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Biomedical subjects

B Sierra

Publications and source records attributed to B Sierra.

7 recordsLinked to original sources
PubMed2006

Antibodies from patients with dengue viral infection mediate cellular cytotoxicity.

Acute and late convalescent sera (collected at day 5 of disease onset and 1 year later) from dengue fever (DF) and dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS) laboratory confirmed cases, were tested for antibody-dependent cell-mediated cytotoxicity (ADCC) activity using dengue 1 (DENV-1) or dengue 2 (DENV-2) infected cells as target. All patients experienced their first dengue virus (DENV) infection 20 years before. ADCC activity was detected in acute sera from DHF/DSS but not in sera from DF patients. However, 1 year after illness, ADCC activity was observed in all cases. This preliminary report represents one of the few studies of ADCC in dengue patients and suggests that ADCC could be implicated in dengue pathogenesis.

Antibodies, Viral↗
PubMed2003

Carboxy-terminally truncated Dengue 4 virus envelope glycoprotein expressed in Pichia pastoris induced neutralizing antibodies and resistance to Dengue 4 virus challenge in mice.

We have expressed a recombinant Dengue 4 virus envelope glycoprotein (E4rec), truncated at its C-terminus by 53 amino acids, in Pichia pastoris. The presence of E4rec was confirmed by Western-blot using anti-DEN 4 hyper immune mouse ascitic fluid. E4rec migrated during SDS-PAGE as a 64 kDa protein. Treatment with endoglycosidases showed that the E protein was modified by the addition of short mannose chains and the absence of hyperglycosylation. When administered to BALB-C mice, E4rec elicited a DEN 4 neutralizing antibody response haemagglutination inhibition antibodies and specific memory T cell response. Mice immunized were also significantly protected against lethal DEN 4 virus challenge (86.6%, p < 0.001).

Animals↗
PubMed2001

A phase I clinical trial of a multi-epitope polypeptide TAB9 combined with Montanide ISA 720 adjuvant in non-HIV-1 infected human volunteers.

A phase I clinical trial was performed to examine the safety and immunogenicity of a multi-epitope polypeptide comprising the central 15 amino acids of the V3 loop from six HIV-1 isolates. This protein called TAB9 was emulsified in Montanide ISA720 (Seppic, Paris) and administered intramuscularly at doses of 0, 0.2 and 1 mg to 24 healthy, HIV-1 seronegative adult males. Three immunisations were given at months 0, 1 and 6 in a randomised, double blind, placebo controlled clinical trial. The placebo was generally well tolerated. However, severe local reactions were observed in TAB9 vaccinated subjects after the second and third inoculations. Seven out of eight volunteers from the lower dose group showed moderate or severe local inflammation, while four out of eight subjects from the higher dose group developed granulomas and sterile abscesses. In general, the reactogenicity depended on the number of inoculations given and the dose of TAB9. Both doses were immunogenic, all immunised volunteers seroconverted and antibodies were broadly reactive against the V3 peptides included in the protein. All vaccine's sera reacted against gp120 in Western blot and 50% of them also neutralised at least one out of five laboratory isolates tested. No differences between doses were found. Anti TAB9 lymphoproliferative responses were observed, being more intense in the high dose group. Due to the strong local reactions that were found in this study, a change in the formulation will be required for further trials with this vaccine candidate in humans.

AIDS Vaccines↗
PubMed2001

Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data.

Combining the predictions of a set of classifiers has shown to be an effective way to create composite classifiers that are more accurate than any of the component classifiers. There are many methods for combining the predictions given by component classifiers. We introduce a new method that combine a number of component classifiers using a Bayesian network as a classifier system given the component classifiers predictions. Component classifiers are standard machine learning classification algorithms, and the Bayesian network structure is learned using a genetic algorithm that searches for the structure that maximises the classification accuracy given the predictions of the component classifiers. Experimental results have been obtained on a datafile of cases containing information about ICU patients at Canary Islands University Hospital. The accuracy obtained using the presented new approach statistically improve those obtained using standard machine learning methods.

Algorithms↗
PubMed2001

Feature subset selection by genetic algorithms and estimation of distribution algorithms. A case study in the survival of cirrhotic patients treated with TIPS.

The transjugular intrahepatic portosystemic shunt (TIPS) is an interventional treatment for cirrhotic patients with portal hypertension. In the light of our medical staff's experience, the consequences of TIPS are not homogeneous for all the patients and a subgroup dies in the first 6 months after TIPS placement. Actually, there is no risk indicator to identify this subgroup of patients before treatment. An investigation for predicting the survival of cirrhotic patients treated with TIPS is carried out using a clinical database with 107 cases and 77 attributes. Four supervised machine learning classifiers are applied to discriminate between both subgroups of patients. The application of several feature subset selection (FSS) techniques has significantly improved the predictive accuracy of these classifiers and considerably reduced the amount of attributes in the classification models. Among FSS techniques, FSS-TREE, a new randomized algorithm inspired on the new EDA (estimation of distribution algorithm) paradigm has obtained the best average accuracy results for each classifier.

Algorithms↗
PubMedDate not supplied

Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches.

In this work we introduce a methodology based on genetic algorithms for the automatic induction of Bayesian networks from a file containing cases and variables related to the problem. The structure is learned by applying three different methods: The Cooper and Herskovits metric for a general Bayesian network, the Markov blanket approach and the relaxed Markov blanket method. The methodologies are applied to the problem of predicting survival of people after 1, 3 and 5 years of being diagnosed as having malignant skin melanoma. The accuracy of the obtained models, measured in terms of the percentage of well-classified subjects, is compared to that obtained by the so-called Naive-Bayes. In the four approaches, the estimation of the model accuracy is obtained from the 10-fold cross-validation method.

Algorithms↗