From gut dysbiosis to altered brain function and mental illness: mechanisms and pathways

The human body hosts an enormous abundance and diversity of microbes, which perform a range of essential and beneficial functions. Our appreciation of the importance of these microbial communities to many aspects of human physiology has grown dramatically in recent years. We know, for example, that animals raised in a germ-free environment exhibit substantially altered immune and metabolic function, while the disruption of commensal microbiota in humans is associated with the development of a growing number of diseases. Evidence is now emerging that, through interactions with the gut-brain axis, the bidirectional communication system between the central nervous system and the gastrointestinal tract, the gut microbiome can also influence neural development, cognition and behaviour, with recent evidence that changes in behaviour alter gut microbiota composition, while modifications of the microbiome can induce depressive-like behaviours. Although an association between enteropathy and certain psychiatric conditions has long been recognized, it now appears that gut microbes represent direct mediators of psychopathology. Here, we examine roles of gut microbiome in shaping brain development and neurological function, and the mechanisms by which it can contribute to mental illness. Further, we discuss how the insight provided by this new and exciting field of research can inform care and provide a basis for the design of novel, microbiota-targeted, therapies.GB Rogers, DJ Keating, RL Young, M-L Wong, J Licinio, and S Wesseling

Microbiome to Brain:Unravelling the Multidirectional Axes of Communication

The gut microbiome plays a crucial role in host physiology. Disruption of its community structure and function can have wide-ranging effects making it critical to understand exactly how the interactive dialogue between the host and its microbiota is regulated to maintain homeostasis. An array of multidirectional signalling molecules is clearly involved in the host-microbiome communication. This interactive signalling not only impacts the gastrointestinal tract, where the majority of microbiota resides, but also extends to affect other host systems including the brain and liver as well as the microbiome itself. Understanding the mechanistic principles of this inter-kingdom signalling is fundamental to unravelling how our supraorganism function to maintain wellbeing, subsequently opening up new avenues for microbiome manipulation to favour desirable mental health outcome

Prediction of the pre-morbid 3D anatomy of the proximal humerus based on statistical shape modelling

International audienceAIMS: Restoring the pre-morbid anatomy of the proximal humerus is a goal of anatomical shoulder arthroplasty, but reliance is placed on the surgeon's experience and on anatomical estimations. The purpose of this study was to present a novel method, 'Statistical Shape Modelling', which accurately predicts the pre-morbid proximal humeral anatomy and calculates the 3D geometric parameters needed to restore normal anatomy in patients with severe degenerative osteoarthritis or a fracture of the proximal humerus. MATERIALS AND METHODS: From a database of 57 humeral CT scans 3D humeral reconstructions were manually created. The reconstructions were used to construct a statistical shape model (SSM), which was then tested on a second set of 52 scans. For each humerus in the second set, 3D reconstructions of four diaphyseal segments of varying lengths were created. These reconstructions were chosen to mimic severe osteoarthritis, a fracture of the surgical neck of the humerus and a proximal humeral fracture with diaphyseal extension. The SSM was then applied to the diaphyseal segments to see how well it predicted proximal morphology, using the actual proximal humeral morphology for comparison. RESULTS: With the metaphysis included, mimicking osteoarthritis, the errors of prediction for retroversion, inclination, height, radius of curvature and posterior and medial offset of the head of the humerus were 2.9° (± 2.3°), 4.0° (± 3.3°), 1.0 mm (± 0.8 mm), 0.8 mm (± 0.6 mm), 0.7 mm (± 0.5 mm) and 1.0 mm (± 0.7 mm), respectively. With the metaphysis excluded, mimicking a fracture of the surgical neck, the errors of prediction for retroversion, inclination, height, radius of curvature and posterior and medial offset of the head of the humerus were 3.8° (± 2.9°), 3.9° (± 3.4°), 2.4 mm (± 1.9 mm), 1.3 mm (± 0.9 mm), 0.8 mm (± 0.5 mm) and 0.9 mm (± 0.6 mm), respectively. CONCLUSION: This study reports a novel, computerised method that accurately predicts the pre-morbid proximal humeral anatomy even in challenging situations. This information can be used in the surgical planning and operative reconstruction of patients with severe degenerative osteoarthritis or with a fracture of the proximal humerus

Derivation and validation of a diagnostic test for irritable bowel syndrome using latent class analysis

BACKGROUND: The accuracy of symptom-based diagnostic criteria for irritable bowel syndrome (IBS) is modest. AIMS: To derive and validate a new test that utilises latent class analysis. METHODS: Symptom, colonoscopy, and histology data were collected from 1981 patients and 360 patients in two cohorts referred to secondary care for investigation of their gastrointestinal symptoms in Canada and the UK, respectively. Latent class analysis was used to identify naturally occurring clusters in patient-reported symptoms in the Canadian dataset, and the latent class model derived from this was then applied to the UK dataset in order to validate it. Sensitivity, specificity, and positive and negative likelihood ratios (LRs) were calculated for the latent class models. RESULTS: In the Canadian cohort, the model had a sensitivity of 44.7% (95% CI 40.0-50.0) and a specificity of 85.3% (95% CI 83.4-87.0). Positive and negative LRs were 3.03 (95% CI 2.57-3.56) and 0.65 (95% CI 0.59-0.71) respectively. A maximum positive LR of 3.93 was achieved following construction of a receiver operating characteristic curve. The performance in the UK cohort was similar, with a sensitivity and specificity of 52.5% (95% CI 42.2-62.7) and 84.3% (95% CI 79.3-88.6), respectively. Positive and negative LRs were 3.35 (95% CI 2.38-4.70) and 0.56 (95% CI 0.45-0.68), respectively, with a maximum positive LR of 4.15. CONCLUSIONS: A diagnostic test for IBS, utilising patient-reported symptoms incorporated into a latent class model, performs as accurately as symptom-based criteria. It has potential for improvement via addition of clinical markers, such as coeliac serology and faecal calprotectin