English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Biomarker analysis of athletes' urinary steroid profiles is crucial for thesuccess of anti-doping efforts. Current statistical analysis methods generatepersonalised limits for each athlete based on univariate modelling oflongitudinal biomarker values from the urinary steroid profile. However,simultaneous modelling of multiple biomarkers has the potential to furtherenhance abnormality detection. In this study, we propose a multivariateBayesian adaptive model for longitudinal data analysis, which extends theestablished single-biomarker model in forensic toxicology. The proposedapproach employs Markov chain Monte Carlo sampling methods and addresses thescarcity of confirmed abnormal values through a one-class classificationalgorithm. By adapting decision boundaries as new measurements are obtained,the model provides robust and personalised detection thresholds for eachathlete. We tested the proposed approach on a database of 229 athletes whichincludes longitudinal steroid profiles classified as normal, atypical, orconfirmed abnormal. Our results demonstrate improved detection performance,highlighting the potential value of a multivariate approach in dopingdetection.

A multivariate Bayesian learning approach for improved detection of doping in athletes using urinary steroid profiles