Automatic assignment of absolute configuration from 1D NMR data

Opposite enantiomers exhibit different NMR properties in the presence of an external common chiral element, and a chiral molecule exhibits different NMR properties in the presence of external enantiomeric chiral elements. Automatic prediction of such differences, and comparison with experimental values, leads to the assignment of the absolute configuration. Here two cases are reported, one using a dataset of 80 chiral secondary alcohols esterified with (R)-MTPA and the corresponding 1H NMR chemical shifts and the other with 94 13C NMR chemical shifts of chiral secondary alcohols in two enantiomeric chiral solvents. For the first application, counterpropagation neural networks were trained to predict the sign of the difference between chemical shifts of opposite stereoisomers. The neural networks were trained to process the chirality code of the alcohol as the input, and to give the NMR property as the output. In the second application, similar neural networks were employed, but the property to predict was the difference of chemical shifts in the two enantiomeric solvents. For independent test sets of 20 objects, 100% correct predictions were obtained in both applications concerning the sign of the chemical shifts differences. Additionally, with the second dataset, the difference of chemical shifts in the two enantiomeric solvents was quantitatively predicted, yielding r2 0.936 for the test set between the predicted and experimental values.

Ressonância magnética funcional: mapeamento do córtex motor através da técnica BOLD

A ressonância magnética funcional (RMf) é hoje uma ferramenta fundamental na investigação funcional do cérebro humano, quer em indivíduos saudáveis quer em pacientes com patologias diversas. É uma técnica complexa que necessita de uma aplicação cuidada e rigorosa, e uma compreensão dos mecanismos biofísicos a ela subjacentes, de modo a serem obtidos resultados fiáveis e com melhor aceitação clínica. O efeito BOLD (Blood Oxygenation Level Dependent) é o método mais utilizado para medir e estudar a actividade cerebral e baseia-se nas alterações das propriedades magnéticas da molécula hemoglobina. Com este Projecto propomo-nos optimizar um protocolo de RMf realizada com o efeito BOLD, em voluntários saudáveis, de modo a que este possa ser aplicado em futuros estudos de pacientes com patologias. ABSTRACT - Nowadays functional magnetic resonance imaging (fMRI) is a fundamental tool for the research of human brain function of healthy subjects or patients with several pathologies. It is a complex technique that requires a careful and rigorous application, and an understanding of its biophysical mechanisms, so that reliable results can be obtained with better clinical acceptance. The BOLD effect (Blood Oxygenation Level Dependent) is the most widely used method to measure and study the brain activity and its based on changes in magnetic properties of the hemoglobin molecule. The aim of this project was to optimize a BOLD fMRI protocol on healthy subjects, so it can be applied in future studies of patients with pathologies.

Molecular imprinted nanoelectrodes for ultra sensitive detection of ovarian cancer marker

The relentless discovery of cancer biomarkers demands improved methods for their detection. In this work, we developed protein imprinted polymer on three-dimensional gold nanoelectrode ensemble (GNEE) to detect epithelial ovarian cancer antigen-125 (CA 125), a protein biomarker associated with ovarian cancer. CA 125 is the standard tumor marker used to follow women during or after treatment for epithelial ovarian cancer. The template protein CA 125 was initially incorporated into the thin-film coating and, upon extraction of protein from the accessible surfaces on the thin film, imprints for CA 125 were formed. The fabrication and analysis of the CA 125 imprinted GNEE was done by using cyclic voltammetry (CV), differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS) techniques. The surfaces of the very thin, protein imprinted sites on GNEE are utilized for immunospecific capture of CA 125 molecules, and the mass of bound on the electrode surface can be detected as a reduction in the faradic current from the redox marker. Under optimal conditions, the developed sensor showed good increments at the studied concentration range of 0.5–400 U mL−1. The lowest detection limit was found to be 0.5 U mL−1. Spiked human blood serum and unknown real serum samples were analyzed. The presence of non-specific proteins in the serum did not significantly affect the sensitivity of our assay. Molecular imprinting using synthetic polymers and nanomaterials provides an alternative approach to the trace detection of biomarker proteins.

