Budget impact from the incorporation of positron emission tomography – computed tomography for staging lung cancers

OBJECTIVE To estimate the budget impact from the incorporation of positron emission tomography (PET) in mediastinal and distant staging of non-small cell lung cancer.METHODS The estimates were calculated by the epidemiological method for years 2014 to 2018. Nation-wide data were used about the incidence; data on distribution of the disease´s prevalence and on the technologies’ accuracy were from the literature; data regarding involved costs were taken from a micro-costing study and from Brazilian Unified Health System (SUS) database. Two strategies for using PET were analyzed: the offer to all newly-diagnosed patients, and the restricted offer to the ones who had negative results in previous computed tomography (CT) exams. Univariate and extreme scenarios sensitivity analyses were conducted to evaluate the influence from sources of uncertainties in the parameters used.RESULTS The incorporation of PET-CT in SUS would imply the need for additional resources of 158.1 BRL (98.2 USD) million for the restricted offer and 202.7 BRL (125.9 USD) million for the inclusive offer in five years, with a difference of 44.6 BRL (27.7 USD) million between the two offer strategies within that period. In absolute terms, the total budget impact from its incorporation in SUS, in five years, would be 555 BRL (345 USD) and 600 BRL (372.8 USD) million, respectively. The costs from the PET-CT procedure were the most influential parameter in the results. In the most optimistic scenario, the additional budget impact would be reduced to 86.9 BRL (54 USD) and 103.8 BRL (64.5 USD) million, considering PET-CT for negative CT and PET-CT for all, respectively.CONCLUSIONS The incorporation of PET in the clinical staging of non-small cell lung cancer seems to be financially feasible considering the high budget of the Brazilian Ministry of Health. The potential reduction in the number of unnecessary surgeries may cause the available resources to be more efficiently allocated.

Thermostability of heterophile antibodies from human sera infected with Schistosoma mansoni and geo-helminths: an immuno-metric statistical analysis

Antibody in human sera that induces lysis of sheep erythrocytes in hemolytic assay was investigated. The present study showed that the presence in serum of the thermostable cytolytic anti-sheep red blood cells antibodies is dependent on the Schistosoma mansoni infection, and this is more frequent in adults than in children. The thermostable characteristic of hemolysins in normal sera was not dependent on the presence of Ascaris lumbricoides, Trichuris trichiura or hookworm geo-helminths. Further, thermostable complement-activating heterophile antibodies were noticed in children in association with massive number of S. mansoni eggs. The results were obtained by using the z- and the chi-square tests. The z-test allows us to formulate a one-sided alternative, i.e., a tendency of one of the attributes. On the other hand, the chi-square test analyzes the independence between attributes by using a contingency table. Besides the obtained results being interesting in the field of schistosomiasis mansoni, they can provide a new insight into the use of statistics in medical science.

Effects of light and temperature on isoprene emission at different leaf developmental stages of eschweilera coriacea in central Amazon

Isoprene emission from plants accounts for about one third of annual global volatile organic compound emissions. The largest source of isoprene for the global atmosphere is the Amazon Basin. This study aimed to identify and quantify the isoprene emission and photosynthesis at different levels of light intensity and leaf temperature, in three phenological phases (young mature leaf, old mature leaf and senescent leaf) of Eschweilera coriacea (Matamatá verdadeira), the species with the widest distribution in the central Amazon. In situ photosynthesis and isoprene emission measurements showed that young mature leaf had the highest rates at all light intensities and leaf temperatures. Additionally, it was observed that isoprene emission capacity (Es) changed considerably over different leaf ages. This suggests that aging leads to a reduction of both leaf photosynthetic activity and isoprene production and emission. The algorithm of Guenther et al. (1999) provided good fits to the data when incident light was varied, however differences among E S of all leaf ages influenced on quantic yield predicted by model. When leaf temperature was varied, algorithm prediction was not satisfactory for temperature higher than ~40 °C; this could be because our data did not show isoprene temperature optimum up to 45 °C. Our results are consistent with the hypothesis of the isoprene functional role in protecting plants from high temperatures and highlight the need to include leaf phenology effects in isoprene emission models.

