Relevance of Baseline Viral Genetic Heterogeneity and Host Factors for Treatment Outcome Prediction in Hepatitis C Virus 1b-Infected Patients

Only about 50% of patients chronically infected with HCV genotype 1 (HCV-1) respond to treatment with pegylated interferon-alfa and ribavirin (dual therapy), and protease inhibitors have to be administered together with these drugs increasing costs and side-effects. We aimed to develop a predictive model of treatment response based on a combination of baseline clinical and viral parameters. Seventy-four patients chronically infected with HCV-1b and treated with dual therapy were studied (53 retrospectively −training group−, and 21 prospectively −validation group−). Host and viral-related factors (viral load, and genetic variability in the E1-E2, core and Interferon Sensitivity Determining Region) were assessed. Multivariate discriminant analysis and decision tree analysis were used to develop predictive models on the training group, which were then validated in the validation group. A multivariate discriminant predictive model was generated including the following variables in decreasing order of significance: the number of viral variants in the E1-E2 region, an amino acid substitution pattern in the viral core region, the IL28B polymorphism, serum GGT and ALT levels, and viral load. Using this model treatment outcome was accurately predicted in the training group (AUROC = 0.9444; 96.3% specificity, 94.7% PPV, 75% sensitivity, 81% NPV), and the accuracy remained high in the validation group (AUROC = 0.8148, 88.9% specificity, 90.0% PPV, 75.0% sensitivity, 72.7% NPV). A second model was obtained by a decision tree analysis and showed a similarly high accuracy in the training group but a worse reproducibility in the validation group (AUROC = 0.9072 vs. 0.7361, respectively). The baseline predictive models obtained including both host and viral variables had a high positive predictive value in our population of Spanish HCV-1b treatment naïve patients. Accurately identifying those patients that would respond to the dual therapy could help reducing implementation costs and additional side effects of new treatment regimens

Material necessari per al control de la diabetis mellitus: prestació de servei al SISCAT; cartera de serveis del sistema sanitari integral d’utilització pública de Catalunya (SISCAT)

Diabetis mellitus; Cartera de serveis; Control de la malaltia; Prestació de serveisDiabetes mellitus; Cartera de servicios; Control de la enfermedad; Prestación de serviciosDiabetes mellitus; Portfolio of services; Disease control; Provision of servicesLa DM és una malaltia crònica i la seva gestió pot ser complexa. Requereix un seguiment assistencial continuat amb estratègies multifactorials de reducció de riscos més enllà d’un bon control glucèmic, per tal de prevenir complicacions agudes i evitar o frenar possibles complicacions a llarg termini. Si es deixa evolucionar lliurement, és causa important de morbiditat i mortalitat. Així doncs, requereix un seguiment que ha d’incloure aspectes d’educació per a la salut i empoderament de les persones afectades, amb l’objectiu de millorar-ne la qualitat de vida i reduir les complicacions associades. El tractament dependrà del tipus de diabetis, i pot constar des de mesures higienicodietètiques (alimentació, activitat física, etc.), presa de medicaments orals o fins i tot administració d’insulina. En qualsevol dels casos, és important seguir un pla d’alimentació saludable, mantenir un pes adequat i la realització d’activitat física regular

TFG 2016/2017

Amb aquesta publicació, EINA, Centre universitari de Disseny i Art adscrit a la Universitat Autònoma de Barcelona, dóna a conèixer el recull dels Treballs de Fi de Grau presentats durant el curs 2016-2017. Voldríem que un recull com aquest donés una idea més precisa de la tasca que es realitza a EINA per tal de formar nous dissenyadors amb capacitat de respondre professionalment i intel·lectualment a les necessitats i exigències de la nostra societat. El treball formatiu s’orienta a oferir resultats que responguin tant a paràmetres de rigor acadèmic i capacitat d’anàlisi del context com a l’experimentació i la creació de nous llenguatges, tot fomentant el potencial innovador del disseny.Con esta publicación, EINA, Centro universitario de diseño y arte adscrito a la Universidad Autónoma de Barcelona, da a conocer la recopilación de los Trabajos de Fin de Grado presentados durante el curso 2016-2017. Querríamos que una recopilación como ésta diera una idea más precisa del trabajo que se realiza en EINA para formar nuevos diseñadores con capacidad de responder profesional e intelectualmente a las necesidades y exigencias de nuestra sociedad. El trabajo formativo se orienta a ofrecer resultados que respondan tanto a parámetros de rigor académico y capacidad de análisis, como a la experimentación y la creación de nuevos lenguajes, al tiempo que se fomenta el potencial innovador del diseño.With this publication, EINA, University School of Design and Art, affiliated to the Autonomous University of Barcelona, brings to the public eye the Final Degree Projects presented during the 2016-2017 academic year. Our hope is that this volume might offer a more precise idea of the task performed by EINA in training new designers, able to speak both professionally and intellectually to the needs and demands of our society. The educational task is oriented towards results that might respond to the parameters of academic rigour and the capacity for contextual analysis, as well as to considerations of experimentation and the creation of new languages, all the while reinforcing design’s innovative potential

