Hollow-fiber liquid-phase microextraction of amphetamine-type stimulants in human hair samples

A fast method was optimized and validated in order to quantify amphetamine-type stimulants (amphetamine, AMP; methamphetamine, MAMP; fenproporex, FPX; 3,4-methylenedioxymethamphetamine, MDMA; and 3,4-methylenedioxyamphetamine, MDA) in human hair samples. The method was based in an initial procedure of decontamination of hair samples (50 mg) with dichloromethane, followed by alkaline hydrolysis and extraction of the amphetamines using hollow-fiber liquid-phase micro extraction (HF-LPME) in the three-phase mode. Gas chromatography-mass spectrometry (GC-MS) was used for identification and quantification of the analytes. The LoQs obtained for all amphetamines (around 0.05 ng/mg) were below the cut-off value (0.2 ng/mg) established by the Society of Hair Testing (SoHT). The method showed to be simple and precise. The intra-day and inter-day precisions were within 10.6% and 11.4%, respectively, with the use of only two deuteratecl internal standards (AMP-d5 and MDMA-d5). By using the weighted least squares linear regression (1/x(2)), the accuracy of the method was satisfied in the lower concentration levels (accuracy values better than 87%). Hair samples collected from six volunteers who reported regular use of amphetamines were submitted to the developed method. Drug detection was observed in all samples of the volunteers. (c) 2012 Elsevier B.V. All rights reserved.

Determination of dimethyltryptamine and beta-carbolines (ayahuasca alkaloids) in plasma samples by LC-MS/MS

Background: Ayahuasca is a psychoactive plant beverage originally used by indigenous people throughout the Amazon Basin, long before its modern use by syncretic religious groups established in Brazil, the USA and European countries. The objective of this study was to develop a method for quantification of dimethyltryptamine and beta-carbolines in human plasma samples. Results: The analytes were extracted by means of C18 cartridges and injected into LC-MS/MS, operated in positive ion mode and multiple reaction monitoring. The LOQs obtained for all analytes were below 0.5 ng/ml. By using the weighted least squares linear regression, the accuracy of the analytical method was improved at the lower end of the calibration curve (from 0.5 to 100 ng/ml; r(2)> 0.98). Conclusion: The method proved to be simple, rapid and useful to estimate administered doses for further pharmacological and toxicological investigations of ayahuasca exposure.

Bartlett corrections in Birnbaum-Saunders nonlinear regression models

Lemonte and Cordeiro [Birnbaum-Saunders nonlinear regression models, Comput. Stat. Data Anal. 53 (2009), pp. 4441-4452] introduced a class of Birnbaum-Saunders (BS) nonlinear regression models potentially useful in lifetime data analysis. We give a general matrix Bartlett correction formula to improve the likelihood ratio (LR) tests in these models. The formula is simple enough to be used analytically to obtain several closed-form expressions in special cases. Our results generalize those in Lemonte et al. [Improved likelihood inference in Birnbaum-Saunders regressions, Comput. Stat. DataAnal. 54 (2010), pp. 1307-1316], which hold only for the BS linear regression models. We consider Monte Carlo simulations to show that the corrected tests work better than the usual LR tests.

Influence diagnostics for elliptical semiparametric mixed models

In this paper we extend semiparametric mixed linear models with normal errors to elliptical errors in order to permit distributions with heavier and lighter tails than the normal ones. Penalized likelihood equations are applied to derive the maximum penalized likelihood estimates (MPLEs) which appear to be robust against outlying observations in the sense of the Mahalanobis distance. A reweighed iterative process based on the back-fitting method is proposed for the parameter estimation and the local influence curvatures are derived under some usual perturbation schemes to study the sensitivity of the MPLEs. Two motivating examples preliminarily analyzed under normal errors are reanalyzed considering some appropriate elliptical errors. The local influence approach is used to compare the sensitivity of the model estimates.

