DOTS in primary care units in the city of Rio de Janeiro, Southeastern Brazil

OBJECTIVE: To describe the implantation and the effects of directly-observed treatment short course (DOTS) in primary health care units. METHODS: Interviews were held with the staff of nine municipal health care units (MHU) that provided DOTS in Rio de Janeiro City, Southeastern Brazil, in 2004-2005. A dataset with records of all tuberculosis treatments beginning in 2004 in all municipal health care units was collected. Bivariate analyses and a multinomial model were applied to identify associations between treatment outcomes and demographic and treatment process variables, including being in DOTS or self-administered therapy (SAT). RESULTS: From 4,598 tuberculosis cases treated in public health units administrated by the municipality, 1,118 (24.3%) were with DOTS and 3,480 (75.7%) with SAT. The odds of DOTS were higher among patients with age under 50 years, tuberculosis relapse and prior history of default or treatment failure. The odds of death were 52.0% higher among patients on DOTS as compared to SAT. DOTS modality including community health workers (CHWs) showed the highest treatment success rate. A reduction of 21.0% was observed in the odds of default (vs. cure) among patients on DOTS as compared to patients on SAT, and a reduction of 64.0% among patients on DOTS with CHWs as compared to those without CHWs. CONCLUSIONS: Patients with a "low compliance profile" were more likely to be included in DOTS. This strategy improves the quality of care provided to tuberculosis patients, although the proposed goals were not achieved.

Fasciola hepatica IN BOVINES IN BRAZIL: DATA AVAILABILITY AND SPATIAL DISTRIBUTION

Fasciolosis is a disease of importance for both veterinary and public health. For the first time, georeferenced prevalence data of Fasciola hepatica in bovines were collected and mapped for the Brazilian territory and data availability was discussed. Bovine fasciolosis in Brazil is monitored on a Federal, State and Municipal level, and to improve monitoring it is essential to combine the data collected on these three levels into one dataset. Data were collected for 1032 municipalities where livers were condemned by the Federal Inspection Service (MAPA/SIF) because of the presence of F. hepatica. The information was distributed over 11 states: Espírito Santo, Goiás, Minas Gerais, Mato Grosso do Sul, Mato Grosso, Pará, Paraná, Rio de Janeiro, Rio Grande do Sul, Santa Catarina and São Paulo. The highest prevalence of fasciolosis was observed in the southern states, with disease clusters along the coast of Paraná and Santa Catarina and in Rio Grande do Sul. Also, temporal variation of the prevalence was observed. The observed prevalence and the kriged prevalence maps presented in this paper can assist both animal and human health workers in estimating the risk of infection in their state or municipality.

Close relationship between diameters at 30cm height and at breast height (DBH)

This paper proposes the establishment of a second diameter measuring standard at 30cm shoot extension ('diam30') as input variable for allometric biomass estimation of small and mid-sized plant shoots. This diameter standard is better suited than the diameter at breast height (DBH, i.e. diameter at 1.30m shoot extension) for adequate characterization of plant dimensions in low bushy vegetation or in primary forest undergrowth. The relationships between both diameter standards are established based on a dataset of 8645 tree, liana and palm shoots in secondary and primary forests of central Amazonia (ranging from 1-150mm dbh). Dbh can be predicted from the diam(30) with high precision, the error introduced by diameter transformation is only 2-3% for trees and palms, and 5% for lianas. This is well acceptable for most field study purposes. Relationships deviate slightly from linearity and differ between growth forms. Relationships were markedly similar for different vegetation types (low secondary regrowth vs. primary forests), soils, and selected genera or species. This points to a general validity and applicability of diameter transformations for other field studies. This study provides researchers with a tool for the allometric estimation of biomass in low or structurally heterogeneous vegetation. Rather than applying a uniform diameter standard, the measuring position which best represents the respective plant can be decided on shoot-by-shoot. Plant diameters measured at 30cm height can be transformed to dbh for subsequent allometric biomass estimation. We recommend the use of these diameter transformations only for plants extending well beyond the theoretical minimum shoot length (i.e., >2m height). This study also prepares the ground for the comparability and compatability of future allometric equations specifically developed for small- to mid-sized vegetation components (i.e., bushes, undergrowth) which are based on the diam(30) measuring standard.

