Environmental epidemiological study and estimation of benchmark dose for renal dysfunction in a cadmium-polluted area in China

We have performed a study aimed at investigating the critical concentration of urinary cadmium (UCd) required for the development of renal dysfunction. We studied population groups (totally 790 persons) living in two cadmium exposed areas and one control area in China. UCd, was determined as an indicator of cadmium exposure and accumulation, while the concentrations of N-acetyl-beta-D-glucosaminidase (NAG), its iso-form B (NAG-B), beta2-microglobulin (B2M), retinol binding protein (RBP), and albumin (ALB) in urine were measured as indicators of the renal effects caused by cadmium. There was a significantly increased prevalence of hyperNAGuria, hyperNAG-Buria, hyperB2Muria, hyperRBPuria and hyperALBuria with increasing levels of Cd excretion in urine. We used the benchmark dose (BMD) procedure to estimate the critical concentration of urinary cadmium in this general population. The lower confidence limit of the BMD (LBMD-05) of urinary cadmium for a 5% level of risk above the background level was estimated for each of the renal effect indicators. The BMD-05/LBMD-05 were estimated to be 4.46/3.99, 6.70/5.87, 8.36/7.31, 7.98/6.98 and 15.06/12.18 microg/g creatinine for urinary NAG-B, NAG, B2M, RBP and ALB, respectively. Our findings suggest, based on the present study, that the Lower Confidence Limit of the Population Critical Concentration of UCd (LPCCUCd-05) of tubular dysfunction for 5% excess risk level above the background may be ca. 3-4 microg/g creatinine, and that cadmium concentration in urine should be kept below this level to prevent renal tubular damage. This report is the first to use the BMD method in this field and to define the concept of critical concentration in urine.     

Comparison of different approaches for dose response analysis

Characterizing an appropriate dose‐response relationship and identifying the right dose in a clinical trial are two main goals of early drug‐development. MCP‐Mod is one of the pioneer approaches developed within the last 10 years that combines the modeling techniques with multiple comparison procedures to address the above goals in clinical drug development. The MCP‐Mod approach begins with a set of potential dose‐response models, tests for a significant dose‐response effect (proof of concept, PoC) using multiple linear contrasts tests and selects the “best” model among those with a significant contrast test. A disadvantage of the method is that the parameter values of the candidate models need to be fixed a priori for the contrasts tests. This may lead to a loss in power and unreliable model selection. For this reason, several variations of the MCP‐Mod approach and a hierarchical model selection approach have been suggested where the parameter values need not be fixed in the proof of concept testing step and can be estimated after the model selection step. This paper provides a numerical comparison of the different MCP‐Mod variants and the hierarchical model selection approach with regard to their ability of detecting the dose‐response trend, their potential to select the correct model and their accuracy in estimating the dose response shape and minimum effective dose. Additionally, as one of the approaches is based on two‐sided model comparisons only, we make it more consistent with the common goals of a PoC study, by extending it to one‐sided comparisons between the constant and alternative candidate models in the proof of concept step.     

Shapes and functions of species–area curves (II): a review of new models and parameterizations

This paper has extended and updated my earlier list and analysis of candidate models used in theoretical modelling and empirical examination of species–area relationships (SARs). I have also reviewed trivariate models that can be applied to include a second independent variable (in addition to area) and discussed extensively the justifications for fitting curves to SARs and the choice of model. There is also a summary of the characteristics of several new candidate models, especially extended power models, logarithmic models and parameterizations of the negative-exponential family and the logistic family. I have, moreover, examined the characteristics and shapes of trivariate linear, logarithmic and power models, including combination variables and interaction terms. The choice of models according to best fit may conflict with problems of non-normality or heteroscedasticity. The need to compare parameter estimates between data sets should also affect model choice. With few data points and large scatter, models with few parameters are often preferable. With narrow-scale windows, even inflexible models such as the power model and the logarithmic model may produce good fits, whereas with wider-scale windows where inflexible models do not fit well, more flexible models such as the second persistence (P2) model and the cumulative Weibull distribution may be preferable. When extrapolations and expected shapes are important, one should consider models with expected shapes, e.g. the power model for sample area curves and the P2 model for isolate curves. The choice of trivariate models poses special challenges, which one can more effectively evaluate by inspecting graphical plots.     

Interval estimation of the kappa coefficient with binary classification and an equal marginal probability model

We derive a likelihood score method for interval estimation of the intraclass version of the kappa coefficient of agreement with binary classification using a general theory of Bartlett (1953, Biometrika 40, 306-317). By exact evaluation, we investigate statistical properties of the score method, the chi-square goodness-of-fit procedure (Donner and Eliasziw, 1992, Statistics in Medicine 11, 1511-1519; Hale and Fleiss, 1993, Biometrics 49, 523-534), and a crude confidence interval for small and medium sample sizes. Actual coverage percentages of the score and chi-square methods are satisfactorily close to the nominal confidence coefficient, while that of the crude method is quite unsatisfactory. The expected length of the score method is shorter than that of the chi-square procedure when the response rate is very small or very large.     

General response patterns of fish populations to stress: an evaluation using an individual-based simulation model

General response patterns of fish populations tostress, originally proposed by Colby for fisheriesrehabilitation and later adapted by Munkittrick forcontaminants, were evaluated using an individual-basedsimulation model. General response patterns relatechanges in population-level variables to the type ofstress. The model follows the daily growth,mortality, and spawning of individual yellow perch andwalleye through their lifetime, and was corroboratedusing Oneida Lake data. Two versions of the model wereused: population (yellow perch only) and community(dynamic predation on yellow perch by walleye). Eightstresses were imposed on the population and communityversions of the model and 100-year simulations wereperformed. Response patterns were defined by changesin predicted yellow perch mean population abundance,mean age of adults, and mean adult growth (representedby mean length at age-7). Proposed response patternswere similar to those predicted using the populationversion of the model. Simulations using the communityversion of the model distorted the response patterns,either causing amplification, dampening, or reversalof many of the patterns. Predicted response patternsbecame unique when additional variables were included.Our model results suggest that caution is appropriatein interpreting general response patterns based onmean age, or when the population of interest plays amajor role in a relatively simple food web. The responsepattern approach may be better at identifying the lifestage impacted rather than the mechanism of the stress.     

Polymorphism of estrogen response element in TFF1 gene promoter is associated with an increased susceptibility to gastric cancer

TFF1 is a cysteine-rich protein that forms a characteristic trefoil domain through disulfide bonds, which render it resistant to vigorous conditions and it involves in maintaining the integrity of the gastric mucosa. Decreased expression of TFF1 gene plays a role in the development of gastric cancer. We examined the association between the promoter polymorphisms of the TFF1 gene and the risk of development of gastric cancer, in a case-control study including 199 controls and 141 patients with gastric cancer. Assessment of single nucleotide polymorphisms in the promoter region of the TFF1 gene was performed by sequencing and polymerase chain reaction-based restriction fragment length polymorphism. We found a statistically significant increased risk of gastric cancer associated with − 394 TT genotypes (OR = 8.78, CI = 2.85-27.05, p < 0.001) and CT (OR = 1.64, CI = 1.04-2.60, p = 0.033). This single nucleotide polymorphism occurs naturally in an estrogen response element. According to induction of the TFF1 gene by estrogen, it is possible that the substitution of C to T results in a decreased estrogen receptor binding affinity to the estrogen response element and in turn it decreases the expression of the TFF1 gene that may be involved in development of gastric cancer over a lifetime.