Effects of antihypertensive drugs in the differentiation and activation of human bone cells

O osso é um tecido metabolicamente ativo e a sua remodelação é importante para regular e manter a massa óssea. Esse processo envolve a reabsorção do material ósseo por ação dos osteoclastos e a síntese de novo material ósseo mediado pelos osteoblastos. Vários estudos têm sugerido que a pressão arterial elevada está associada a alterações no metabolismo do cálcio, o que leva ao aumento da perda de cálcio e da remoção de cálcio do osso. Embora as alterações no metabolismo ósseo sejam um efeito adverso associado a alguns fármacos antihipertensores, o conhecimento em relação a este efeito terapêutico ligado com os bloqueadores de canais de cálcio é ainda muito escasso. Uma vez que os possíveis efeitos no osso podem ser atribuídos à ação antihipertensiva dessas moléculas, ou através de um efeito direto nas atividades metabólicas ósseas, torna-se necessário esclarecer este assunto. Devido ao facto de que as alterações no metabolismo ósseo são um efeito adverso associado a alguns fármacos antihipertensores, o objetivo deste trabalho é avaliar o efeito que os bloqueadores dos canais de cálcio exercem sobre as células ósseas humanas, nomeadamente osteoclastos, osteoblastos e co-culturas de ambos os tipos celulares. Verificou-se que os efeitos dos fármacos antihipertensores variaram consoante o fármaco testado e o sistema de cultura usado. Alguns fármacos revelaram a capacidade de estimular a osteoclastogénese e a osteoblastogénese em concentrações baixas. Independentemente da identidade do fármaco, concentrações elevadas revelaram ser prejudiciais para a resposta das células ósseas. Os mecanismos intracelulares através dos quais os efeitos foram exercidos foram igualmente afetados de forma diferencial pelos diferentes fármacos. Em resumo, este trabalho demonstrou que os bloqueadores dos canais de cálcio utilizados possuem a capacidade de afetar direta- e indiretamente a resposta de células ósseas humanas, cultivadas isoladamente ou co-cultivadas. Este tipo de informação é crucial para compreender e prevenir os potenciais efeitos destes fármacos no tecido ósseo, e também para adequar e eventualmente melhorar a terapêutica antihipertensora de cada paciente.

Solution enthalpies of 1,4-dioxane: Study of solvent effects through quantitative structure-property relationships

Solution enthalpies of 1,4-dioxane have been obtained in 15 protic and aprotic solvents at 298.15 K. Breaking the overall process through the use of Solomonov's methodology the cavity term was calculated and interaction enthalpies (Delta H-int) were determined. Main factors involved in the interaction enthalpy have been identified and quantified using a QSPR approach based on the TAKA model equation. The relevant descriptors were found to be pi* and beta, which showed, respectively, exothermic and endothermic contributions. The magnitude of pi* coefficient points toward non-specific solute-solvent interactions playing a major role in the solution process. The positive value of the beta coefficient reflects the endothermic character of the solvents' hydrogen bond acceptor (HBA) basicity contribution, indicating that solvent molecules engaged in hydrogen bonding preferentially interact with each other rather than with 1,4-dioxane. (C) 2013 Elsevier B.V. All rights reserved.

Comparative study of amine solutions used in CO2 absorption/desorption cycles

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Química

Particle agglomeration study in rf silane plasmas: In situ study by polarization-sensitive laser light scattering

To determine self-consistently the time evolution of particle size and their number density in situ multi-angle polarization-sensitive laser light scattering was used. Cross-polarization intensities (incident and scattered light intensities with opposite polarization) measured at 135 degrees and ex situ transmission electronic microscopy analysis demonstrate the existence of nonspherical agglomerates during the early phase of agglomeration. Later in the particle time development both techniques reveal spherical particles again. The presence of strong cross-polarization intensities is accompanied by low-frequency instabilities detected on the scattered light intensities and plasma emission. It is found that the particle radius and particle number density during the agglomeration phase can be well described by the Brownian free molecule coagulation model. Application of this neutral particle coagulation model is justified by calculation of the particle charge whereby it is shown that particles of a few tens of nanometer can be considered as neutral under our experimental conditions. The measured particle dispersion can be well described by a Brownian free molecule coagulation model including a log-normal particle size distribution. (C) 1996 American Institute of Physics.

Synthesis and properties of Co-doped titanate nanotubes and their optical sensitization with methylene blue

Agência Financiadora - Fundação para a Ciência e Tecnologia - PTDC/CTM NAN/113021/2009

Selective recognition in potentiometric transduction of amoxicillin by molecularly imprinted materials