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

Evidence for a neotropical origin of Leishmania

Contradictory biogeographic hypotheses for either a Neotropical or a Palaearctic origin of the genus Leishmania have been proposed. Hypotheses constructed on the basis of biogeographic data must be tested against an independent dataset and cannot be supported by biogeographic data alone. In the absence of a fossil record for the Leishmania these two hypotheses were tested against a combined dataset of sequences from the DNA polymerase A catalytic subunit and the RNA polymerase II largest subunit. The phylogeny obtained provided considerable support for a Neotropical origin of the genus Leishmania and leads us to reject the hypothesis for a Palaearctic origin.

Infection with Trypanosoma cruzi TcII and TcI in free-ranging population of lion tamarins (Leontopithecus spp): an 11-year follow-up

Here, we present a review of the dataset resulting from the 11-years follow-up of Trypanosoma cruziinfection in free-ranging populations of Leontopithecus rosalia(golden lion tamarin) andLeontopithecus chrysomelas(golden-headed lion tamarin) from distinct forest fragments in Atlantic Coastal Rainforest. Additionally, we present new data regarding T. cruziinfection of small mammals (rodents and marsupials) that live in the same areas as golden lion tamarins and characterisation at discrete typing unit (DTU) level of 77 of these isolates. DTU TcII was found to exclusively infect primates, while TcI infectedDidelphis aurita and lion tamarins. The majority ofT. cruziisolates derived from L. rosaliawere shown to be TcII (33 out 42) Nine T. cruziisolates displayed a TcI profile. Golden-headed lion tamarins demonstrated to be excellent reservoirs of TcII, as 24 of 26 T. cruziisolates exhibited the TcII profile. We concluded the following: (i) the transmission cycle of T. cruziin a same host species and forest fragment is modified over time, (ii) the infectivity competence of the golden lion tamarin population fluctuates in waves that peak every other year and (iii) both golden and golden-headed lion tamarins are able to maintain long-lasting infections by TcII and TcI.

PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA

Under field conditions in the Amazon forest, soil bulk density is difficult to measure. Rigorous methodological criteria must be applied to obtain reliable inventories of C stocks and soil nutrients, making this process expensive and sometimes unfeasible. This study aimed to generate models to estimate soil bulk density based on parameters that can be easily and reliably measured in the field and that are available in many soil-related inventories. Stepwise regression models to predict bulk density were developed using data on soil C content, clay content and pH in water from 140 permanent plots in terra firme (upland) forests near Manaus, Amazonas State, Brazil. The model results were interpreted according to the coefficient of determination (R2) and Akaike information criterion (AIC) and were validated with a dataset consisting of 125 plots different from those used to generate the models. The model with best performance in estimating soil bulk density under the conditions of this study included clay content and pH in water as independent variables and had R2 = 0.73 and AIC = -250.29. The performance of this model for predicting soil density was compared with that of models from the literature. The results showed that the locally calibrated equation was the most accurate for estimating soil bulk density for upland forests in the Manaus region.

