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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Artificial neural networks became an attractive alternative for modeling and simulation of com- plex biological systems. In the present work, a blood plasma model based on artificial neural networks was proposed in order to evaluate the relationship between the magnesium ion pre-sent in the blood plasma and systolic blood pressure and glucose. Experimental and simu- lated data were used to construct and validate the model. It performed the analysis consider-ing the systolic blood pressure and glucose as a function of magnesium ion concentration at a fixed temperature (37oC). Predictions of these relationships through the proposed model produced errors, on average, below 1% com-pared against experimental data not presented in the training step. The proposed methodology revealed quantitative results and correctly pre-dicted behaviors and trends towards the asso-ciation between magnesium concentrations and systolic blood pressure, and glucose in far agreement with experimental results from lit-erature. These results indicated that artificial neural networks can successfully learn the complexity of the relationships among bio-logical parameters of distinct groups and can be used as a complementary tool to assist studies in which the role of magnesium in systolic blood pressure and glucose are con-sidered

health

Modelling of the relationship between systolic blood pressure and glucose with the magnesium ion present in the blood plasma: an approach using artificial neural networks