Influência da idade no comportamento da frequência cardíaca na transição repouso-exercício: uma análise por deltas e regressão linear

INTRODUÇÃO: As modificações da frequência cardíaca (FC) durante a transição repouso-exercício podem ser caracterizadas por meio da aplicação de cálculos matemáticos simples, como: deltas 0-10 e 0-30s para inferir sobre o sistema nervoso parassimpático, e delta e regressão linear aplicados no intervalo 60-240s para inferir sobre o sistema nervoso simpático. Assim, o objetivo deste estudo foi testar a hipótese de que indivíduos jovens e de meia-idade apresentam diferentes respostas da FC em exercício de intensidade moderada e intensa, com diferentes cálculos matemáticos. MÉTODOS: Homens aparentemente saudáveis, sendo sete de meia-idade e 10 jovens, foram submetidos a testes de carga constante de intensidade moderada e intensa. Foram calculados os deltas da FC nos períodos de 0-10s, 0-30s e 60-240s e a regressão linear simples no período de 60 a 240s. Os parâmetros obtidos na análise de regressão linear simples foram: intercepto e inclinação angular. Utilizou-se o teste Shapiro-Wilk para verificar a distribuição dos dados e o teste t não pareado para comparação entre os grupos. O nível de significância estatística considerado foi 5%. RESULTADOS: O valor do intercepto e do delta 0-10s foi menor no grupo meia-idade nas duas cargas e a inclinação do ângular foi menor no grupo meia-idade no exercício moderado. CONCLUSÃO: Os indivíduos jovens apresentam retirada vagal de maior magnitude no estágio inicial da resposta da FC durante exercício dinâmico em carga constante nas intensidades analisadas e maior velocidade de ajuste da resposta simpática em exercícios moderados.

Influence of chest wall on lung mechanics during inspiration

RATIONALE: The interaction between lungs and chest wall influences lung volume, that determines lung history during respiration cycle. In this study, the influence of chest wall mechanics on respiratory system is assessed by the evaluation of inspiration pressure-volume curve (PV curve) under three different situations: closed-chest, open-chest and isolated lung. The PV curve parameters in each situation allow us to further understand the role played by different chest wall elements in the respiratory function. Methods: Twenty-four male Wistar rats (236 ± 29 g) were used. The animals were weighted and then anesthetized with xylazine 2% (O,SmL/kg) and ketamine 10% (0,9mL/kg), exsanguinated and later tracheostomies with a metallic cannula (14 gauge).The cannula was connected to an automatic small animal insufflator. This setup was connected to a pressure transducer (32 samples/s). The 24 animals were randomly separated in three groups:(i) closed chest,(ii) open chest and (iii) isolated lung. The rats were insufflated with 20mL quasi-statically (constant speed of 0,1mUs). lnsufflated volume and measured pressure data were kept and PV curves were obtained for all animals. The PV curves were fitted (non-linear least squares) against the sigmoid equation (1) to obtain the sigmoid equation parameters (a,b,c,d). Elastance measurements were obtained from linear regression of pressure/volume measurements in a 0,8s interval before and after the calculated point. Results: The parameters a,b and c showed no significant change, but the parameter d showed a significant variation among the three groups. The initial elastance also varied between open and closed chest, indicating the need of a higher pressure for the lung expansion, as can be seen in Table 1. Conclusion: A supporting effect of the chest wall was observed at the initial moments of inspiration, observed as a higher initial elastance in open chest situations than in closed chest situations (p=0,00001). The similar initial elastance for the isolated lung and closed chest may be explained by the specific method used for the isolated lung experiment. As the isolated lung is supported by the trachea vertically, the weight of the tissue may have a similar effect of the residual negative pressure in the thorax, responsible for maintaining the residual volume.

Influence of chest wall on lung mechanics during inspiration.