Annonaceae substitution rates: a codon model perspective

The Annonaceae includes cultivated species of economic interest and represents an important source of information for better understanding the evolution of tropical rainforests. In phylogenetic analyses of DNA sequence data that are used to address evolutionary questions, it is imperative to use appropriate statistical models. Annonaceae are cases in point: Two sister clades, the subfamilies Annonoideae and Malmeoideae, contain the majority of Annonaceae species diversity. The Annonoideae generally show a greater degree of sequence divergence compared to the Malmeoideae, resulting in stark differences in branch lengths in phylogenetic trees. Uncertainty in how to interpret and analyse these differences has led to inconsistent results when estimating the ages of clades in Annonaceae using molecular dating techniques. We ask whether these differences may be attributed to inappropriate modelling assumptions in the phylogenetic analyses. Specifically, we test for (clade-specific) differences in rates of non-synonymous and synonymous substitutions. A high ratio of nonsynonymous to synonymous substitutions may lead to similarity of DNA sequences due to convergence instead of common ancestry, and as a result confound phylogenetic analyses. We use a dataset of three chloroplast genes (rbcL, matK, ndhF) for 129 species representative of the family. We find that differences in branch lengths between major clades are not attributable to different rates of non-synonymous and synonymous substitutions. The differences in evolutionary rate between the major clades of Annonaceae pose a challenge for current molecular dating techniques that should be seen as a warning for the interpretation of such results in other organisms.

Seleção de variáveis em QSAR

The process of building mathematical models in quantitative structure-activity relationship (QSAR) studies is generally limited by the size of the dataset used to select variables from. For huge datasets, the task of selecting a given number of variables that produces the best linear model can be enormous, if not unfeasible. In this case, some methods can be used to separate good parameter combinations from the bad ones. In this paper three methodologies are analyzed: systematic search, genetic algorithm and chemometric methods. These methods have been exposed and discussed through practical examples.

Classificação periódica: um exemplo didático para ensinar análise de componentes principais

A dataset of chemical properties of the elements is used herein to introduce principal components analysis (PCA). The focus in this article is to verify the classification of the elements within the periodic table. The reclassification of the semimetals as metals or nonmetals emerges naturally from PCA and agrees with the current SBQ/IUPAC periodic table. Dataset construction, basic preprocessing, loading and score plots, and interpretation have been emphasized. This activity can be carried out even when students with distinct levels of formation are together in the same learning environment.

Estudos de QSAR 2D baseados em descritores topológicos e fragmentos moleculares para uma série de derivados azólicos ativos contra Candida albicans

Azole derivatives are the main therapeutical resource against Candida albicans infection in immunocompromised patients. Nevertheless, the widespread use of azoles has led to reduced effectiveness and selection of resistant strains. In order to guide the development of novel antifungal drugs, 2D-QSAR models based on topological descriptors or molecular fragments were developed for a dataset of 74 molecules. The optimal fragment-based model (r² = 0.88, q² = 0.73 and r²pred = 0.62 with 6PCs) and descriptor-based model (r² = 0.82, q² = 0.79 and r²pred = 0.70 with 2 PCs), when analysed synergically, suggested that the triazolone ring and lipophilic properties are both important to antifungal activity.

A risk infection simulation model for fusarium head blight of wheat

Fusarium Head Blight (FHB) is a disease of great concern in wheat (Triticum aestivum). Due to its relatively narrow susceptible phase and environmental dependence, the pathosystem is suitable for modeling. In the present work, a mechanistic model for estimating an infection index of FHB was developed. The model is process-based driven by rates, rules and coefficients for estimating the dynamics of flowering, airborne inoculum density and infection frequency. The latter is a function of temperature during an infection event (IE), which is defined based on a combination of daily records of precipitation and mean relative humidity. The daily infection index is the product of the daily proportion of susceptible tissue available, infection frequency and spore cloud density. The model was evaluated with an independent dataset of epidemics recorded in experimental plots (five years and three planting dates) at Passo Fundo, Brazil. Four models that use different factors were tested, and results showed all were able to explain variation for disease incidence and severity. A model that uses a correction factor for extending host susceptibility and daily spore cloud density to account for post-flowering infections was the most accurate explaining 93% of the variation in disease severity and 69% of disease incidence according to regression analysis.