The indiscriminate use of antibiotics in foodproducing animals has received increasing attention as a contributory factor in the international emergence of antibiotic- resistant bacteria (Woodward in Pesticide, veterinary and other residues in food, CRC Press, Boca Raton, 2004). Numerous analytical methods for quantifying antibacterial residues in edible animal products have been developed over years (Woodward in Pesticide, veterinary and other residues in food, CRC Press, Boca Raton, 2004; Botsoglou and Fletouris in Handbook of food analysis, residues and other food component analysis, Marcel Dekker, Ghent, 2004). Being Amoxicillin (AMOX) one of those critical veterinary drugs, efforts have been made to develop simple and expeditious methods for its control in food samples. In literature, only one AMOX-selective electrode has been reported so far. In that work, phosphotungstate:amoxycillinium ion exchanger was used as electroactive material (Shoukry et al. in Electroanalysis 6:914–917, 1994). Designing new materials based on molecularly imprinted polymers (MIPs) which are complementary to the size and charge of AMOX could lead to very selective interactions, thus enhancing the selectivity of the sensing unit. AMOXselective electrodes used imprinted polymers as electroactive materials having AMOX as target molecule to design a biomimetic imprinted cavity. Poly(vinyl chloride), sensors of methacrylic acid displayed Nernstian slopes (60.7 mV/decade) and low detection limits (2.9×10-5 mol/L). The potentiometric responses were not affected by pH within 4–5 and showed good selectivity. The electrodes were applied successfully to the analysis of real samples.

Accounting for the dissociating properties of organic chemicals in LCIA: an uncertainty analysis applied to micropollutants in the assessment of freshwater ecotoxicity

In life cycle impact assessment (LCIA) models, the sorption of the ionic fraction of dissociating organic chemicals is not adequately modeled because conventional non-polar partitioning models are applied. Therefore, high uncertainties are expected when modeling the mobility, as well as the bioavailability for uptake by exposed biota and degradation, of dissociating organic chemicals. Alternative regressions that account for the ionized fraction of a molecule to estimate fate parameters were applied to the USEtox model. The most sensitive model parameters in the estimation of ecotoxicological characterization factors (CFs) of micropollutants were evaluated by Monte Carlo analysis in both the default USEtox model and the alternative approach. Negligible differences of CFs values and 95% confidence limits between the two approaches were estimated for direct emissions to the freshwater compartment; however the default USEtox model overestimates CFs and the 95% confidence limits of basic compounds up to three orders and four orders of magnitude, respectively, relatively to the alternative approach for emissions to the agricultural soil compartment. For three emission scenarios, LCIA results show that the default USEtox model overestimates freshwater ecotoxicity impacts for the emission scenarios to agricultural soil by one order of magnitude, and larger confidence limits were estimated, relatively to the alternative approach.

Avaliação por fMRI do córtex visual, motor e auditivo através de estimulação sensoriomotora e sonora em desportistas invisuais e desportistas sem deficiência visual

Mestrado em Radiações Aplicadas às Tecnologias da Saúde - Ramo de especialização: Imagem por Ressonância Magnética

Novos compostos orgânicos reticuláveis para aplicações em células fotovoltaicas

Trabalho Final de Mestrado para obtenção do grau de mestre em Engenharia Química e Biológica

Electrochemical oxidation of tamoxifen revisited

Tamoxifen is a selective estrogen receptor modulator that is used as an adjuvant and/or chemotherapeutic agent for the treatment of all stages of hormone-dependent breast cancer. Currently there is a deep interest in the study of tamoxifen biotransformation and identification of metabolites since they can significantly contribute to the overall pharmacological or adverse effects of the drug. Accordingly, the study of the electrochemical behavior of tamoxifen in aqueous solution is reported. To clarify the occurring oxidative process and to assess the influence of the functional groups on the oxidation mechanism, the voltammetric assessment was extended to the study of tamoxifen’s analogues (E)-tamoxifen and dihydrotamoxifen, and to its main phase I oxidative metabolite, N-desmethyl tamoxifen. The data found shows that the oxidative processes occurring in tamoxifen are essentially related with the two chemical moieties present in the molecule: the substituted aromatic nucleus and the tertiary amine group. Moreover, the results obtained suggest that the ethylenic linkage is not critical for tamoxifen’s oxidation although it could play an important role in the course of the oxidation process. These results could contribute to highlight some remaining questions regarding tamoxifen’s metabolic behavior and to the development of new analytical strategies, based on electrochemical approaches.

Hydrogen evolution and consumption in AOT–isooctane reverse micelles by Desulfovibrio gigas hydrogenase

The enzyme hydrogenase isolated from the sulphate reducing anaerobic bacterium Desulfovibrio gigas was encapsulated in reverse micelles of AOT–water–isooctane. The enzyme ability to consume molecular hydrogen was studied as a function of the micelle size (given by Wo = [H2O]/[organic solvent]). A peak of catalytic activity was obtained for Wo = 18, a micelle size theoretically fitting the heterodimeric hydrogenase molecule. At this Wo value, the recorded catalytic activity was slightly higher than in a buffer system(Kcat = 169.43 s−1 against the buffer value of 151 s−1). The optimal buffer used to encapsulate the enzyme was found to be imidazole 50 mM, pH 9.0. The molecular hydrogen production activity was also tested in this reverse micelle medium.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.