Medicamentos inadequados para idosos na estratégia da saúde da família

The ageing process can change the pharmacodynamics and pharmacokinetics parameters. Therefore, some medications are considered potentially inappropriate (PIM) for the elderly people, since they can increase the likelihood of occurrence of adverse drug events. The objectives are to estimate the frequency of use of PIM in the elderly people, with potentially hazardous drug interactions (PHDI) and to evaluate the impact of pharmaceutical intervention (PI) for the prescription of safer therapeutic alternatives. A cross-sectional study was performed in a Health Family Strategy (region of Araraquara, SP), between January and February/2012. The medical records of patients aged ≥60 years, that use at least one drug, were consulted for identification of PIM, according to the Beers criteria. The MPI identified were classified considering the Anatomical Therapeutic Chemical Classification System (ATC) and the essentiality of the drug (safety, effectiveness, quality and cost parameters) The inclusion criteria were met by 358 elderly, being that 93 of them (26%) had taken at least one PIM. Of the 114 different drugs prescribed for elderly, ten were classified as PIM, of which four of them act on the central nervous system, four on cardiovascular system and two on the digestive tract. Seven MPI are essential medicines, belonging to national list of essential drugs (RENAME-2010). Fourteen drug interactions were identified, of which two are PHDI (fluoxetine/amitriptyline and digoxin/hydrochlorothiazide).After the PI, there was no change in medical prescriptions of patients with PIM use or with DI. Medical prescriptions of elderly attended in the Health Family Strategy show pharmacotherapeutic safety problems, of which may be responsible for health hazardous for this age group. Although the intervention carried out by letter had been ineffective for the adherence of doctors in prescribing safe alternatives, wide dissemination of the lists that contain PIM and PHDI is need, as well as the inclusion of safer equivalents in RENAME, in order to contribute for rational use of drugs.

Antibiotic use in adult outpatients in Switzerland in relation to regions, seasonality and point of care tests

The use of antibiotics is highest in primary care and directly associated with antibiotic resistance in the community. We assessed regional variations in antibiotic use in primary care in Switzerland and explored prescription patterns in relation to the use of point of care tests. Defined daily doses of antibiotics per 1000 inhabitants (DDD(1000pd) ) were calculated for the year 2007 from reimbursement data of the largest Swiss health insurer, based on the anatomic therapeutic chemical classification and the DDD methodology recommended by WHO. We present ecological associations by use of descriptive and regression analysis. We analysed data from 1 067 934 adults, representing 17.1% of the Swiss population. The rate of outpatient antibiotic prescriptions in the entire population was 8.5 DDD(1000pd) , and varied between 7.28 and 11.33 DDD(1000pd) for northwest Switzerland and the Lake Geneva region. DDD(1000pd) for the three most prescribed antibiotics were 2.90 for amoxicillin and amoxicillin-clavulanate, 1.77 for fluoroquinolones, and 1.34 for macrolides. Regions with higher DDD(1000pd) showed higher seasonal variability in antibiotic use and lower use of all point of care tests. In regression analysis for each class of antibiotics, the use of any point of care test was consistently associated with fewer antibiotic prescriptions. Prescription rates of primary care physicians showed variations between Swiss regions and were lower in northwest Switzerland and in physicians using point of care tests. Ecological studies are prone to bias and whether point of care tests reduce antibiotic use has to be investigated in pragmatic primary care trials.

Traditional knowledge of medicinal plants in the Serra de Mariola Natural Park, South-eastern Spain

The present study aims to inventory and analyse the ethnobotanical knowledge about medicinal plants in the Serra de Mariola Natural Park. In respect to traditional uses, 93 species reported by local informants were therapeutic, 27 food, 4 natural dyes and 13 handcrafts. We developed a methodology that allowed the location of individuals or vegetation communities with a specific popular use. We prepared a geographic information system (GIS) that included gender, family, scientific nomenclature and common names in Spanish and Catalan for each species. We also made a classification of 39 medicinal uses from ATC (Anatomical, Therapeutic, Chemical classification system). Labiatae (n=19), Compositae (n=9) and Leguminosae (n=6) were the families most represented among the plants used to different purposes in humans. Species with the most elevated cultural importance index (CI) values were Thymus vulgaris (CI=1.431), Rosmarinus officinalis (CI=1.415), Eryngium campestre (CI=1.325), Verbascum sinuatum (CI=1.106) and Sideritis angustifolia (CI=1.041). Thus, the collected plants with more therapeutic uses were: Lippia triphylla (12), Thymus vulgaris and Allium roseum (9) and Erygium campestre (8). The most repeated ATC uses were: G04 (urological use), D03 (treatment of wounds and ulcers) and R02 (throat diseases). These results were in a geographic map where each point represented an individual of any species. A database was created with the corresponding therapeutic uses. This application is useful for the identification of individuals and the selection of species for specific medicinal properties. In the end, knowledge of these useful plants may be interesting to revive the local economy and in some cases promote their cultivation.

