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Dr. Martin C. Mihm, Jr.'s innovative work on the dysplastic nevus achieved a milestone in his chapter in the World Health Organisation Classification of Skin Tumours (WHO-C). WHO-C presents a dichotomous classification (high-grade versus low-grade dysplastic nevi) and a quantitative metric to assess melanocytic nuclear enlargement. The Duke classification is a related approach that provides mostly quantitative histopathologic criteria for dysplastic nevi and gives due weight to architectural features as well as cytology. This paper proposes and illustrates updated criteria for scoring and grading melanocytic dysplasia, incorporating some of the definitions and categories of WHO-C, while refining the quantitative and architectural elements of the Duke grading system to facilitate more detailed and precise assessment of dysplastic nevi., (© 2024 The Author(s). Journal of Cutaneous Pathology published by John Wiley & Sons Ltd.)

For genome-wide association studies, we propose a new method for identifying significant biological pathways. In this approach, we aggregate data across single-nucleotide polymorphisms to obtain summary measures at the gene level. We then use a hierarchical Bayesian model, which takes the gene-level summary measures as data, in order to evaluate the relevance of each pathway to an outcome of interest (e.g., disease status). Although shifting the focus of analysis from individual genes to pathways has proven to improve the statistical power and provide more robust results, such methods tend to eliminate a large number of genes whose pathways are unknown. For these genes, we propose to use a Bayesian multinomial logit model to predict the associated pathways by using the genes with known pathways as the training data. Our hierarchical Bayesian model takes the uncertainty regarding the pathway predictions into account while assessing the significance of pathways. We apply our method to two independent studies on type 2 diabetes and show that the overlap between the results from the two studies is statistically significant. We also evaluate our approach on the basis of simulated data., (Copyright © 2012 John Wiley & Sons, Ltd.)