Biomedical subjects

Jesus A Lopez

Publications and source records attributed to Jesus A Lopez.

3 recordsLinked to original sources

PubMed2006

Gene expression in normal urothelium depends on location within the bladder: a possible link to bladder carcinogenesis.

OBJECTIVES: Clinical studies have shown that more than 70% of primary bladder tumours arise in the area around the ureteric orifice. In this study a genomic approach was taken to explore the molecular mechanisms that may influence this phenomenon. METHODS: RNA was isolated from each individual normal ureteric orifice and the dome biopsy from 33 male patients. Equal amounts of the pooled ureteric orifice and dome mRNAs were labelled with Cy3 and Cy5, respectively before hybridising to the gene chip (UniGEM 2.0, Incyte Genomics Inc., Wilmington, Delaware, USA). RESULTS: Significant changes (more than a twofold difference) in gene expression were observed in 3.1% (312) of the 10,176 gene array: 211 genes upregulated and 101 downregulated. Analysis of Cdc25B, TK1, PKM, and PDGFra with RT-PCR supported the reliability of the microarray result. Seladin-1 was the most upregulated gene in the ureteric orifice: 8.3-fold on the microarray and 11.4-fold by real time PCR. CONCLUSIONS: Overall, this study suggests significant altered gene expression between these two anatomically distinct areas of the normal human bladder. Of particular note is Seladin-1, whose significance in cancer is yet to be clarified. Further studies of the genes discovered by this work will help clarify which of these differences influence primary bladder carcinogenesis.

Adult↗

PubMed2004

Representing bioinformatics causality.

This paper reviews a variety of different graphical notations currently in active use for modelling dynamic processes in bioinformatics and biotechnology, and crystallises from these notations a set of properties essential to any proposal for a modelling language seeking to provide an adequate systemic description of biological processes.

Algorithms↗

PubMed2002

Prediction models in the design of neural network based ECG classifiers: a neural network and genetic programming approach.

BACKGROUND: Classification of the electrocardiogram using Neural Networks has become a widely used method in recent years. The efficiency of these classifiers depends upon a number of factors including network training. Unfortunately, there is a shortage of evidence available to enable specific design choices to be made and as a consequence, many designs are made on the basis of trial and error. In this study we develop prediction models to indicate the point at which training should stop for Neural Network based Electrocardiogram classifiers in order to ensure maximum generalisation. METHODS: Two prediction models have been presented; one based on Neural Networks and the other on Genetic Programming. The inputs to the models were 5 variable training parameters and the output indicated the point at which training should stop. Training and testing of the models was based on the results from 44 previously developed bi-group Neural Network classifiers, discriminating between Anterior Myocardial Infarction and normal patients. RESULTS: Our results show that both approaches provide close fits to the training data; p = 0.627 and p = 0.304 for the Neural Network and Genetic Programming methods respectively. For unseen data, the Neural Network exhibited no significant differences between actual and predicted outputs (p = 0.306) while the Genetic Programming method showed a marginally significant difference (p = 0.047). CONCLUSIONS: The approaches provide reverse engineering solutions to the development of Neural Network based Electrocardiogram classifiers. That is given the network design and architecture, an indication can be given as to when training should stop to obtain maximum network generalisation.

Electrocardiography↗