Behavioral and Cognitive Improvement Induced by Novel Imidazoline I2 Receptor Ligands in Female SAMP8 Mice

As populations increase their life expectancy, age-related neurodegenerative disorders such as Alzheimer's disease have become more common. I2-Imidazoline receptors (I2-IR) are widely distributed in the central nervous system, and dysregulation of I2-IR in patients with neurodegenerative diseases has been reported, suggesting their implication in cognitive impairment. This evidence indicates that high-affinity selective I2-IR ligands potentially contribute to the delay of neurodegeneration. In vivo studies in the female senescence accelerated mouse-prone 8 mice have shown that treatment with I2-IR ligands, MCR5 and MCR9, produce beneficial effects in behavior and cognition. Changes in molecular pathways implicated in oxidative stress, inflammation, synaptic plasticity, and apoptotic cell death were also studied. Furthermore, treatments with these I2-IR ligands diminished the amyloid precursor protein processing pathway and increased Aβ degrading enzymes in the hippocampus of SAMP8 mice. These results collectively demonstrate the neuroprotective role of these new I2-IR ligands in a mouse model of brain aging through specific pathways and suggest their potential as therapeutic agents in brain disorders and age-related neurodegenerative diseases. Keywords Imidazoline I2 receptors (2-imidazolin-4-yl)phosphonates Behavior Cognition Neurodegeneration Neuroprotection Agin

The Rbfox1 gene: expression analysis and study of the transcriptional regulation

The RBFOX1 gene, which is located on chromosome 16p13.2, encodes an RNA-binding protein that regulates pre-mRNA splicing events in specific cell types including neurons. In the mouse, the gene contains a large noncoding part in its 5' end with at least four alternative promoters, promoters 1B, 1C, 1D and 1E. Promoters 1B, 1C and 1D are brain specific and promoter 1E is muscle specific. The promoters drive the expression of the alternative Rbfox1 transcript isoforms, which differ in their 5'UTR but not in their coding exons. Copy number variants (CNVs) in the human RBFOX1 gene that are located in the 5'-noncoding part of the gene and that typically only affect some but not all RBFOX1 transcript isoforms, have been associated with a range of neurodevelopmental disorders such as autism spectrum disorder, intellectual disability, epilepsy, attention deficit hyperactivity disorder and schizophrenia. In this thesis, I analyzed the expression of the three brain-specific Rbfox1 transcript isoforms controlled by the promoters 1B, 1C and 1D during embryonic development of the cerebral cortex as well as in various brain regions of the juvenile mouse. Furthermore, through in silico analysis and luciferase assays, I characterized the alternative Rbfox1 promoters and demonstrated that the expression of Rbfox1 in primary cortical neurons is driven by promoters 1B and 1C. I identified three transcription factors (C-MYC, NEUROD2 and TCF4) that regulate the expression of Rbfox1 in cortical neurons. Using transcript-specific studies, I was able to show that TCF4 regulates the expression of transcripts 1B and 1C, whereas NEUROD2 only controls the expression of transcript 1B.Das RBFOX1-Gen, das auf Chromosom 16p13.2 lokalisiert ist, kodiert für ein RNA-bindendes Protein, das Prä-mRNA-Spleißereignisse in bestimmten Zelltypen einschließlich Neuronen reguliert. In der Maus enthält das Gen einen großen nichtcodierenden Teil in seinem 5'-Ende mit mindestens vier alternativen Promotoren, den Promotoren 1B, 1C, 1D und 1E. Die Promotoren 1B-1D sind hirnspezifisch und der Promotor 1E ist muskelspezifisch. Die Promotoren steuern die Expression der alternativen Rbfox1-Transkript-Isoformen, die sich lediglich in ihren 5'UTR-Exons unterscheiden. Kopienzahlvarianten (CNVs) im humanen RBFOX1-Gen, die sich im 5'-nichtcodierenden Teil des Gens befinden und typischerweise nur einige, jedoch nicht alle RBFOX1-Transkript-Isoformen betreffen, sind mit einer Reihe von neurologischen Entwicklungsstörungen wie Autismus Spektrum Störung, geistige Behinderung, Epilepsie, Aufmerksamkeitsdefizit-Hyperaktivitätsstörung und Schizophrenie assoziiert. In dieser Arbeit analysierte ich die Expression der drei durch die Promotoren 1B, 1C und 1D gesteuerten Gehirn-spezifischen Rbfox1-Transkript-Isoformen während der Embryonalentwicklung des zerebralen Kortex der Maus sowie in verschiedenen Hirnregionen der juvenilen Maus. Darüber hinaus charakterisierte ich die alternativen Rbfox1-Promotoren und zeigte, dass die Expression von Rbfox1 in primären cortikalen Neuronen von den Promotoren 1B und 1C gesteuert wird. Ich identifizierte drei Transkriptionsfaktoren (C-MYC, NEUROD2 und TCF4), die die Expression von Rbfox1 in kortikalen Neuronen regulieren. Mittels transkriptspezifischer Untersuchungen konnte ich zeigen, dass TCF4 die Expression der Transkripte 1B und 1C reguliert, wohingegen NEUROD2 lediglich die Expression von Transkript 1B steuert

