metodologia quimiométrica para descrever o perfil químico de amostras de cocaína apreendidas no estado de Minas Gerais (Brasil). A identificação de adulterantes e a quantificação do teor de cocaína foram feitas por cromatografia gasosa acoplada à espectrometria de massa (GC-MS). Espectros de 91 amostras foram obtidos por espectroscopia no infravermelho usando refletância total atenuada (ATR-FTIR), e utilizados na construção de um modelo de análise de componentes principais (PCA). A primeira componente principal (PC1) discriminou as amostras de maior pureza das mais diluídas/adulteradas, nas quais foram identificadas lidocaína, cafeína e benzocaína. PC2 discriminou a forma química da cocaína, cloridrato ou base. Além disso, dois modelos supervisionados discriminantes (mínimos quadrados parciais para análise discriminante, PLS-DA) foram desenvolvidos para classificar as amostras em função de sua diluição (abaixo e acima de 15% m/m) e de sua forma química, apresentando taxas de acerto que variaram entre 83 e 97%. Os modelos de classificação constituem uma ferramenta simples, rápida e não destrutiva, de grande valor para peritos forenses e investigadores criminais.

The aim of this article was to develop a chemometric methodology for determining the chemical profile of cocaine samples seized in Minas Gerais State, Brazil. The adulterant detection and the cocaine determination were performed by gas chromatography-mass spectrometry (GC-MS). Spectra of 91 samples were obtained by attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) and used to build an exploratory principal component analysis (PCA) model. The first principal component (PC1) discriminated samples of more purity from the more diluted/adulterated ones, which were characterized by the presence of lidocaine, caffeine and benzocaine. PC2 discriminated the two chemical forms of cocaine, hydrochloride and base. In addition, two supervised discriminant partial least-squares models (partial least-squares discriminant analysis, PLS-DA) were developed for classifying the samples according to dilution (above and below 15% m/m) and chemical form, with a rate of success that varied between 83 and 97%. The classification models constitute a simple, rapid and non-destructive tool, of great value for both forensic experts and criminal investigators.

This paper describes 2D-QSAR and 3D-QSAR studies against Candida albicans and Cryptococcus neofarmans for a set of 20 bisbenzamidines. In the studies of 2D-QSAR with C. albicans it was obtained a correlation between log MIC-1 and lipolo component-Z (r² = 0.68; Q² = 0.51). In the case of C. neofarmans a correlation between log MIC-1 and lipolo component-Z and of Balaban index (r² = 0.85; Q² = 0.6) was obtained. 3D-QSAR studies using CoMFA showed that the steric fields contributed more to the predicted activities for Candida albicans (94.9%) and Cryptococcus neofarmans (97.9%).

A propriedade antifúngica de extratos de oito plantas utilizadas na medicina tradicional brasileira foi testada contra cinco espécies de Candida, com relevância clínica, Cryptococcus neoformans e Sporothrix schenckii. Os resultados mostraram que todos os extratos exibiram atividade antifúngica contra pelo menos um dos microrganismos testados. O extrato etanólico das folhas de Schinus terebinthifolius apresentou potencial atividade antifúngica contra C. glabrata e S. schenckii. Na análise fitoquímica preliminar dos extratos de S. terebinthifolius observou-se a presença de compostos biologicamente ativos como, flavonóides, triterpenos, esteróides e taninos.

Antifungal properties of extracts from eight Brazilian plants traditionally used in popular Brazilian medicine were tested against five clinically relevant Candida species, Cryptococcus neoformans, and Sporothrix schenckii. Results demonstrate that almost all extracts exhibited antifungal activity, at least against one of the microorganisms tested. The ethanolic extract from the leaves of Schinus terebinthifolius exhibited potential antifungal activity against C. glabrata and S. schenckii. Preliminary phytochemical analysis of extract from S. terebinthifolius showed the presence of biologically active compounds, namely saponins, flavonoids, triterpenes, steroids and tannins.

Combinatorial chemistry has emerged as a tool to circumvent a major problem of pharmaceutical industries to discover new lead compounds. A rapid and massive evaluation of a myriad of newly synthesised compounds can be carried out. Combinatorial synthesis leads to high throughput screening en masse towards another myriad of biological targets. The design of a set of compounds based upon combinatorial chemistry may be envisaged by using of QSPR-SIMCA and QSAR-SIMCA as tools for classification purposes. This work deals with the definition and establishment of a spanned substituent space (SSS) that reduces the analogue numbers with no exclusion of global content. The chemical diversity may be set properly within a specified pharmacological field. This allows a better use of its potentiality without loosing information.