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.

Sentiment classification for early detection of health alerts in the chemical textile domain

In the chemical textile domain experts have to analyse chemical components and substances that might be harmful for their usage in clothing and textiles. Part of this analysis is performed searching opinions and reports people have expressed concerning these products in the Social Web. However, this type of information on the Internet is not as frequent for this domain as for others, so its detection and classification is difficult and time-consuming. Consequently, problems associated to the use of chemical substances in textiles may not be detected early enough, and could lead to health problems, such as allergies or burns. In this paper, we propose a framework able to detect, retrieve, and classify subjective sentences related to the chemical textile domain, that could be integrated into a wider health surveillance system. We also describe the creation of several datasets with opinions from this domain, the experiments performed using machine learning techniques and different lexical resources such as WordNet, and the evaluation focusing on the sentiment classification, and complaint detection (i.e., negativity). Despite the challenges involved in this domain, our approach obtains promising results with an F-score of 65% for polarity classification and 82% for complaint detection.

Chemical characterization and oxidative stability of the oils from three morphotypes of Mauritia flexuosa L.f, from the Peruvian Amazon

Three morphotypes of aguaje Mauritia flexuosa were tested, classified by the color of their mesocarpium: ""color"", ""shambo"" and ""amarillo"", collected from different areas near the city of Iquitos, Peru. Also, physical-chemical analyses of the mesocarpium were performed, such as the characterization of fatty acids by gas chromatography, determination of beta- carotene y alpha tocopherol by high efficiency liquid chromatography system in normal and reverse phase and the determination of oxidation induction time in the Rancimat apparatus. Proximate, mineral and fatty acid analyses were done on the seeds. The aguaje mesocarpium is rich in oleic oil (""amarillo"": 75.63% +/- 0.31), (beta-carotene (""amarillo"": 342.42ug/g 0.71) and alpha- tocopherol (""color"": 685.81mg/L +/- 1.04), plus the morphotype ""color"" has a superior oxidation induction time compared to other morphotypes with 6.91 +/- 0.01. The aguaje seed contains significant amounts of (06 (linoleic oil) in ""shambo"" with 36.04 +/- 0.09%. The results indicate that these oils, regardless their classification, contain important chemical compounds that give them a special nutritive value.

Chemical abundances of 11 bulge stars from high-resolution, near-IR spectra

Context. It is debated whether the Milky Way bulge has characteristics more similar to those of a classical bulge than those of a pseudobulge. Detailed abundance studies of bulge stars are important when investigating the origin, history, and classification of the bulge. These studies provide constraints on the star-formation history, initial mass function, and differences between stellar populations. Not many similar studies have been completed because of the large distance and high variable visual extinction along the line-of-sight towards the bulge. Therefore, near-IR investigations can provide superior results. Aims. To investigate the origin of the bulge and study its chemical abundances determined from near-IR spectra for bulge giants that have already been investigated with optical spectra. The optical spectra also provide the stellar parameters that are very important to the present study. In particular, the important CNO elements are determined more accurately in the near-IR. Oxygen and other alpha elements are important for investigating the star-formation history. The C and N abundances are important for determining the evolutionary stage of the giants and the origin of C in the bulge. Methods. High-resolution, near-infrared spectra in the H band were recorded using the CRIRES spectrometer mounted on the Very Large Telescope. The CNO abundances are determined from the numerous molecular lines in the wavelength range observed. Abundances of the alpha elements Si, S, and Ti are also determined from the near-IR spectra. Results. The abundance ratios [O/Fe], [Si/Fe], and [S/Fe] are enhanced to metallicities of at least [Fe/H] = -0.3, after which they decline. This suggests that the Milky Way bulge experienced a rapid and early burst of star formation similar to that of a classical bulge. However, a similarity between the bulge trend and the trend of the local thick disk seems to be present. This similarity suggests that the bulge could have had a pseudobulge origin. The C and N abundances suggest that our giants are first-ascent red-giants or clump stars, and that the measured oxygen abundances are those with which the stars were born. Our [C/Fe] trend does not show any increase with [Fe/H], which is expected if W-R stars contributed substantially to the C abundances. No ""cosmic scatter"" can be traced around our observed abundance trends: the measured scatter is expected, given the observational uncertainties.