RATIONALE: The interaction between lungs and chest wall influences lung volume, that determines lung history during respiration cycle. In this study, the influence of chest wall mechanics on respiratory system is assessed by the evaluation of inspiration pressure-volume curve (PV curve) under three different situations: closed-chest, open-chest and isolated lung. The PV curve parameters in each situation allow us to further understand the role played by different chest wall elements in the respiratory function. Methods: Twenty-four male Wistar rats (236 ± 29 g) were used. The animals were weighted and then anesthetized with xylazine 2% (0,5mL/kg) and ketamine 10% (0,9mL/kg), exsanguinated and later tracheostomized with a metallic cannula (14 gauge). The cannula was connected to an automatic small animal insufflator. This setup was connected to a pressure transducer (32 samples/s). The 24 animals were randomly separated in three groups: (i) closed chest, (ii) open chest and (iii) isolated lung. The rats were insufflated with 20mL quasi-statically (constant speed of 0,1mL/s). Insufflated volume and measured pressure data were kept and PV curves were obtained for all animals. The PV curves were fitted (non-linear least squares) against the sigmoid equation (1) to obtain the sigmoid equation parameters (a,b,c,d). Elastance measurements were obtained from linear regression of pressure/volume measurements in a 0,8s interval before and after the calculated point. Results: The parameters a, b and c showed no significant change, but the parameter d showed a significant variation among the three groups. The initial elastance also varied between open and closed chest, indicating the need of a higher pressure for the lung expansion, as can be seen in Table 1. Table 1: Mean and Standard Deviation of parameters obtained for each protocol. Protocol: Closed Chest – a (mL) -0.35±0.33; b (mL) 13.93±0.89; c (cm H2O) 21.28±2.37; d (cm H2O) 6.17±0.84; r²** (%) 99.4±0.14; Initial Elastance* (cm H2)/mL) 12.72±6.66; Weight (g) 232.33±5.72. Open Chest - a (mL) 0.01±0.28; b (mL) 14.79±0.54; c (cm H2O) 19.47±1.41; d (cm H2O) 3.50±0.28; r²** (%) 98.8±0.34; Initial Elastance* (cm H2)/mL) 28.68±2.36; Weight (g) 217.33±7.97. Isolated Lung - a (mL) -0.09±0.46; b (mL) 14.22±0.75; c (cm H2O) 21.76±1.43; d (cm H2O) 4.24±0.50; r²** (%) 98.9±0.19; Initial Elastance* (cm H2)/mL) 7.13±8.85; Weight (g) 224.33±16.66. * Elastance measures in the 0-0,1 mL range. ** Goodness of sigmoid fit versus measured data Conclusion: A supporting effect of the chest wall was observed at the initial moments of inspiration, observed as a higher initial elastance in open chest situations than in closed chest situations (p=0,00001). The similar initial elastance for the isolated lung and closed chest may be explained by the specific method used for the isolated lung experiment. As the isolated lung is supported by the trachea vertically, the weight of the tissue may have a similar effect of the residual negative pressure in the thorax, responsible for maintaining the residual volume.

Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.

A prediction model for assessing residential radon concentration in Switzerland

Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th-90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40-111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69-215 Bq/m³) in the medium category, and 219 Bq/m³ (108-427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be robust through validation with an independent dataset. The model is appropriate for predicting radon level exposure of the Swiss population in epidemiological research. Nevertheless, some exposure misclassification and regression to the mean is unavoidable and should be taken into account in future applications of the model.

Blood lead level association with lower body weight in NHANES 1999-2006

BACKGROUND Lead exposure is associated with low birth-weight. The objective of this study is to determine whether lead exposure is associated with lower body weight in children, adolescents and adults. METHODS We analyzed data from NHANES 1999-2006 for participants aged ≥3 using multiple logistic and multivariate linear regression. Using age- and sex-standardized BMI Z-scores, overweight and obese children (ages 3-19) were classified by BMI ≥85 th and ≥95 th percentiles, respectively. The adult population (age ≥20) was classified as overweight and obese with BMI measures of 25-29.9 and ≥30, respectively. Blood lead level (BLL) was categorized by weighted quartiles. RESULTS Multivariate linear regressions revealed a lower BMI Z-score in children and adolescents when the highest lead quartile was compared to the lowest lead quartile (β (SE)=-0.33 (0.07), p<0.001), and a decreased BMI in adults (β (SE)=-2.58 (0.25), p<0.001). Multiple logistic analyses in children and adolescents found a negative association between BLL and the percentage of obese and overweight with BLL in the highest quartile compared to the lowest quartile (OR=0.42, 95% CI: 0.30-0.59; and OR=0.67, 95% CI: 0.52-0.88, respectively). Adults in the highest lead quartile were less likely to be obese (OR=0.42, 95% CI: 0.35-0.50) compared to those in the lowest lead quartile. Further analyses with blood lead as restricted cubic splines, confirmed the dose-relationship between blood lead and body weight outcomes. CONCLUSIONS BLLs are associated with lower body mass index and obesity in children, adolescents and adults.

