Dose-response analysis can be carried out using multi-purpose commercial statistical software, but except for a few special cases the analysis easily becomes cumbersome as relevant, non-standard output requires manual programming. The extension package drc for the statistical environment R provides a flexible and versatile infrastructure for dose-response analyses in general. The present version of the package, reflecting extensions and modifications over the last decade, provides a user-friendly interface to specify the model assumptions about the dose-response relationship and comes with a number of extractors for summarizing fitted models and carrying out inference on derived parameters. The aim of the present paper is to provide an overview of state-of-the-art dose-response analysis, both in terms of general concepts that have evolved and matured over the years and by means of concrete examples.

Developmental neurotoxicity (DNT) and many forms of reproductive toxicity (RT) often manifest themselves in functional deficits that are not necessarily based on cell death, but rather on minor changes relating to cell differentiation or communication. The fields of DNT/RT would greatly benefit from in vitro tests that allow the identification of toxicant-induced changes of the cellular proteostasis, or of its underlying transcriptome network. Therefore, the ‘human embryonic stem cell (hESC)-derived novel alternative test systems (ESNATS)’ European commission research project established RT tests based on defined differentiation protocols of hESC and their progeny. Valproic acid (VPA) and methylmercury (MeHg) were used as positive control compounds to address the following fundamental questions: (1) Does transcriptome analysis allow discrimination of the two compounds? (2) How does analysis of enriched transcription factor binding sites (TFBS) and of individual probe sets (PS) distinguish between test systems? (3) Can batch effects be controlled? (4) How many DNA microarrays are needed? (5) Is the highest non-cytotoxic concentration optimal and relevant for the study of transcriptome changes? VPA triggered vast transcriptional changes, whereas MeHg altered fewer transcripts. To attenuate batch effects, analysis has been focused on the 500 PS with highest variability. The test systems differed significantly in their responses (<20 % overlap). Moreover, within one test system, little overlap between the PS changed by the two compounds has been observed. However, using TFBS enrichment, a relatively large ‘common response’ to VPA and MeHg could be distinguished from ‘compound-specific’ responses. In conclusion, the ESNATS assay battery allows classification of human DNT/RT toxicants on the basis of their transcriptome profiles.Electronic supplementary materialThe online version of this article (doi:10.1007/s00204-012-0967-3) contains supplementary material, which is available to authorized users.

Dose-response analysis can be carried out using multi-purpose commercial statistical software, but except for a few special cases the analysis easily becomes cumbersome as relevant, non-standard output requires manual programming. The extension package drc for the statistical environment R provides a flexible and versatile infrastructure for doseresponse analyses in general. The present version of the package, reflecting extensions and modifications over the last decade, provides a user-friendly interface to specify the model assumptions about the dose-response relationship and comes with a number of extractors for summarizing fitted models and carrying out inference on derived parameters. The aim of the present paper is to provide an overview of state-of-the-art dose-response analysis, both in terms of general concepts that have evolved and matured over the years and by means of concrete examples.

SLP students can make significant improvements in both breadth and depth of written reflective practice over a six-week period. Implications for clinical teaching are discussed.

Genome-wide association studies have reported an association between the A-allele of rs1006737 within CACNA1C and affective disorders and schizophrenia. The aim of the present study was to investigate the relationship between rs1006737 and established and potential endophenotypes for these disorders in a population-based cohort of 3793 subjects, using an analytical method designed to assess a previously reported sex-specific effect of CACNA1C. The investigated endophenotypes included personality traits and resilience factors. At 10-year follow-up, subjects were screened for depressive symptoms. All subjects were genotyped for rs1006737. The direction of the effect and mode of inheritance of rs1006737 differed between the sexes. In men, the A-allele was associated with higher emotional lability and lower resilience, that is, lower sense of coherence (P=0.021), lower perceived social support (P=0.018), lower dispositional optimism (P=0.032) and more depressive symptoms at follow-up (P=0.007). In women, the A-allele was associated with lower emotional lability and stronger resilience, that is, higher sense of coherence (P=0.00028), higher perceived social support (P=0.010), lower neuroticism (P=0.022) and fewer depressive symptoms at follow-up (P=0.035). After conservative Bonferroni correction for 32 tests, results only remained significant for sense of coherence in women (P=0.009). These results suggest that CACNA1C is involved in the genetic architecture of endophenotypes for affective disorders and schizophrenia, and that it shows a distinct sex-specific effect. Comprehensive phenotype characterization in case-control samples and the general population, as well as an adequate modeling of sex-specific genetic effects, may be warranted to elucidate the pathogenetic mechanisms conferred by robustly identified susceptibility genes.