Prognostic value of technetium-99m-labeled single-photon emission computerized tomography in the follow-up of patients after their first myocardial revascularization surgery

OBJECTIVE: To assess the prognostic value of Technetium-99m-labeled single-photon emission computerized tomography (SPECT) in the follow-up of patients who had undergone their first myocardial revascularization. METHODS: We carried out a retrospective study of 280 revascularized patients undergoing myocardial scintigraphy under stress (exercise or pharmacological stress with dipyridamole) and at rest according to a 2-day protocol. A set of clinical, stress electrocardiographic and scintigraphic variables was assessed. Cardiac events were classified as "major" (death, infarction, unstable angina) and "any" (major event or coronary angioplasty or new myocardial revascularization surgery). RESULTS: Thirty-six major events occurred as follows: 3 deaths, 11 infarctions, and 22 unstable anginas. In regard to any event, 22 angioplasties and 7 new surgeries occurred in addition to major events, resulting a total of 65 events. The sensitivity of scintigraphy in prognosticating a major event or any event was, respectively, 55% and 58%, showing a negative predictive value of 90% and 83%, respectively. Diabetes mellitus, inconclusive stress electrocardiography, and a scintigraphic visualization of left ventricular enlargement were significant variables for the occurrence of a major event. On multivariate analysis, abnormal myocardial scintigraphy was a predictor of any event. CONCLUSION: Myocardial perfusion tomography with Technetium-99m may be used to identify high-risk patients after their first myocardial revascularization surgery.

Biometric and statistical investigations on the cnidoma of the genus Hydra (Cnidaria, Hydrozoa)

This paper deals about the nematocysts like a source of biometric information for comparison between the species Hydra vulgaris Pallas, 1766, Hydra vulgaris pedunculata Deserti et al., 2011 and Hydra pseudoligactis (Hyman, 1931). This biometric tool lets us carry out statistical comparisons and adding these results to the identification of specimens from different classificatory groups. In this particular study, we obtained significant differences between species, individuals of each species and nematocysts type when compared the biometry of its nematocysts. Another result was the variation in of particular nematocysts, like atrichous isorhiza and holotrichous isorhiza for the species H. vulgaris in relation to the column size.

Capillaria spp. eggs in Patagonian archaeological sites: statistical analysis of morphometric data

Discriminant analysis was used to identify eggs of Capillaria spp. at specific level found in organic remains from an archaeological site in Patagonia, Argentina, dated of 6,540 ± 110 years before present. In order to distinguish eggshell morphology 149 eggs were measured and grouped into four arbitrary subsets. The analysis used on egg width and length discriminated them into different morphotypes (Wilks' lambda = 0.381, p < 0.05). The correlation analysis suggests that width was the most important variable to discriminate among the Capillaria spp. egg morphotypes (Pearson coefficient = 0.950, p < 0.05). The study of eggshell patterns, the relative frequency in the sample, and the morphometric data allowed us to correlate the four morphotypes with Capillaria species.

Emission of nitrous oxide and carbon dioxide from semi-arid tropical soils in Chiapas México

The semi-arid region of Chiapas is dominated by N2 -fixing shrubs, e.g., Acacia angustissima. Urea-fertilized soil samples under maize were collected from areas covered and uncovered by A. angustissima in different seasons and N2O and CO2 emissions were monitored. The objective of this study was to determine the effects of urea and of the rainy and dry season on gas emissions from semi-arid soil under laboratory conditions. Urea and soil use had no effect on CO2 production. Nitrons oxide emission from soil was three times higher in the dry than in the rainy season, while urea fertilization doubled emissions. Emissions were twice as high from soil sampled under A. angustissima canopy than from arable land, but 1.2 lower than from soil sampled outside the canopy, and five times higher from soil incubated at 40 % of the water-holding capacity (WHC) than at soil moisture content, but 15 times lower than from soil incubated at 100 WHC. It was found that the soil sampling time and water content had a significant effect on N2O emissions, while N fertilizer and sampling location were less influent.

