Can Brief Mindfulness-Based Intervention Improve Attention in Individuals With Mixed Neurological Disorders?

It is estimated that there are 12.5 million people in England living with neurological disorders (Neurological Alliance, 2014). People with neurological disorders as a result of acquired brain injury (ABI) are living with short and long-term disabilities. These include cognitive impairment, and physical and emotional distress. One of the most common complaints by individuals who have ABI is attention impairment. Attention difficulties can have serious ramifications for daily functioning. Although studies have explored the effects of evidence-based interventions such as mindfulness-based therapy on attention abilities, and have found that it improves individuals’ attention skills (Moore et al, 2012), thus far research has been conducted mainly with non-clinical populations. This study set out to investigate whether a mindfulness-based intervention could prove beneficial for people with neurological disorders, particularly whether it could positively impact on attention impairment. The study employed a one group pre-test post-test design. The intervention was adapted from the MBSR programme developed by Kabat-Zinn. Twenty-two participants with ABI were recruited. The Conners Continuous Performance Test 3rd Edition (CPT-3), Mindful Attention Awareness Scale (MAAS), Attention Process Training-II Attention Questionnaire (APT-II AQ) and Clinical Outcome in Routine Evaluation-Outcome Measure (CORE-OM) were utilised to measure outcomes. The results revealed that there was a clinical improvement in self reported measures of mindfulness (MAAS) (Cohen d=0.28), attention (APT-II AQ) (Cohen d=0.33), and psychological distress (CORE-OM) (Cohen d=0.72). This was not observed using the neuropsychological test of attention (CPT-3) for overall group scores, but further evaluation showed some individuals’ scores improved. The study is promising as it indicates that mindfulness based treatment can be effective with attentional problems as well as in reducing psychological distress for individuals with ABI. This could be valuable in terms of providing treatment for this client group and adds to the expanding research base on mindfulness-based intervention with this population

Exile Vol. XI No. 2

FICTION Don\u27t Break Bread with Strangers by Thomas Getz 5-12 Story by Kathy Swiger 17-20 The Last of the Flag Pole Sitters 25-29 POETRY Muttering: A Letter by Gretchen Schenk 14-15 Poem by Jane Pearson 20 Marina in March by Karen Combs 21 of the pope too slow in recovery by Bill C. West Jr. 22 Poem by Jim Nichols 23 Poem by Judy Strange 23 Haiku by Bonnie Bishop 29 ART Pen and Ink by Dan Thaxton 4 Linocut by Richard Machlan 13 Etching by Lynne Wiley 16 Woodcut by Lela Giles 22 Charcoal by Carol Kubie 2

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease

Crohn's disease and ulcerative colitis, the two common forms of inflammatory bowel disease (IBD), affect over 2.5 million people of European ancestry, with rising prevalence in other populations. Genome-wide association studies and subsequent meta-analyses of these two diseases as separate phenotypes have implicated previously unsuspected mechanisms, such as autophagy, in their pathogenesis and showed that some IBD loci are shared with other inflammatory diseases. Here we expand on the knowledge of relevant pathways by undertaking a meta-analysis of Crohn's disease and ulcerative colitis genome-wide association scans, followed by extensive validation of significant findings, with a combined total of more than 75,000 cases and controls. We identify 71 new associations, for a total of 163 IBD loci, that meet genome-wide significance thresholds. Most loci contribute to both phenotypes, and both directional (consistently favouring one allele over the course of human history) and balancing (favouring the retention of both alleles within populations) selection effects are evident. Many IBD loci are also implicated in other immune-mediated disorders, most notably with ankylosing spondylitis and psoriasis. We also observe considerable overlap between susceptibility loci for IBD and mycobacterial infection. Gene co-expression network analysis emphasizes this relationship, with pathways shared between host responses to mycobacteria and those predisposing to IBD