Chemical composition of mixed construction and demolition recycled aggregates from the State of Sao Paulo

Construction and Demolition Waste (CDW) represents. about 50% of the total Brazilian municipal solid waste: thus, recycling represents huge benefits both in environmental and economic perspectives. Herein, the chemical characterization results of three samples from two different recycling plants from the State of Sao Paulo is prevented. The results demonstrated that the visual classification into grey and red is not related to the chemical composition but mostly to the grain size fraction. The chemical composition of the CDW varies according to the content of cement paste, natural aggregates (quartz sand or granite), red ceramic and clay. Furthermore, the production of recycled concrete aggregates requires two crushing stages to meet the technical standards. The sand fraction (below 4.8 mm) presents high grades of SiO(2), which indicates the liberation of cement paste to fines (< 0.15 mm). The fines have a great potential to be used in the cement industry.

Prediction of Crude Oil Properties and Chemical Composition by Means of Steady-State and Time-Resolved Fluorescence

Steady-state and time-resolved fluorescence measurements are reported for several crude oils and their saturates, aromatics, resins, and asphaltenes (SARA) fractions (saturates, aromatics and resins), isolated from maltene after pentane precipitation of the asphaltenes. There is a clear relationship between the American Petroleum Institute (API) grade of the crude oils and their fluorescence emission intensity and maxima. Dilution of the crude oil samples with cyclohexane results in a significant increase of emission intensity and a blue shift, which is a clear indication of the presence of energy-transfer processes between the emissive chromophores present in the crude oil. Both the fluorescence spectra and the mean fluorescence lifetimes of the three SARA fractions and their mixtures indicate that the aromatics and resins are the major contributors to the emission of crude oils. Total synchronous fluorescence scan (TSFS) spectral maps are preferable to steady-state fluorescence spectra for discriminating between the fractions, making TSFS maps a particularly interesting choice for the development of fluorescence-based methods for the characterization and classification of crude oils. More detailed studies, using a much wider range of excitation and emission wavelengths, are necessary to determine the utility of time-resolved fluorescence (TRF) data for this purpose. Preliminary models constructed using TSFS spectra from 21 crude oil samples show a very good correlation (R(2) > 0.88) between the calculated and measured values of API and the SARA fraction concentrations. The use of models based on a fast fluorescence measurement may thus be an alternative to tedious and time-consuming chemical analysis in refineries.

Chemical-mineralogical characterization of C&D waste recycled aggregates from Sao Paulo, Brazil

This study presents a methodology for the characterization of construction and demolition (C&D) waste recycled aggregates based on a combination of analytical techniques (X-ray fluorescence (XRF), soluble ions, semi-quantitative X-ray diffraction (XRD), thermogravimetric analysis (TCA-DTG) and hydrochloric acid (HCl) selective dissolution). These combined analytical techniques allow for the estimation of the amount of cement paste, its most important hydrated and carbonated phases, as well as the amount of clay and micas. Details of the methodology are presented here and the results of three representative C&D samples taken from the Sao Paulo region in Brazil are discussed. Chemical compositions of mixed C&D aggregate samples have mostly been influenced by particle size rather than the visual classification of C&D into red or grey and geographical origin. The amount of measured soluble salts in C&D aggregates (0.15-25.4 mm) is lower than the usual limits for mortar and concrete production. The content of porous cement paste in the C&D aggregates is around 19.3% (w/w). However, this content is significantly lower than the 43% detected for the C&D powders (< 0.15 min). The clay content of the powders was also high, potentially resulting from soil intermixed with the C&D waste, as well as poorly burnt red ceramic. Since only about 50% of the measured CaO is combined with CO(2), the powders have potential use as raw materials for the cement industry. (C) 2008 Elsevier Ltd. All rights reserved.

