ABSTRACT
EL microbioma humano es definido como el genoma colectivo de una población compuesta por bacterias, hongos, virus y protozoos que residen en los diferentes nichos ecológicos del cuerpo humano. El microbioma contribuye a mantener las funciones metabólica, proteger el cuerpo contra los patógenos, es esencial para el desarrollo de la inmunidad y de la nutrición. Muchas enfermedades pueden ser explicadas debido a la alteración en el equilibrio del microbioma. (AU)
The human microbiome is defined as the colective genomes of the microbes composed of bacteria, fungi, viruses and protozoa that residen in the human body. These microbes have the potential to impact the physiology, both in health and desease. Microbiome contribute methabolic functions, protect tha body against pathogens, is essential for development of inmunity and nutrition. Many deseases may possibly be explained by alterations in the microbiome. (AU)
Subject(s)
Humans , Genome, Bacterial , MicrobiotaABSTRACT
BACKGROUND: The World Wide Web has become a dissemination platform for scientific and non-scientific publications. However, most of the information remains locked up in discrete documents that are not always interconnected or machine-readable. The connectivity tissue provided by RDF technology has not yet been widely used to support the generation of self-describing, machine-readable documents. RESULTS: In this paper, we present our approach to the generation of self-describing machine-readable scholarly documents. We understand the scientific document as an entry point and interface to the Web of Data. We have semantically processed the full-text, open-access subset of PubMed Central. Our RDF model and resulting dataset make extensive use of existing ontologies and semantic enrichment services. We expose our model, services, prototype, and datasets at http://biotea.idiginfo.org/ CONCLUSIONS: The semantic processing of biomedical literature presented in this paper embeds documents within the Web of Data and facilitates the execution of concept-based queries against the entire digital library. Our approach delivers a flexible and adaptable set of tools for metadata enrichment and semantic processing of biomedical documents. Our model delivers a semantically rich and highly interconnected dataset with self-describing content so that software can make effective use of it.
ABSTRACT
Objetivo: Comprobar que polimorfismos implicados en genes del metabolismo de carcinógenos pueden contribuir a la susceptibilidad individual a desarrollar cáncer de pulmón. Hemos estudiado la relación entre dos polimorfismos en estos genes (CYP1A1 MspI y GSTP1 Ile105Val) y el riesgo de desarrollar cáncer de pulmón.Diseño: Estudio caso control de base hospitalaria que incluye 406 casos incidentes de cáncer de pulmón y 436 controles apareados por edad, género y área geográfica. Los genotipos fueron determinados por PCR-RFLP y los resultados fueron analizados usando un método de regresión logística.Resultados: Encontramos asociación entre el polimorfismoCYP1A1MspI y el riesgo de desarrollar cáncer de pulmón mientras que no encontramos asociación al estudiar el polimorfismo GSTP1 Ile105Val. El valor de la OR ajustada obtenida en el caso del CYP1A1MspI es de 1,47; 95% CI = 0,35-6,14, lo cual indica que aumenta el riesgo a desarrollar cáncer de pulmón aunque nuestros datos no son estadísticamente significativos. Por otra parte, parece que el genotipo heterocigoto para el polimorfismoGSTP1 Ile105Val tiene un papel protector frente aldesarrollo del cáncer de pulmón en mujeres (OR ajustada=0,22; CI = 0,09-0,56).Conclusión: Nuestros resultados apoyan la hipótesis de que polimorfismos en genes implicados en el metabolismo de carcinógenos y sus combinaciones pueden influir en la etiología del cáncer de pulmón
Purpose: Polymorphisms in genes involved in carcinogens metabolism might contribute to the individual susceptibility to develop different types of cancer. We have investigated the relationship between polymorphisms in two of these genes, CYP1A1 (MspI) and GSTP (Ile105Val) and the risk of developing lung cancer.Experimental Design: A hospital-based case-control studywas designed including 406 lung cancer patients and 436control subjects matched on age, gender, and geographicalarea. Genotypes were determined by PCR-RFLP and the results were analyzed using unconditional logistic regression, adjusting for relevant confounding variables.Results: A slight association was found for CYP1A1 MspI polymorphism, while no association was observed for GSTP1Ile105Val polymorphism. In this regard, the CYP1A1MspI genotype was associated with an increased risk of lung cancer(adjusted OR=1,47; 95% CI = 0,35-6,14) but there was no statistical significance. It seems that heterozygous GSTP1 variant protects to develop lung cancer in women (adjusted OR= 0,22; CI = 0,09-0,56). Furthermore, an interaction between CYP1A1 MspI and GSTP1 Ile105Val was observed, resulting in a further increase in the risk of developing lung cancer.Conclusions: These results support the hypothesis that polymorphisms and their combined effects in metabolic genes might play a role in lung cancer etiology (AU)
