Search

Differentiated vulvar intraepithelial neoplasia (dVIN) is a premalignant lesion that is known to progress rapidly to invasive carcinoma. Accurate histological diagnosis is therefore crucial to allow appropriate treatment. To identify reliable diagnostic features, we evaluated the inter-observer agreement in the histological assessment of dVIN, among a bi-national, multi-institutional group of pathologists. Two investigators from Erasmus MC selected 36 hematoxylin-eosin-stained glass slides of dVIN and no-dysplasia, and prepared a list of 15 histological features of dVIN. Nine participating pathologists (i) diagnosed each slide as dVIN or no-dysplasia, (ii) indicated which features they used for the diagnosis, and (iii) rated these features in terms of their diagnostic usefulness. Diagnoses rendered by > 50% participants were taken as the consensus (gold standard). p53-immunohistochemistry (IHC) was performed for all cases, and the expression patterns were correlated with the consensus diagnoses. Kappa (ĸ)-statistics were computed to measure inter-observer agreements, and concordance of the p53-IHC patterns with the consensus diagnoses. For the diagnosis of dVIN, overall agreement was moderate (ĸ = 0.42), and pair-wise agreements ranged from slight (ĸ = 0.10) to substantial (ĸ = 0.73). Based on the levels of agreement and ratings of usefulness, the most helpful diagnostic features were parakeratosis, cobblestone appearance, chromatin abnormality, angulated nuclei, atypia discernable under × 100, and altered cellular alignment. p53-IHC patterns showed substantial concordance (ĸ = 0.67) with the consensus diagnoses. Histological interpretation of dVIN remains challenging with suboptimal inter-observer agreement. We identified the histological features that may facilitate the diagnosis of dVIN. For cases with a histological suspicion of dVIN, consensus-based pathological evaluation may improve the reliability of the diagnosis.

The importance of structural biology has been highlighted in the past few years not only as part of drug discovery programs in the pharmaceutical industry but also by structural genomics programs. Although the function of a protein can be studied by several biochemical and or biophysical techniques a molecular understanding of a protein can only be obtained by combining functional data with the three-dimensional structure. In principle three techniques exist to determine a protein structure, namely X-ray crystallography, nuclear magnetic resonance (NMR) and electron microscopy (EM). X-ray crystallography contributes over 90 % of all structures in the protein data bank (PDB) and emphasis the importance of this technique. Crystallization of a protein is a tedious route and although a lot of knowledge about crystallization has been gained in the last decades, one still cannot predict the outcome. The sometimes unexpected bottlenecks in protein purification and crystallization have recently been summarized and possible strategies to obtain a protein crystal were postulated [1]. This book chapter will tackle the next step: How to crystallize proteinligand complexes or intermediate steps of the reaction cycle?

This paper analyses a class of route choice models with closed-form probability expressions, namely, Generalized Multivariate Extreme Value (GMEV) models. A large group of these models emerge from different utility formulas that combine systematic utility and random error terms. Twelve models are captured in a single discrete choice framework. The additive utility formula leads to the known logit family, being multinomial, path-size, paired combinatorial and link-nested. For the multiplicative formulation only the multinomial and path-size weibit models have been identified; this study also identifies the paired combinatorial and link-nested variations, and generalizes the path-size variant. Furthermore, a new traveller's decision rule based on the multiplicative utility formula with a reference route is presented. Here the traveller chooses exclusively based on the differences between routes. This leads to four new GMEV models. We assess the models qualitatively based on a generic structure of route utility with random foreseen travel times, for which we empirically identify that the variance of utility should be different from thus far assumed for multinomial probit and logit-kernel models. The expected travellers' behaviour and model-behaviour under simple network changes are analysed. Furthermore, all models are estimated and validated on an illustrative network example with long distance and short distance origin-destination pairs. The new multiplicative models based on differences outperform the additive models in both tests.