Predictors of Gastrointestinal Transit Times in Colon Capsule Endoscopy

Optimizing the accuracy of colon capsule endoscopy (CCE) requires high completion rates. To prevent incomplete CCE, we aimed to identify predictors associated with slow CCE transit times. METHODS: In this population-based study, participants received CCE with a split-dose polyethylene glycol bowel preparation and booster regimen (0.5 L oral sulfate solution and 10 mg metoclopramide if capsule remained in stomach for > 1 hour). The following predictors were assessed: age, sex, body mass index (BMI), smoking, coffee and fiber intake, diet quality, physical activity, dyspeptic complaints, stool pattern, history of abdominal surgery, medication use, and CCE findings. Multivariable logistic and linear regressions with backward elimination were performed. RESULTS: We analyzed 451 CCE procedures with a completion rate of 51.9%. The completion rate was higher among older participants (odds ratio [OR] 1.54, 95% confidence interval [CI] 1.04–2.28, P = 0.03) and participants with a changed stool pattern (OR 2.27, 95% CI 1.20–4.30, P = 0.01). Participants with a history of abdominal surgery had a lower completion rate (OR 0.54, 95% CI 0.36–0.80, P = 0.003). Participants with higher BMI had faster stomach, small bowel, and total transit times (β = −0.10, P = 0.01; β = −0.14, P = 0.001; β = −0.12, P = 0.01). A faster small bowel transit was found in participants with a changed stool pattern (β = −0.08, P = 0.049) and the use of metoclopramide (β = −0.14, P = 0.001). Participants with high fiber intake had a slower colonic transit (β = 0.11, P = 0.03). DISCUSSION: Younger age, unchanged stool pattern, history of abdominal surgery, low BMI, and high fiber intake resulted in slower CCE transit times and lower completion rates. In future practice, these factors can be considered to adjust preparation protocols

Pre-service School Professionals\u27 Knowledge of Speech-Language Pathologists\u27 Literacy Practices

Purpose: The purpose of this study is to examine school-based pre-service professionals’ knowledge of speech-language pathologists’ (SLPs) literacy assessment and intervention practices in K-12 students before and following participation in an interprofessional education (IPE) workshop. Methods: A pre-/post-workshop survey of school-based SLP’s literacy practices will be distributed to the attendees of the IPE workshop. Participation is voluntary and anonymous. Descriptive statistics will be analyzed and reported. Originality: A growing body of literature suggests that collaborative interprofessional practice (IPP) is more likely to be successfully conducted when professionals have participated in IPE experiences when they were enrolled in their pre-service professional training programs. In particular, knowledge of the roles, responsibilities, and scope of practice of the other professionals with whom they will interact has been identified as a significant predictor of successful IPP. Significance: Results of this study will provide preliminary data of the effectiveness of an interprofessional education (IPE) workshop with respect to informing school-based pre-service professionals on the scope of the school-based SLP’s practice in literacy assessment and intervention. This is significant in that while there are numerous studies of IPE practices in medical-based fields, such as nursing and pharmacy, few such studies exist that examine the IPE experiences of school-based pre-service professionals

Deciphering the virulence potential of Listeria monocytogenes in the Norwegian meat and salmon processing industry by combining whole genome sequencing and in vitro data

publishedVersio

The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes

The genomic landscape of breast cancer is complex, and inter- and intra-tumour heterogeneity are important challenges in treating the disease. In this study, we sequence 173 genes in 2,433 primary breast tumours that have copy number aberration (CNA), gene expression and long-term clinical follow-up data. We identify 40 mutation-driver (Mut-driver) genes, and determine associations between mutations, driver CNA profiles, clinical-pathological parameters and survival. We assess the clonal states of Mut-driver mutations, and estimate levels of intra-tumour heterogeneity using mutant-allele fractions. Associations between PIK3CA mutations and reduced survival are identified in three subgroups of ER-positive cancer (defined by amplification of 17q23, 11q13-14 or 8q24). High levels of intra-tumour heterogeneity are in general associated with a worse outcome, but highly aggressive tumours with 11q13-14 amplification have low levels of intra-tumour heterogeneity. These results emphasize the importance of genome-based stratification of breast cancer, and have important implications for designing therapeutic strategies.The METABRIC project was funded by Cancer Research UK, the British Columbia Cancer Foundation and Canadian Breast Cancer Foundation BC/Yukon. This sequencing project was funded by CRUK grant C507/A16278 and Illumina UK performed all the sequencing. The authors also acknowledge the support of the University of Cambridge, Hutchinson Whampoa, the NIHR Cambridge Biomedical Research Centre, the Cambridge Experimental Cancer Medicine Centre, the Centre for Translational Genomics (CTAG) Vancouver and the BCCA Breast Cancer Outcomes Unit. We thank the Genomics, Histopathology, and Biorepository Core Facilities at the Cancer Research UK Cambridge Institute, and the Addenbrooke’s Human Research Tissue Bank (supported by the National Institute for Health Research Cambridge Biomedical Research Centre).This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms1147

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource

A tumor DNA complex aberration index is an independent predictor of survival in breast and ovarian cancer