Sweet Basil (Ocimum basilicum) Extracts Obtained by Supercritical Fluid Extraction (SFE): Global Yields, Chemical Composition, Antioxidant Activity, and Estimation of the Cost of Manufacturing

In this work, supercritical technology was used to obtain extracts from Ocimum basilicum (sweet basil) with CO(2) and the cosolvent H(2)O at 1, 10, and 20% (w/w). The raw material was obtained from hydroponic cultivation. The extract`s global yield isotherms, chemical compositions, antioxidant activity, and cost of manufacturing were determined. The extraction assays were done for pressures of 10 to 30 MPa at 303 to 323 K. The identification of the compounds present in the extracts was made by GC-MS and ESI-MS. The antioxidant activity of extracts was determined using the coupled reaction of beta-carotene and linolenic acid. At 1% of cosolvent, the largest global yield was obtained at 10 MPa and 303 K (2%, dry basis-d.b.); at 10% of cosolvent the largest global yield was obtained at 10 and 15 MPa (11%, d.b.), and at 20% of cosolvent the largest global yield was detected at 30 MPa and 303 K (24%, d.b.). The main components identified in the extracts were eugenol, germacrene-D, epi-alpha-cadinol, malic acid, tartaric acid, ramnose, caffeic acid, quinic acid, kaempferol, caffeoylquinic acid, and kaempferol 3-O-glucoside. Sweet basil extracts exhibited high antioxidant activity compared to beta-carotene. Three types of SFE extracts from sweet basil were produced, for which the estimated cost of manufacturing (class 5 type) varied from US$ 47.96 to US$ 1,049.58 per kilogram of dry extract.

Self-Organizing Maps as a New Tool for Classification of Plants at Lower Hierarchical Levels

A chemotaxonomic analysis is described of a database containing various types of compounds from the Heliantheae tribe (Asteraceae) using Self-Organizing Maps (SOM). The numbers of occurrences of 9 chemical classes in different taxa of the tribe were used as variables. The study shows that SOM applied to chemical data can contribute to differentiate genera, subtribes, and groups of subtribes (subtribe branches), as well as to tribal and subtribal classifications of Heliantheae, exhibiting a high hit percentage comparable to that of an expert performance, and in agreement with the previous tribe classification proposed by Stuessy.

Multilabeled classification approach to find a plaint source for terpenoids

Recently, we have built a classification model that is capable of assigning a given sesquiterpene lactone (STL) into exactly one tribe of the plant family Asteraceae from which the STL has been isolated. Although many plant species are able to biosynthesize a set of peculiar compounds, the occurrence of the same secondary metabolites in more than one tribe of Asteraceae is frequent. Building on our previous work, in this paper, we explore the possibility of assigning an STL to more than one tribe (class) simultaneously. When an object may belong to more than one class simultaneously, it is called multilabeled. In this work, we present a general overview of the techniques available to examine multilabeled data. The problem of evaluating the performance of a multilabeled classifier is discussed. Two particular multilabeled classification methods-cross-training with support vector machines (ct-SVM) and multilabeled k-nearest neighbors (M-L-kNN)were applied to the classification of the STLs into seven tribes from the plant family Asteraceae. The results are compared to a single-label classification and are analyzed from a chemotaxonomic point of view. The multilabeled approach allowed us to (1) model the reality as closely as possible, (2) improve our understanding of the relationship between the secondary metabolite profiles of different Asteraceae tribes, and (3) significantly decrease the number of plant sources to be considered for finding a certain STL. The presented classification models are useful for the targeted collection of plants with the objective of finding plant sources of natural compounds that are biologically active or possess other specific properties of interest.

Chemical analysis of 17th century Millefiori glasses excavated in the Monastery of Sta. Clara‐a‐Velha: comparison with Venetian and façon‐de‐Venise production

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Conservação e Restauro,Área de especialização Cerâmica e Vidro

Evaluation of mechanical soft-abrasive blasting and chemical cleaning methods on alkyd-paint graffiti made on carbonate stones

In the context of this dissertation several studies were developed resulting in submission and publication “Evaluation of mechanical soft-abrasive blasting and chemical cleaning methods on alkyd-paint graffiti made on calcareous stones” to Journal of Cultural Heritage. (http://dx.doi.org/10.101 /j.culher.2014.10.004)