Absence of hepatitis B virus DNA in patients with hepatitis C and non-A-E hepatitis in the State of São Paulo, Brazil

Occult hepatitis B virus (HBV) infection has been reported as cases in which HBV DNA was detected despite the absence of any HBV serological markers or in cases in which anti-HBc antibody was the sole marker. The aim of the present study was to determine, using the polymerase chain reaction (PCR), whether HBV infection occurs in hepatitis C and non-A-E hepatitis patients without serological evidence of hepatitis B infection in São Paulo State. Two different populations were analyzed: 1) non-A-E hepatitis patients, including 12 patients with acute and 50 patients with chronic hepatic disorders without serological evidence of infection with known hepatitis viruses; 2) 43 patients previously diagnosed as hepatitis C with positive results for anti-HCV and HCV RNA. Among hepatitis C patients, anti-HBc was detected in 18.6% of the subjects. Three different sets of primers were employed for HBV DNA detection by nested PCR, covering different HBV genes: C, S and X. HBV-DNA was not detected in any sample, whereas the positive controls did produce signals. The lack of HBV DNA detection with these pairs of primers could be due to a very low viral load or to the presence of mutations in their annealing sites. The latter is unlikely as these primers were screened against an extensive dataset of HBV sequences. The development of more sensitive methods, such as real time PCR, to detect circular covalent closed DNA is necessary in order to evaluate this question since previous studies have shown that cryptic hepatitis B might occur.

Pipeline for macro- and microarray analyses

The pipeline for macro- and microarray analyses (PMmA) is a set of scripts with a web interface developed to analyze DNA array data generated by array image quantification software. PMmA is designed for use with single- or double-color array data and to work as a pipeline in five classes (data format, normalization, data analysis, clustering, and array maps). It can also be used as a plugin in the BioArray Software Environment, an open-source database for array analysis, or used in a local version of the web service. All scripts in PMmA were developed in the PERL programming language and statistical analysis functions were implemented in the R statistical language. Consequently, our package is a platform-independent software. Our algorithms can correctly select almost 90% of the differentially expressed genes, showing a superior performance compared to other methods of analysis. The pipeline software has been applied to 1536 expressed sequence tags macroarray public data of sugarcane exposed to cold for 3 to 48 h. PMmA identified thirty cold-responsive genes previously unidentified in this public dataset. Fourteen genes were up-regulated, two had a variable expression and the other fourteen were down-regulated in the treatments. These new findings certainly were a consequence of using a superior statistical analysis approach, since the original study did not take into account the dependence of data variability on the average signal intensity of each gene. The web interface, supplementary information, and the package source code are available, free, to non-commercial users at http://ipe.cbmeg.unicamp.br/pub/PMmA.

Gene expression profiling analysis of lung adenocarcinoma

The present study screened potential genes related to lung adenocarcinoma, with the aim of further understanding disease pathogenesis. The GSE2514 dataset including 20 lung adenocarcinoma and 19 adjacent normal tissue samples from 10 patients with lung adenocarcinoma aged 45-73 years was downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) between the two groups were screened using the t-test. Potential gene functions were predicted using functional and pathway enrichment analysis, and protein-protein interaction (PPI) networks obtained from the STRING database were constructed with Cytoscape. Module analysis of PPI networks was performed through MCODE in Cytoscape. In total, 535 upregulated and 465 downregulated DEGs were identified. These included ATP5D, UQCRC2, UQCR11 and genes encoding nicotinamide adenine dinucleotide (NADH), which are mainly associated with mitochondrial ATP synthesis coupled electron transport, and which were enriched in the oxidative phosphorylation pathway. Other DEGs were associated with DNA replication (PRIM1, MCM3, and RNASEH2A), cell surface receptor-linked signal transduction and the enzyme-linked receptor protein signaling pathway (MAPK1, STAT3, RAF1, and JAK1), and regulation of the cytoskeleton and phosphatidylinositol signaling system (PIP5K1B, PIP5K1C, and PIP4K2B). Our findings suggest that DEGs encoding subunits of NADH, PRIM1, MCM3, MAPK1, STAT3, RAF1, and JAK1 might be associated with the development of lung adenocarcinoma.