Cognitive behaviour therapy response and dropout rate across purging and nonpurging bulimia nervosa and binge eating disorder : DSM-5 implications

Background: With the imminent publication of the new edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), there has been a growing interest in the study of the boundaries across the three bulimic spectrum syndromes [bulimia nervosa-purging type (BN-P), bulimia nervosa-non purging type (BN-NP) and binge eating disorder (BED)]. Therefore, the aims of this study were to determine differences in treatment response and dropout rates following Cognitive Behavioural Therapy (CBT) across the three bulimic-spectrum syndromes. Method: The sample comprised of 454 females (87 BED, 327 BN-P and 40 BN-NP) diagnosed according to DSM-IV-TR criteria who were treated with 22 weekly outpatient sessions of group CBT therapy. Patients were assessed before and after treatment using a food and binging/purging diary and some clinical questionnaires in the field of ED. "Full remission" was defined as total absence of binging and purging (laxatives and/or vomiting) behaviors and psychological improvement for at least 4 (consecutive). Results: Full remission rate was found to be significantly higher in BED (69.5%) than in both BN-P (p < 0.005) and BN-NP (p < 0.001), which presented no significant differences between them (30.9% and 35.5%). The rate of dropout from group CBT was also higher in BED (33.7%) than in BN-P (p < 0.001) and BN-NP (p < 0.05), which were similar (15.4% and 12.8%, respectively). Conclusions: Results suggest that purging and non-purging BN have similar treatment response and dropping out rates, whereas BED appears as a separate diagnosis with better outcome for those who complete treatment. The results support the proposed new DSM-5 classification

Rate and duration of hospitalisation for acute pulmonary embolism in the real-world clinical practice of different countries : Analysis from the RIETE registry

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Bio-oil upgrading in supercritical water using Ni-Co catalysts supported on carbon nanofibres

This work addresses the preparation, characterisation and screening of different Ni-Co catalysts supported on carbon nanofibres (CNFs) for use in the upgrading of bio-oil in supercritical water. The aim is to improve the physicochemical properties of bio-oil so that it can be used as a fuel. The CNFs were firstly oxidised in HNO3 and afterwards subjected to a thermal treatment to selectively modify their surface chemistry prior to the incorporation of the metal active phase (Ni-Co). The CNFs and the supported catalysts were thoroughly characterised by several techniques, which allowed a relationship to be established between the catalyst properties and the upgrading results. The use of Ni-Co/CNFs for bio-oil upgrading in supercritical water (SCW) significantly improved the properties of the original feedstock. In addition, the thermal treatment to which the fibres were subjected exerted a significant influence on their catalytic properties. An increase in the severity of the thermal treatment led to a substantial reduction in the oxygen content of the CNFs, mainly due to the removal of the less stable oxygen surface groups, which allowed their surface polarity to decrease. This decrease resulted in less formation of solid products. However, it also reduced the H/C and increased the O/C ratios of the upgraded liquid. Therefore, a compromise between the yield and the properties of the upgraded bio-oil was achieved with a Ni-Co supported on a CNF with a moderate amount of oxygen surface groups

Integration of Multi-omics Data for Expression Quantitative Trait Loci (eQTL) Analysis and eQTL Epistasis

Expression quantitative trait loci (eQTL) mapping studies identify genetic loci that regulate gene expression. eQTL mapping studies can capture gene regulatory interactions and provide insight into the genetic mechanism of biological systems. Recently, the integration of multi-omics data, such as single-nucleotide polymorphisms (SNPs), copy number variations (CNVs), DNA methylation, and gene expression, plays an important role in elucidating complex biological systems, since biological systems involve a sequence of complex interactions between various biological processes. This chapter introduces multi-omics data that have been used in many eQTL studies and integrative methodologies that incorporate multi-omics data for eQTL studies. Furthermore, we describe a statistical approach that can detect nonlinear causal relationships between eQTLs, called eQTL epistasis, and its importance