Biomedical subjects

B Sansó

Publications and source records attributed to B Sansó.

3 recordsLinked to original sources

PubMed1998

Enzyme-linked immunosorbent assay (ELISA) for detection of anti-Trypanosoma evansi equine antibodies.

The standardization of ELISA for the detection of anti-Trypanosoma evansi antibodies in naturally and experimentally infected horses is described. Bayesian analysis was used to establish the cutoff between positive and negative sera. In order to determine the assessment of the ELISA test, the results obtained were compared with those from an IFA. A relative sensibility of 98.39%, a specificity of 95.12% and a predictive value of 96.83% were determined. The standardized technique was used to evaluate the antibody production against trypanosome in an experimentally infected equine, in which the sera converted 15 days after infection. The test was also used for a study of sera prevalence in a non-random sample from two different populations. A prevalence of 81.7% in workhorse and 57.14% in stable horses was found.

Animals↗

PubMed1998

Approximate Bayesian inference for random effects meta-analysis.

Whilst meta-analysis is becoming a more commonplace statistical technique, Bayesian inference in meta-analysis requires complex computational techniques to be routinely applied. We consider simple approximations for the first and second moments of the parameters of a Bayesian random effects model for meta-analysis. These computationally inexpensive methods are based on simple analytical formulae that provide an efficient tool for a qualitative analysis and a quick numerical estimation of posterior quantities. They are shown to lead to sensible approximations in two examples of meta-analyses and to be in broad agreement with the more computationally intensive Gibbs sampling.

Antibiotic Prophylaxis↗

PubMed1995

An approach to intelligent ischaemia monitoring.

The paper describes an approach to intelligent ischaemia event detection based on ECG ST-T segment analysis. ST-T trends are processed by means of a Bayesian forecasting approach using the multistate Kalman filter. A complete procedure, intended for use in CCU/ICU monitoring areas, is proposed, in order to give the clinician an intelligent monitoring tool. The approach serves to describe trends and their changes in a symbolic way. A novel aspect is its ability to observe certain features of ST-T elevation/depression not detected by other means, and to reject artefacts and erroneous events. A sensitivity of 89.58% and a predictivity of 84.31% are obtained on selected records of the European ST-T database. Using a restriction on event amplitude, the predictivity is raised to 95.55%. An ischaemia sensitivity index of 1.2 was determined. The method has been shown to be a robust and practical trend analysis tool, and seems to be appropriate for numeric/symbolic transformations in next-generation intelligent monitoring systems.

Bayes Theorem↗