Systematic review with meta-analysis: neuroimaging in hepatitis C chronic infection.

BACKGROUND: Chronic hepatitis C is considered a systemic disease because of extra-hepatic manifestations. Neuroimaging has been employed in hepatitis C virus-infected patients to find in vivo evidence of central nervous system alterations. AIMS: Systematic review and meta-analysis of neuroimaging research in chronic hepatitis C treatment naive patients, or patients previously treated without sustained viral response, to study structural and functional brain impact of hepatitis C. METHODS: Using PRISMA guidelines a database search was conducted from inception up until 1 May 2017 for peer-reviewed studies on structural or functional neuroimaging assessment of chronic hepatitis C patients without cirrhosis or encephalopathy, with control group. Meta-analyses were performed when possible. RESULTS: The final sample comprised 25 studies (magnetic resonance spectroscopy [N = 12], perfusion weighted imaging [N = 1], positron emission tomography [N = 3], single-photon emission computed tomography [N = 4], functional connectivity in resting state [N = 1], diffusion tensor imaging [N = 2] and structural magnetic resonance imaging [N = 2]). The whole sample was of 509 chronic hepatitis C patients, with an average age of 41.5 years old and mild liver disease. A meta-analysis of magnetic resonance spectroscopy studies showed increased levels of choline/creatine ratio (mean difference [MD] 0.12, 95% confidence interval [CI] 0.06-0.18), creatine (MD 0.85, 95% CI 0.42-1.27) and glutamate plus glutamine (MD 1.67, 95% CI 0.39-2.96) in basal ganglia and increased levels of choline/creatine ratio in centrum semiovale white matter (MD 0.13, 95% CI 0.07-0.19) in chronic hepatitis C patients compared with healthy controls. Photon emission tomography studies meta-analyses did not find significant differences in PK11195 binding potential in cortical and subcortical regions of chronic hepatitis C patients compared with controls. Correlations were observed between various neuroimaging alterations and neurocognitive impairment, fatigue and depressive symptoms in some studies. CONCLUSIONS: Patients with chronic hepatitis C exhibit cerebral metabolite alterations and structural or functional neuroimaging abnormalities, which sustain the hypothesis of hepatitis C virus involvement in brain disturbances.

Glucocorticoid Receptors, Brain-Derived Neurotrophic Factor, Serotonin and Dopamine Neurotransmission are Associated with Interferon-Induced Depression.

BACKGROUND: The role of inflammation in mood disorders has received increased attention. There is substantial evidence that cytokine therapies, such as interferon alpha (IFN-alpha), can induce depressive symptoms. Indeed, proinflammatory cytokines change brain function in several ways, such as altering neurotransmitters, the glucocorticoid axis, and apoptotic mechanisms. This study aimed to evaluate the impact on mood of initiating IFN-alpha and ribavirin treatment in a cohort of patients with chronic hepatitis C. We investigated clinical, personality, and functional genetic variants associated with cytokine-induced depression. METHODS: We recruited 344 Caucasian outpatients with chronic hepatitis C, initiating IFN-alpha and ribavirin therapy. All patients were euthymic at baseline according to DSM-IV-R criteria. Patients were assessed at baseline and 4, 12, 24, and 48 weeks after treatment initiation using the Patient Health Questionnaire (PHQ), the Hospital Anxiety and Depression Scale (HADS), and the Temperament and Character Inventory (TCI). We genotyped several functional polymorphisms of interleukin-28 (IL28B), indoleamine 2,3-dioxygenase (IDO-1), serotonin receptor-1A (HTR1A), catechol-O-methyl transferase (COMT), glucocorticoid receptors (GCR1 and GCR2), brain-derived neurotrophic factor (BDNF), and FK506 binding protein 5 (FKBP5) genes. A survival analysis was performed, and the Cox proportional hazards model was used for the multivariate analysis. RESULTS: The cumulative incidence of depression was 0.35 at week 24 and 0.46 at week 48. The genotypic distributions were in Hardy-Weinberg equilibrium. Older age (p = 0.018, hazard ratio [HR] per 5 years = 1.21), presence of depression history (p = 0.0001, HR = 2.38), and subthreshold depressive symptoms at baseline (p = 0.005, HR = 1.13) increased the risk of IFN-induced depression. So too did TCI personality traits, with high scores on fatigability (p = 0.0037, HR = 1.17), impulsiveness (p = 0.0200 HR = 1.14), disorderliness (p = 0.0339, HR = 1.11), and low scores on extravagance (p = 0.0040, HR = 0.85). An interaction between HTR1A and COMT genes was found. Patients carrying the G allele of HTR1A plus the Met substitution of the COMT polymorphism had a greater risk for depression during antiviral treatment (HR = 3.83) than patients with the CC (HTR1A) and Met allele (COMT) genotypes. Patients carrying the HTR1A CC genotype and the COMT Val/Val genotype (HR = 3.25) had a higher risk of depression than patients with the G allele (HTR1A) and the Val/Val genotype. Moreover, functional variants of the GCR1 (GG genotype: p = 0.0436, HR = 1.88) and BDNF genes (Val/Val genotype: p = 0.0453, HR = 0.55) were associated with depression. CONCLUSIONS: The results of the study support the theory that IFN-induced depression is associated with a complex pathophysiological background, including serotonergic and dopaminergic neurotransmission as well as glucocorticoid and neurotrophic factors. These findings may help to improve the management of patients on antiviral treatment and broaden our understanding of the pathogenesis of mood disorders.

Real-time map building and navigation for autonomous robots in unknown environments.

An algorithmic solution method is presented for the problem of autonomous robot motion in completely unknown environments. Our approach is based on the alternate execution of two fundamental processes: map building and navigation. In the former, range measures are collected through the robot exteroceptive sensors and processed in order to build a local representation of the surrounding area. This representation is then integrated in the global map so far reconstructed by filtering out insufficient or conflicting information. In the navigation phase, an A*-based planner generates a local path from the current robot position to the goal. Such a path is safe inside the explored area and provides a direction for further exploration. The robot follows the path up to the boundary of the explored area, terminating its motion if unexpected obstacles are encountered. The most peculiar aspects of our method are the use of fuzzy logic for the efficient building and modification of the environment map, and the iterative application of A*, a complete planning algorithm which takes full advantage of local information. Experimental results for a NOMAD 200 mobile robot show the real-time performance of the proposed method, both in static and moderately dynamic environments.