Complex focal chromosomal rearrangements in cancer genomes, also called "firestorms", can be scored from DNA copy number data. The complex arm-wise aberration index (CAAI) is a score that captures DNA copy number alterations that appear as focal complex events in tumors, and has potential prognostic value in breast cancer. This study aimed to validate this DNA-based prognostic index in breast cancer and test for the first time its potential prognostic value in ovarian cancer. Copy number alteration (CNA) data from 1950 breast carcinomas (METABRIC cohort) and 508 high-grade serous ovarian carcinomas (TCGA dataset) were analyzed. Cases were classified as CAAI positive if at least one complex focal event was scored. Complex alterations were frequently localized on chromosome 8p (n = 159), 17q (n = 176) and 11q (n = 251). CAAI events on 11q were most frequent in estrogen receptor positive (ER+) cases and on 17q in estrogen receptor negative (ER) cases. We found only a modest correlation between CAAI and the overall rate of genomic instability (GII) and number of breakpoints (r = 0.27 and r = 0.42, p <0.001). Breast cancer specific survival (BCSS), overall survival (OS) and ovarian cancer progression free survival (PUS) were used as clinical end points in Cox proportional hazard model survival analyses. CAAI positive breast cancers (43%) had higher mortality: hazard ratio (HR) of 1.94 (95%CI, 1.62-2.32) for BCSS, and of 1.49 (95%CI, 1.30-1.71) for OS. Representations of the 70-gene and the 21-gene predictors were compared with CAAI in multivariable models and CAAI was independently significant with a Cox adjusted HR of 1.56 (95%CI, 1.23-1.99) for ER+ and 1.55 (95%CI, 1.11-2.18) for ER disease. None of the expression-based predictors were prognostic in the ER subset. We found that a model including CAM and the two expression-based prognostic signatures outperformed a model including the 21-gene and 70-gene signatures but excluding CAAL Inclusion of CAAI in the clinical prognostication tool PREDICT significantly improved its performance. CAAI positive ovarian cancers (52%) also had worse prognosis: HRs of 1.3 (95%CI, 1.1-1.7) for PFS and 1.3 (95%CI, 1.1-1.6) for OS. This study validates CAM as an independent predictor of survival in both ER+ and ER breast cancer and reveals a significant prognostic value for CAAI in high-grade serous ovarian cancer. (C) 2014 The Authors. Published by Elsevier B.V. on behalf of Federation of European Biochemical Societies. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).Publisher PDFPeer reviewe

The temporomandibular joint in juvenile idiopathic arthritis: frequently used and frequently arthritic

Recent recognition of the markedly high prevalence of temporomandibular joint (TMJ) arthritis in children with juvenile idiopathic arthritis (JIA) coupled with the significant morbidity associated with TMJ damage has prompted increased interest in both the clinical and pathological aspects of TMJ arthritis. This review focuses on the prevalence of TMJ arthritis in JIA, the imaging modalities used to detect TMJ arthritis, and the treatment of TMJ arthritis in children with JIA

Loss of Sex and Age Driven Differences in the Gut Microbiome Characterize Arthritis-Susceptible *0401 Mice but Not Arthritis-Resistant *0402 Mice

<div><h3>Background</h3><p>HLA-DRB1*0401 is associated with susceptibility, while HLA-DRB1*0402 is associated with resistance to developing rheumatoid arthritis (RA) and collagen-induced arthritis in humans and transgenic mice respectively. The influence of gut-joint axis has been suggested in RA, though not yet proven.</p> <h3>Methodology/Principal Findings</h3><p>We have used HLA transgenic mice carrying arthritis susceptible and -resistant HLA-DR genes to explore if genetic factors and their interaction with gut flora gut can be used to predict susceptibility to develop arthritis. Pyrosequencing of the 16S rRNA gene from the fecal microbiomes of DRB1*0401 and DRB1*0402 transgenic mice revealed that the guts of *0401 mice is dominated by a Clostridium-like bacterium, whereas the guts of *0402 mice are enriched for members of the <em>Porphyromonadaceae</em> family and <em>Bifidobacteria</em>. DRB1*0402 mice harbor a dynamic sex and age-influenced gut microbiome while DRB1*0401 mice did not show age and sex differences in gut microbiome even though they had altered gut permeability. Cytokine transcripts, measured by rtPCR, in jejuna showed differential TH17 regulatory network gene transcripts in *0401 and *0402 mice.</p> <h3>Conclusions/Significance</h3><p>We have demonstrated for the first time that HLA genes in association with the gut microbiome may determine the immune environment and that the gut microbiome might be a potential biomarker as well as contributor for susceptibility to arthritis. Identification of pathogenic commensal bacteria would provide new understanding of disease pathogenesis, thereby leading to novel approaches for therapy.</p> </div

Genome-wide association study of placental weight identifies distinct and shared genetic influences between placental and fetal growth

A well-functioning placenta is essential for fetal and maternal health throughout pregnancy. Using placental weight as a proxy for placental growth, we report genome-wide association analyses in the fetal (n = 65,405), maternal (n = 61,228) and paternal (n = 52,392) genomes, yielding 40 independent association signals. Twenty-six signals are classified as fetal, four maternal and three fetal and maternal. A maternal parent-of-origin effect is seen near KCNQ1. Genetic correlation and colocalization analyses reveal overlap with birth weight genetics, but 12 loci are classified as predominantly or only affecting placental weight, with connections to placental development and morphology, and transport of antibodies and amino acids. Mendelian randomization analyses indicate that fetal genetically mediated higher placental weight is causally associated with preeclampsia risk and shorter gestational duration. Moreover, these analyses support the role of fetal insulin in regulating placental weight, providing a key link between fetal and placental growth