Mother-child histocompatibility and risk of rheumatoid arthritis and systemic lupus erythematosus among mothers.

The study objective was to test the hypothesis that having histocompatible children increases the risk of rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), possibly by contributing to the persistence of fetal cells acquired during pregnancy. We conducted a case control study using data from the UC San Francisco Mother Child Immunogenetic Study and studies at the Inova Translational Medicine Institute. We imputed human leukocyte antigen (HLA) alleles and minor histocompatibility antigens (mHags). We created a variable of exposure to histocompatible children. We estimated an average sequence similarity matching (SSM) score for each mother based on discordant mother-child alleles as a measure of histocompatibility. We used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals. A total of 138 RA, 117 SLE, and 913 control mothers were analyzed. Increased risk of RA was associated with having any child compatible at HLA-B (OR 1.9; 1.2-3.1), DPB1 (OR 1.8; 1.2-2.6) or DQB1 (OR 1.8; 1.2-2.7). Compatibility at mHag ZAPHIR was associated with reduced risk of SLE among mothers carrying the HLA-restriction allele B*07:02 (n = 262; OR 0.4; 0.2-0.8). Our findings support the hypothesis that mother-child histocompatibility is associated with risk of RA and SLE.

Gene conversion of the major histocompatibility complex class I Caja-G in common marmosets (Callithrix jacchus).

Currently, the amount of sequenced and classified MHC class I genes of the common marmoset is limited, in spite of the wide use of this species as an animal model for biomedical research. In this study, 480 clones of MHC class I G locus (Caja-G) cDNA sequences were obtained from 21 common marmosets. Up to 10 different alleles were detected in each common marmoset, leading to the assumption that the Caja-G loci duplicated in the marmoset genome. In the investigated population, four alleles occurred more often, giving evidence for higher immunological advantage of these alleles. In contrast to the human non-classical MHC class I genes, Caja-G shows high rates of polymorphism at the relevant peptide-binding sites, despite its phylogenetic relationship to the non-classical HLA-G. Our results provide information for better understanding of the immunological properties of the common marmoset and confirm the theory of a gene conversion of the Caja-G due to its detected plasticity and the absence of any known HLA-A equivalent.

Semantic Mapping of Named-Entities in openEHR Templates and Ad-hoc Generation of Compositions.

Integration of free texts from reports written by physicians to an interoperable standard is important for improving patient-centric care and research in the medical domain. In the context of unstructured clinical data, NLP Information Extraction serves in finding information in unstructured text. To our best knowledge, there is no efficient solution, in which extracted Named-Entities of an NLP pipeline can be ad-hoc inserted in openEHR compositions. We therefore developed a software solution that solves this data integration problem by mapping Named-Entities of an NLP pipeline to the fields of an openEHR template. The mapping can be accomplished by any user without any programming intervention and allows the ad-hoc creation of a composition based on the mappings.

Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts.

BACKGROUND: Extraction of medical terms and their corresponding values from semi-structured and unstructured texts of medical reports can be a time-consuming and error-prone process. Methods of natural language processing (NLP) can help define an extraction pipeline for accomplishing a structured format transformation strategy. OBJECTIVES: In this paper, we build an NLP pipeline to extract values of the classification of malignant tumors (TNM) from unstructured and semi-structured pathology reports and import them further to a structured data source for a clinical study. Our research interest is not focused on standard performance metrics like precision, recall, and F-measure on the test and validation data. We discuss how with the help of software programming techniques the readability of rule-based (RB) information extraction (IE) pipelines can be improved, and therefore minimize the time to correct or update the rules, and efficiently import them to another programming language. METHODS: The extract rules were manually programmed with training data of TNM classification and tested in two separate pipelines based on design specifications from domain experts and data curators. Firstly we implemented each rule directly in one line for each extraction item. Secondly, we reprogrammed them in a readable fashion through decomposition and intention-revealing names for the variable declaration. To measure the impact of both methods we measure the time for the fine-tuning and programming of the extractions through test data of semi-structured and unstructured texts. RESULTS: We analyze the benefits of improving through readability of the writing of rules, through parallel programming with regular expressions (REGEX), and the Apache Uima Ruta language (AURL). The time for correcting the readable rules in AURL and REGEX was significantly reduced. Complicated rules in REGEX are decomposed and intention-revealing declarations were reprogrammed in AURL in 5 min. CONCLUSION: We discuss the importance of factor readability and how can it be improved when programming RB text IE pipelines. Independent of the features of the programming language and the tools applied, a readable coding strategy can be proven beneficial for future maintenance and offer an interpretable solution for understanding the extraction and for transferring the rules to other domains and NLP pipelines.

openEHR-to-FHIR: Converting openEHR Compositions to Fast Healthcare Interoperability Resources (FHIR) for the German Corona Consensus Dataset (GECCO).

The German Corona Consensus (GECCO) established a uniform dataset in FHIR format for exchanging and sharing interoperable COVID-19 patient specific data between health information systems (HIS) for universities. For sharing the COVID-19 information with other locations that use openEHR, the data are to be converted in FHIR format. In this paper, we introduce our solution through a web-tool named "openEHR-to-FHIR" that converts compositions from an openEHR repository and stores in their respective GECCO FHIR profiles. The tool provides a REST web service for ad hoc conversion of openEHR compositions to FHIR profiles.

High-throughput minor histocompatibility antigen prediction.

MOTIVATION: Minor histocompatibility antigens (mHags) are a diverse collection of MHC-bound peptides that have immunological implications in the context of allogeneic transplantation because of their differential presence in donor and host, and thus play a critical role in the induction of the detrimental graft-versus-host disease (GvHD) or in the development of the beneficial graft-versus-leukemia (GvL) effect. Therefore, the search for mHags has implications not only for preventing GvHD, but also for therapeutic applications involving leukemia-specific T cells. We have created a web-based system, named PeptideCheck, which aims to augment the experimental discovery of mHags using bioinformatic means. Analyzing peptide elution data to search for mHags and predicting mHags from polymorphism and protein databases are the core features. RESULTS: Comparison with known mHag data reveals that some but not all of the previously known mHags can be reproduced. By applying a system of filtering and ranking, we were able to produce an ordered list of potential mHag candidates in which HA-1, HA-3 and HA-8 occur in the best 0.25%. By combining single nucleotide polymorphism, protein, tissue expression and genotypic frequency data, together with antigen presentation prediction algorithms, we propose a list of the best peptide candidates which could potentially induce the GvL effect without causing GvFD. AVAILABILITY: http://www.peptidecheck.org.