Relevance of Baseline Viral Genetic Heterogeneity and Host Factors for Treatment Outcome Prediction in Hepatitis C Virus 1b-Infected Patients

<div><p>Background</p><p>Only about 50% of patients chronically infected with HCV genotype 1 (HCV-1) respond to treatment with pegylated interferon-alfa and ribavirin (dual therapy), and protease inhibitors have to be administered together with these drugs increasing costs and side-effects. We aimed to develop a predictive model of treatment response based on a combination of baseline clinical and viral parameters.</p><p>Methodology</p><p>Seventy-four patients chronically infected with HCV-1b and treated with dual therapy were studied (53 retrospectively −training group−, and 21 prospectively −validation group−). Host and viral-related factors (viral load, and genetic variability in the E1–E2, core and Interferon Sensitivity Determining Region) were assessed. Multivariate discriminant analysis and decision tree analysis were used to develop predictive models on the training group, which were then validated in the validation group.</p><p>Principal Findings</p><p>A multivariate discriminant predictive model was generated including the following variables in decreasing order of significance: the number of viral variants in the E1–E2 region, an amino acid substitution pattern in the viral core region, the <i>IL28B</i> polymorphism, serum GGT and ALT levels, and viral load. Using this model treatment outcome was accurately predicted in the training group (AUROC = 0.9444; 96.3% specificity, 94.7% PPV, 75% sensitivity, 81% NPV), and the accuracy remained high in the validation group (AUROC = 0.8148, 88.9% specificity, 90.0% PPV, 75.0% sensitivity, 72.7% NPV). A second model was obtained by a decision tree analysis and showed a similarly high accuracy in the training group but a worse reproducibility in the validation group (AUROC = 0.9072 <i>vs</i>. 0.7361, respectively).</p><p>Conclusions and Significance</p><p>The baseline predictive models obtained including both host and viral variables had a high positive predictive value in our population of Spanish HCV-1b treatment naïve patients. Accurately identifying those patients that would respond to the dual therapy could help reducing implementation costs and additional side effects of new treatment regimens.</p></div

Baseline variables used for model development in the training group (<i>p</i>-value <0.15).

<p>nHap_E1E2, number of haplotypes in the whole E1–E2 studied region; nHap_HVR1, number of haplotypes in the hypervariable region 1; ISDR, interferon-sensitivity determining region; SD, standard deviation; Ks, number of substitutions per synonymous site; ALT, alanine transaminase; AST, aspartate transaminase; GGT, gamma-glutamyl transferase; ×ULN, factor times upper limit of normal used in our center for males and females: 41 and 31 U/L for ALT, 37 and 31 for AST, and 85 and 50 for GGT, respectively.</p>*<p>One missing value in each group.</p

Descriptive baseline clinical features of study patients.

<p>BMI, body mass index; ALT, alanine transaminase; AST, aspartate transaminase; GGT, gamma-glutamyl transferase; ×ULN, factor times upper limit of normal used in our center for males and females: 41 and 31 U/L for ALT, 37 and 31 for AST, and 85 and 50 for GGT, respectively. Data is presented as mean ± SD for variables following a Normal distribution and as median (range) for the rest.</p

Sensitivity, specificity, and predictive values for the predictive models obtained.

<p>AUROC, area under the receiver operating characteristic curve; PPV, positive predictive value; NPV, negative predictive value.</p