We provide a systematic review of epidemiological surveys of autistic disorder and pervasive developmental disorders (PDDs) worldwide. A secondary aim was to consider the possible impact of geographic, cultural/ethnic, and socioeconomic factors on prevalence estimates and on clinical presentation of PDD. Based on the evidence reviewed, the median of prevalence estimates of autism spectrum disorders was 62/10 000. While existing estimates are variable, the evidence reviewed does not support differences in PDD prevalence by geographic region nor of a strong impact of ethnic/cultural or socioeconomic factors. However, power to detect such effects is seriously limited in existing data sets, particularly in low‐income countries. While it is clear that prevalence estimates have increased over time and these vary in different neighboring and distant regions, these findings most likely represent broadening of the diagnostic concets, diagnostic switching from other developmental disabilities to PDD, service availability, and awareness of autistic spectrum disorders in both the lay and professional public. The lack of evidence from the majority of the world's population suggests a critical need for further research and capacity building in low‐ and middle‐income countries. Autism Res 2012, 5: 160–179. © 2012 International Society for Autism Research, Wiley Periodicals, Inc.

Multiple endocrine neoplasia-type 1 (MEN1) is an autosomal dominant familial cancer syndrome characterized by tumors in parathyroids, enteropancreatic endocrine tissues, and the anterior pituitary. DNA sequencing from a previously identified minimal interval on chromosome 11q13 identified several candidate genes, one of which contained 12 different frameshift, nonsense, missense, and in-frame deletion mutations in 14 probands from 15 families. The MEN1 gene contains 10 exons and encodes a ubiquitously expressed 2.8-kilobase transcript. The predicted 610-amino acid protein product, termed menin, exhibits no apparent similarities to any previously known proteins. The identification of MEN1 will enable improved understanding of the mechanism of endocrine tumorigenesis and should facilitate early diagnosis.

John C Martin Foundation.

The mucus layer coating the gastrointestinal tract is the front line of innate host defense, largely because of the secretory products of intestinal goblet cells. Goblet cells synthesize secretory mucin glycoproteins (MUC2) and bioactive molecules such as epithelial membrane-bound mucins (MUC1, MUC3, MUC17), trefoil factor peptides (TFF), resistin-like molecule β (RELMβ), and Fc-γ binding protein (Fcgbp). The MUC2 mucin protein forms trimers by disulfide bonding in cysteine-rich amino terminal von Willebrand factor (vWF) domains, coupled with crosslinking provided by TFF and Fcgbp proteins with MUC2 vWF domains, resulting in a highly viscous extracellular layer. Colonization by commensal intestinal microbiota is limited to an outer “loose” mucus layer, and interacts with the diverse oligosaccharides of mucin glycoproteins, whereas an “inner” adherent mucus layer is largely devoid of bacteria. Defective mucus layers resulting from lack of MUC2 mucin, mutated Muc2 mucin vWF domains, or from deletion of core mucin glycosyltransferase enzymes in mice result in increased bacterial adhesion to the surface epithelium, increased intestinal permeability, and enhanced susceptibility to colitis caused by dextran sodium sulfate. Changes in mucin gene expression and mucin glycan structures occur in cancers of the intestine, contributing to diverse biologic properties involved in the development and progression of cancer. Further research is needed on identification and functional significance of various components of mucus layers and the complex interactions among mucus layers, microbiota, epithelial cells, and the underlying innate and adaptive immunity. Further elucidation of the regulatory mechanisms involved in mucin changes in cancer and inflammation may lead to the development of novel therapeutic approaches.

Two-thirds of ASD cases in the overall sample were in the mainstream school population, undiagnosed and untreated. These findings suggest that rigorous screening and comprehensive population coverage are necessary to produce more accurate ASD prevalence estimates and underscore the need for better detection, assessment, and services.

BackgroundMHC class II binding predictions are widely used to identify epitope candidates in infectious agents, allergens, cancer and autoantigens. The vast majority of prediction algorithms for human MHC class II to date have targeted HLA molecules encoded in the DR locus. This reflects a significant gap in knowledge as HLA DP and DQ molecules are presumably equally important, and have only been studied less because they are more difficult to handle experimentally.ResultsIn this study, we aimed to narrow this gap by providing a large scale dataset of over 17,000 HLA-peptide binding affinities for a set of 11 HLA DP and DQ alleles. We also expanded our dataset for HLA DR alleles resulting in a total of 40,000 MHC class II binding affinities covering 26 allelic variants. Utilizing this dataset, we generated prediction tools utilizing several machine learning algorithms and evaluated their performance.ConclusionWe found that 1) prediction methodologies developed for HLA DR molecules perform equally well for DP or DQ molecules. 2) Prediction performances were significantly increased compared to previous reports due to the larger amounts of training data available. 3) The presence of homologous peptides between training and testing datasets should be avoided to give real-world estimates of prediction performance metrics, but the relative ranking of different predictors is largely unaffected by the presence of homologous peptides, and predictors intended for end-user applications should include all training data for maximum performance. 4) The recently developed NN-align prediction method significantly outperformed all other algorithms, including a naïve consensus based on all prediction methods. A new consensus method dropping the comparably weak ARB prediction method could outperform the NN-align method, but further research into how to best combine MHC class II binding predictions is required.

The immune epitope database analysis resource (IEDB-AR: http://tools.iedb.org) is a collection of tools for prediction and analysis of molecular targets of T- and B-cell immune responses (i.e. epitopes). Since its last publication in the NAR webserver issue in 2008, a new generation of peptide:MHC binding and T-cell epitope predictive tools have been added. As validated by different labs and in the first international competition for predicting peptide:MHC-I binding, their predictive performances have improved considerably. In addition, a new B-cell epitope prediction tool was added, and the homology mapping tool was updated to enable mapping of discontinuous epitopes onto 3D structures. Furthermore, to serve a wider range of users, the number of ways in which IEDB-AR can be accessed has been expanded. Specifically, the predictive tools can be programmatically accessed using a web interface and can also be downloaded as software packages.

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