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

Implications of the variability in soil penetration resistance for statistical analysis

Soil penetration resistance (PR) is a measure of soil compaction closely related to soil structure and plant growth. However, the variability in PR hampers the statistical analyses. This study aimed to evaluate the variability of soil PR on the efficiency of parametric and nonparametric analyses in indentifying significant effects of soil compaction and to classify the coefficient of variation of PR into low, medium, high and very high. On six dates, the PR of a typical dystrophic Red Ultisol under continuous no-tillage for 16 years was measured. Three tillage and/or traffic conditions were established with the application of: (i) no chiseling or additional traffic, (ii) additional compaction, and (iii) chiseling. On each date, the nineteen PR data (measured at every 1.5 cm to a depth of 28.5 cm) were grouped in layers with different thickness. In each layer, the treatment effects were evaluated by variance (ANOVA) and Kruskal-Wallis analyses in a completely randomized design, and the coefficients of variation of all analyses were classified (low, intermediate, high and very high). The ANOVA performed better in discriminating the compaction effects, but the rejection rate of null hypothesis decreased from 100 to 80 % when the coefficient of variation increased from 15 to 26 %. The values of 15 and 26 % were the thresholds separating the low/intermediate and the high/very high coefficient variation classes of PR in this Ultisol.

Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

EFFECT OF RICE STRAW AND NITRATE LEVELS IN SOIL SOLUTION ON NITROUS OXIDE EMISSION

Among the greenhouse gases, nitrous oxide (N2O) is considered important, in view of a global warming potential 296 times greater than that of carbon dioxide (CO2) and its dynamics strongly depend on the availability of C and mineral N in the soil. The understanding of the factors that define emissions is essential to develop mitigation strategies. This study evaluated the dynamics of N2O emissions after the application of different rice straw amounts and nitrate levels in soil solution. Pots containing soil treated with sodium nitrate rates (0, 50 and 100 g kg-1 of NO−3-N) and rice straw levels (0, 5 and 10 Mg ha-1), i.e., nine treatments, were subjected to anaerobic conditions. The results showed that N2O emissions were increased by the addition of greater NO−3 amounts and reduced by large straw quantities applied to the soil. On the 1st day after flooding (DAF), significantly different N2O emissions were observed between the treatments with and without NO−3 addition, when straw had no significant influence on N2O levels. Emissions peaked on the 4th DAF in the treatments with highest NO−3-N addition. At this moment, straw application negatively affected N2O emissions, probably due to NO−3 immobilization. There were also alterations in other soil electrochemical characteristics, e.g., higher straw levels raised the Fe, Mn and dissolved C contents. These results indicate that a lowering of NO−3 concentration in the soil and the increase of straw incorporation can decrease N2O emissions.

Critical values of nitrogen indices in tomato plants grown in soil and nutrient solution determined by different statistical procedures

The objective of this study was to establish critical values of the N indices, namely soil-plant analysis development (SPAD), petiole sap N-NO3 and organic N in the tomato leaf adjacent to the first cluster (LAC), under soil and nutrient solution conditions, determined by different statistical approaches. Two experiments were conducted in randomized complete block design with four repli-cations. Tomato plants were grown in soil, in 3 L pot, with five N rates (0, 100, 200, 400 and 800 mg kg-1) and in solution at N rates of 0, 4, 8, 12 and 16 mmol L-1. Experiments in nutrient solution and soil were finished at thirty seven and forty two days after transplanting, respectively. At those times, SPAD index and petiole sap N-NO3 were evaluated in the LAC. Then, plants were harvested, separated in leaves and stem, dried at 70ºC, ground and weighted. The organic N was determined in LAC dry matter. Three statistical procedures were used to calculate critical N values. There were accentuated discrepancies for critical values of N indices obtained with plants grown in soil and nutrient solution as well as for different statistical procedures. Critical values of nitrogen indices at all situations are presented.

Spatial statistical analysis and selection of genotypes in plant breeding

The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.