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/

Zendy Plus

Presents the zaia usage as premium feature

ZAIA

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the access of premium content as premium feature

Premium Content

Zendy Tools

Zendy Open

Depression is a very common disease. Approximately 10% of people in the Western world experience severe depression during their lifetime and many more experience a mild form of depression. It is commonly believed that depression is caused by malfunctions in the biological system constituted by the hypothalamus-pituitaryadrenal (HPA) axis. We pose a novel model capable of showing both circardian as well as ultradian oscillations of hormone concentrations. We show that these patterns imitate those observed in the corresponding data. We demonstrate that patient-specific modelling shows its ability to make diagnoses more precise and to offer individual treatment plans and drug design. Efficient and reliable methods for parameter estimation are crucial. Presently we are examine how well the shuffled complex evolution algorithm do in estimating parameters. The next step is to investigate which parameters there are responsible for which pathologies by non-linear mixed effect modelling and statistical hypothesis testing. Preliminary results are promising. Finally, we plan to investigate how well the Metropolis-Hastings Algorithm of the Bayesian Markov Chain Monte Carlo method for estimating the parameters is working and we are about to do the same using iteratively refined principal differential analysis or the approximated maximum likelihood estimate.

Patient Specific Modelling in Diagnosing Depression - Combining Mixture and Non-linear Mixed Effect Modelling