The Retinoic Acid Receptor Agonist Am80 Increases Mucosal Inflammation in an IL-6 Dependent Manner During Trichuris muris Infection

PURPOSE: Vitamin A metabolites, such as all-trans-retinoic acid (RA) that act through the nuclear receptor; retinoic acid receptor (RAR), have been shown to polarise T cells towards Th2, and to be important in resistance to helminth infections. Co-incidentally, people harbouring intestinal parasites are often supplemented with vitamin A, as both vitamin A deficiency and parasite infections often occur in the same regions of the globe. However, the impact of vitamin A supplementation on gut inflammation caused by intestinal parasites is not yet completely understood. METHODS: Here, we use Trichuris muris, a helminth parasite that buries into the large intestine of mice causing mucosal inflammation, as a model of both human Trichuriasis and IBD, treat with an RARα/β agonist (Am80) and quantify the ensuing pathological changes in the gut. RESULTS: Critically, we show, for the first time, that rather than playing an anti-inflammatory role, Am80 actually exacerbates helminth-driven inflammation, demonstrated by an increased colonic crypt length and a significant CD4(+) T cell infiltrate. Further, we established that the Am80-driven crypt hyperplasia and CD4(+) T cell infiltrate were dependent on IL-6, as both were absent in Am80-treated IL-6 knock-out mice. CONCLUSIONS: This study presents novel data showing a pro-inflammatory role of RAR ligands in T. muris infection, and implies an undesirable effect for the administration of vitamin A during chronic helminth infection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10875-013-9936-8) contains supplementary material, which is available to authorized users

Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review.

This is the final version. Available from Nature Research via the DOI in this record. Data availability: All studies reviewed were identified and can be accessed via publicly available databases (PubMed and Embase). Source data can be found in Supplementary Data 3. A full list of included studies is available in Supplementary Data 6. Article review data supporting the findings of this study are available upon reasonable request from the corresponding author.BACKGROUND: Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies. METHODS: We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment. RESULTS: Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation. CONCLUSIONS: Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.DiabDocs K12 programLeona M. & Harry B. Helmsley Charitable TrustNIH NIDDKDiabetes UK Harry Keen FellowshipNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of HealthNational Institute of HealthNational Institute of Healt

Computer-based tools for decision support in agroforestry: Current state and future needs

Successful design of agroforestry practices hinges on the ability to pull together very diverse and sometimes large sets of information (i.e., biophysical, economic and social factors), and then implementing the synthesis of this information across several spatial scales from site to landscape. Agroforestry, by its very nature, creates complex systems with impacts ranging from the site or practice level up to the landscape and beyond. Computer-based Decision Support Tools (DST) help to integrate information to facilitate the decision-making process that directs development, acceptance, adoption, and management aspects in agroforestry. Computer-based DSTs include databases, geographical information systems, models, knowledge-base or expert systems, and ‘hybrid’ decision support systems. These different DSTs and their applications in agroforestry research and development are described in this paper. Although agroforestry lacks the large research foundation of its agriculture and forestry counterparts, the development and use of computer-based tools in agroforestry have been substantial and are projected to increase as the recognition of the productive and protective (service) roles of these tree-based practices expands. The utility of these and future tools for decision-support in agroforestry must take into account the limits of our current scientific information, the diversity of aspects (i.e. economic, social, and biophysical) that must be incorporated into the planning and design process, and, most importantly, who the end-user of the tools will be. Incorporating these tools into the design and planning process will enhance the capability of agroforestry to simultaneously achieve environmental protection and agricultural production goals