105 research outputs found

    Long-term impact of the low-FODMAP diet on gastrointestinal symptoms, dietary intake, patient acceptability, and healthcare utilization in irritable bowel syndrome

    Get PDF
    Background: The low-FODMAP diet is a frequently used treatment for irritable bowel syndrome (IBS). Most research has focused on short-term FODMAP restriction; however, guidelines recommend that high-FODMAP foods are reintroduced to individual tolerance. This study aimed to assess the long-term effectiveness of the low-FODMAP diet following FODMAP reintroduction in IBS patients. Methods: Patients with IBS were prospectively recruited to a questionnaire study following completion of dietitian-led low-FODMAP education. At baseline and following FODMAP restriction (short term) only, gastrointestinal symptoms were measured as part of routine clinical care. Following FODMAP reintroduction, (long term), symptoms, dietary intake, acceptability, food-related quality of life (QOL), and healthcare utilization were assessed. Data were reported for patients who continued long-term FODMAP restriction (adapted FODMAP) and/or returned to a habitual diet (habitual). Key Results: Of 103 patients, satisfactory relief of symptoms was reported in 12% at baseline, 61% at short-term follow-up, and 57% at long-term follow-up. At long-term follow-up, 84 (82%) patients continued an ‘adapted FODMAP’ diet (total FODMAP intake mean 20.6, SD 14.9\ua0g/d) compared with 19 (18%) of patients following a ‘habitual’ diet (29.4, SD 22.9\ua0g/d, P=.039). Nutritional adequacy was not compromised for either group. The ‘adapted FODMAP’ group reported the diet cost significantly more than the ‘habitual’ group (

    From QTL to QTN: Candidate Gene Set Approach and a Case Study in Porcine IGF1-FoxO Pathway

    Get PDF
    <div><p>Unraveling the genetic background of economic traits is a major goal in modern animal genetics and breeding. Both candidate gene analysis and QTL mapping have previously been used for identifying genes and chromosome regions related to studied traits. However, most of these studies may be limited in their ability to fully consider how multiple genetic factors may influence a particular phenotype of interest. If possible, taking advantage of the combined effect of multiple genetic factors is expected to be more powerful than analyzing single sites, as the joint action of multiple loci within a gene or across multiple genes acting in the same gene set will likely have a greater influence on phenotypic variation. Thus, we proposed a pipeline of gene set analysis that utilized information from multiple loci to improve statistical power. We assessed the performance of this approach by both simulated and a real IGF1-FoxO pathway data set. The results showed that our new method can identify the association between genetic variation and phenotypic variation with higher statistical power and unravel the mechanisms of complex traits in a point of gene set. Additionally, the proposed pipeline is flexible to be extended to model complex genetic structures that include the interactions between different gene sets and between gene sets and environments.</p> </div

    Statistical powers between CGSA and candidate gene approach under different heritability levels.

    No full text
    <p>The power was examined on a trait simulated from 15 causative mutations (QTNs). A total of 1000 replications were conducted for each method. The heritability of the trait varied from 0 to 0.5. The differences between our CGSA and candidate gene set approach with mid-level and low-level of heritability were greater than the one with high heritability.</p

    Co-localization of QTL map from Pig QTLdb and candidate gene (FOXO3) from IGF1/FoxO pathway.

    No full text
    <p>Co-localization of QTL map from Pig QTLdb and candidate gene (FOXO3) from IGF1/FoxO pathway.</p

    The comparison of orthologs members between pig and murine IGF1/FoxO pathway.

    No full text
    <p>The comparison of orthologs members between pig and murine IGF1/FoxO pathway.</p

    Additional file 1 of Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network

    No full text
    Supplementary figures and tables. Figure S1. The AUCs of precision recall curves of our proposed method when the number of dimensions K increases. Figure S2. Performance comparison of our proposed method and existing network-based methods, evaluated by IntOGen list. Figure S3. Performance of our proposed method when the parameters for sparseness (or robustness) are fixed and the parameters for prior knowledge varies, where λ RV , λ LV and λ RU are fixed and λ LU varies. Figure S4. Performance of our proposed method when the parameters for sparseness (or robustness) are fixed and the parameters for prior knowledge varies, where λ RU , λ LU and λ RV are fixed and λ LV varies. Figure S5. Performance comparison of our proposed method and existing network-based methods, applied on GBM, COADREAD and BRCA datasets and evaluated by IntOGen list. Figure S6. Performance comparison of our proposed method and existing network-based methods, applied on KIRC, THCA and PRAD datasets and evaluated by CGC list. Figure S7. Performance comparison of our proposed method and existing network-based methods, applied on KIRC, THCA and PRAD datasets and evaluated by IntOGen list. Figure S8. Performance comparison of our proposed method and existing network-based methods with network information from both iRefIndex and String v10. Table S1. Fisher’s exact test on the top scored candidates of BRCA results for CGC and IntOGen benchmarking genes. Table S2. Fisher’s exact test on the top scored candidates of GBM results for CGC and IntOGen benchmarking genes. Table S3. The full list of the top 200 genes detected by our model on GBM dataset. Table S4. The full list of the top 200 genes detected by our model on COADREAD dataset. Table S5. The full list of the top 200 genes detected by our model on BRCA dataset. Table S6. Functional enrichment analysis results for KEGG pathways of the top 200 genes of the proposed method on COADREAD dataset. Table S7. Functional enrichment analysis results for KEGG pathways of the top 200 genes of the proposed method on BRCA dataset. (PDF 5670kb

    Minimizing Defects in Polymer-Based Langmuir–Blodgett Monolayers and Bilayers via Gluing

    No full text
    Polymeric surfactants were prepared by quaternization of poly­(4-chloromethylstyrene) with <i>N</i>,<i>N</i>-dimethyl-<i>N</i>-<i>n</i>-dodecylamine and <i>N</i>,<i>N</i>-dimethyl-<i>N</i>-<i>n</i>-octylamine to give <b>1</b> and <b>2</b>, respectively. Each of these polymers formed stable monolayers at the air/water interface. Injection of poly­(acrylic acid) (PAA) beneath the surface of these films led to a substantial increase in their cohesiveness (i.e., “gluing”), as evidenced by a dramatic increase in their surface viscosity. Examination of monolayers of <b>1</b> by atomic force microscopy, after being transferred to silicon wafers that were surface-modified with <i>n</i>-octadecyltrichlorosilane, showed that the presence of PAA leads to intact film. In contrast, transfer of unglued monolayers resulted in poor coverage. Comparison of the barrier properties of single glued and unglued LB bilayers formed in the presence and in the absence of PAA have shown that PAA minimizes defect formation within these ultrathin assemblies
    corecore