ROBREG: Stata module providing robust regression estimators

robreg provides a number of robust estimators for linear regression models. Among them are the high breakdown-point and high efficiency MM-estimator, the Huber and bisquare M-estimator, and the S-estimator, each supporting classic or robust standard errors. Furthermore, basic versions of the LMS/LQS (least median of squares) and LTS (least trimmed squares) estimators are provided. Note that the moremata package, also available from SSC, is required.

Determinants of renal tissue oxygenation as measured with BOLD-MRI in chronic kidney disease and hypertension in humans.

Experimentally renal tissue hypoxia appears to play an important role in the pathogenesis of chronic kidney disease (CKD) and arterial hypertension (AHT). In this study we measured renal tissue oxygenation and its determinants in humans using blood oxygenation level-dependent magnetic resonance imaging (BOLD-MRI) under standardized hydration conditions. Four coronal slices were selected, and a multi gradient echo sequence was used to acquire T2* weighted images. The mean cortical and medullary R2* values ( = 1/T2*) were calculated before and after administration of IV furosemide, a low R2* indicating a high tissue oxygenation. We studied 195 subjects (95 CKD, 58 treated AHT, and 42 healthy controls). Mean cortical R2 and medullary R2* were not significantly different between the groups at baseline. In stimulated conditions (furosemide injection), the decrease in R2* was significantly blunted in patients with CKD and AHT. In multivariate linear regression analyses, neither cortical nor medullary R2* were associated with eGFR or blood pressure, but cortical R2* correlated positively with male gender, blood glucose and uric acid levels. In conclusion, our data show that kidney oxygenation is tightly regulated in CKD and hypertensive patients at rest. However, the metabolic response to acute changes in sodium transport is altered in CKD and in AHT, despite preserved renal function in the latter group. This suggests the presence of early renal metabolic alterations in hypertension. The correlations between cortical R2* values, male gender, glycemia and uric acid levels suggest that these factors interfere with the regulation of renal tissue oxygenation.

Modeling absolute differences in life expectancy with a censored skew-normal regression approach.

Parameter estimates from commonly used multivariable parametric survival regression models do not directly quantify differences in years of life expectancy. Gaussian linear regression models give results in terms of absolute mean differences, but are not appropriate in modeling life expectancy, because in many situations time to death has a negative skewed distribution. A regression approach using a skew-normal distribution would be an alternative to parametric survival models in the modeling of life expectancy, because parameter estimates can be interpreted in terms of survival time differences while allowing for skewness of the distribution. In this paper we show how to use the skew-normal regression so that censored and left-truncated observations are accounted for. With this we model differences in life expectancy using data from the Swiss National Cohort Study and from official life expectancy estimates and compare the results with those derived from commonly used survival regression models. We conclude that a censored skew-normal survival regression approach for left-truncated observations can be used to model differences in life expectancy across covariates of interest.

Climate-pollen relationships AD 1901-1996 in two small mires near the forest limit in the northern and central Swiss Alps

Climatic relationships were established in two 210Pb dated pollen sequences from small mires closely surrounded by forest just below actual forest limits (but about 300 m below potential climatic forest limits) in the northern Swiss Alps (suboceanic in climate; mainly with Picea) and the central Swiss Alps (subcontinental; mainly Pinus cembra and Larix) at annual or near-annual resolution from ad 1901 to 1996. Effects of vegetational succession were removed by splitting the time series into early and late periods and by linear detrending. Both pollen concentrations detrended by the depth-age model and modified percentages (in which counts of dominant pollen types are down-weighted) are correlated by simple linear regression with smoothed climatic parameters with one-and two-year timelags, including average monthly and April/September daylight air temperatures and with seasonal and annual precipitation sums. Results from detrended pollen concentrations suggest that peat accumulation is favoured in the northern-Alpine mire either by early snowmelt or by summer precipitation, but in the central-Alpine mire by increased precipitation and cooler summers, suggesting a position of the northern-Alpine mire near the upper altitudinal limit of peat formation, but of the central-Alpine mire near the lower limit. Results from modified pollen percentages indicate that pollen pro duction by plants growing near their upper altitudinal limit is limited by insufficient warmth in summer, and pollen production by plants growing near their lower altitudinal limit is limited by too-high temperatures. Only weakly significant pollen/climate relationships were found for Pinus cembra and Larix, probably because they experience little climatic stress growing 300 m below the potential climatic forest limit.

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^