Helminth burden and ecological factors associated with alterations in wild host gastrointestinal microbiota

Infection by gastrointestinal helminths of humans, livestock and wild animals is common, but the impact of such endoparasites on wild hosts and their gut microbiota represents an important overlooked component of population dynamics. Wild host gut microbiota and endoparasites occupy the same physical niche spaces with both affecting host nutrition and health. However, associations between the two are poorly understood. Here we used the commonly parasitized European shag (Phalacrocorax aristotelis) as a model wild host. Forty live adults from the same colony were sampled. Endoscopy was employed to quantify helminth infection in situ. Microbiota from the significantly distinct proventriculus (site of infection), cloacal and faecal gastrointestinal tract microbiomes were characterised using 16S rRNA gene-targeted high-throughput sequencing. We found increasingly strong associations between helminth infection and microbiota composition progressing away from the site of infection, observing a pronounced dysbiosis in microbiota when samples were partitioned into high- and low-burden groups. We posit this dysbiosis is predominately explained by helminths inducing an anti-inflammatory environment in the proventriculus, diverting host immune responses away from themselves. This study, within live wild animals, provides a vital foundation to better understand the mechanisms that underpin the three-way relationship between helminths, microbiota and hosts

Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design

This paper reviews past and ongoing efforts in using high-throughput ab-inito calculations in combination with machine learning models for materials design. The primary focus is on bulk materials, i.e., materials with fixed, ordered, crystal structures, although the methods naturally extend into more complicated configurations. Efficient and robust computational methods, computational power, and reliable methods for automated database-driven high-throughput computation are combined to produce high-quality data sets. This data can be used to train machine learning models for predicting the stability of bulk materials and their properties. The underlying computational methods and the tools for automated calculations are discussed in some detail. Various machine learning models and, in particular, descriptors for general use in materials design are also covered.Comment: 19 pages, 2 figure

Impact of patient characteristics, education and knowledge on emergency room visits in patients with asthma and COPD: a descriptive and correlative study

<p>Abstract</p> <p>Background</p> <p>Asthma and COPD are major health problems and an extensive burden for the patient and the health care system. Patient education has been recommended, but the influence on knowledge and health outcomes is not fully examined. Our aims were to compare patient characteristics, education and knowledge in patients who had an emergency room (ER) visit, to explore factors related to disease knowledge, and to investigate patient characteristics, patient education and knowledge in relation to further ER visits over a 12 month period.</p> <p>Methods</p> <p>Eighty-four patients with asthma and 52 with COPD, who had had an ER visit, were included. They were interviewed by telephone 4 to 6 weeks after the ER visit and followed for a year.</p> <p>Results</p> <p>Patients with COPD were older, more sedentary, had had more ER visits the previous year, and had more co morbidity than patients with asthma. About 80% of the patients had received information from health professionals or participated in education/rehabilitation, but a minority (< 20%) reported that their knowledge about how to handle the disease was good. Patients with "good knowledge" were younger, were more likely to have asthma diagnose, and had a higher educational background (p < 0.05). Sixty-seven percent of the patients with COPD had repeated ER visits during the following year versus 42% in asthma (p < 0.05) (adjusted HRR: 1.73 (1.03-2.90)). Patients who had had ER visits the year before inclusion had a higher risk of ER visits the following year (adjusted HRR: 3.83 (1.99-7.38)). There were no significant differences regarding patient education and knowledge between the group with and without further ER visits after adjusting for sex, diagnose, age, and educational background.</p> <p>Conclusion</p> <p>Patients with asthma had a better self reported knowledge of disease management and were less likely to have new exacerbations than patients with COPD. Reported level of knowledge was, however, in it self not a predictor of exacerbations. This indicates that information is not sufficient to reduce the burden of disease. Patient education focused on self-management and behavioral change should be emphasized.</p

The Pathogenesis of Extraintestinal Manifestations: Implications for IBD Research, Diagnosis, and Therapy.

This is a pre-copyedited, author-produced version of an article accepted for publication in Journal of Crohn's and Colitis following peer review. The version of record Hedin, C. R. H., et al. (2018). "The Pathogenesis of Extraintestinal Manifestations: Implications for IBD research, diagnosis and therapy." Journal of Crohn's and Colitis: jjy191-jjy191.] is available online at:https://doi.org/10.1093/ecco-jcc/jjy191This article reports on the sixth scientific workshop of the European Crohn's and Colitis Organisation [ECCO] on the pathogenesis of extraintestinal manifestations [EIMs] in inflammatory bowel disease [IBD]. This paper has been drafted by 15 ECCO members and 6 external experts [in rheumatology, dermatology, ophthalmology, and immunology] from 10 European countries and the USA. Within the workshop, contributors formed subgroups to address specific areas. Following a comprehensive literature search, the supporting text was finalized under the leadership of the heads of the working groups before being integrated by the group consensus leaders

The role of tenascin-C in tissue injury and tumorigenesis

The extracellular matrix molecule tenascin-C is highly expressed during embryonic development, tissue repair and in pathological situations such as chronic inflammation and cancer. Tenascin-C interacts with several other extracellular matrix molecules and cell-surface receptors, thus affecting tissue architecture, tissue resilience and cell responses. Tenascin-C modulates cell migration, proliferation and cellular signaling through induction of pro-inflammatory cytokines and oncogenic signaling molecules amongst other mechanisms. Given the causal role of inflammation in cancer progression, common mechanisms might be controlled by tenascin-C during both events. Drugs targeting the expression or function of tenascin-C or the tenascin-C protein itself are currently being developed and some drugs have already reached advanced clinical trials. This generates hope that increased knowledge about tenascin-C will further improve management of diseases with high tenascin-C expression such as chronic inflammation, heart failure, artheriosclerosis and cancer