More information: lnkd.in/eRdpVpbv

This study shows that by systematically analyzing and integrating all interacting aspects of the foreign body reaction, in silico modeling can provide valuable insights and complement in vitro and in vivo studies for improved implant safety assessment.

We also show that feedback interactions can protect the system from pathological outcome by gradually reducing the initial inflammatory input.

The in silico model incorporates key material-related factors, including immunogenic properties and mechanical mismatch. Predictions align with existing knowledge, showing that material stiffness and tissue progressive stiffening due to increased ECM deposition can exacerbate the FBR.

Here we used a pig and human dataset to compare six computational models of PD!

More information: doi.org/10.3390/toxi...

However, although several mathematical models have been developed in the past few decades, a direct comparison of the models’ accuracy with respect to predicting in vivo data is needed to further create robust personalized models.

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Our findings highlight the significance of adhesion size, spatial distribution, and lifetime, as well as the diffusion rate of YAP and the rate of YAP binding to adhesions, in modulating YAP phosphorylation levels.

However, formation of larger adhesions on stiff matrices significantly reduces YAP Y357 phosphorylation. In this study, we explore the dynamic interplay between adhesion properties and YAP phosphorylation with the aim to identify factors that could lead to the experimentally-observed behavior.

We also identify how different models either explain contradictory experimental observations or generate experimentally testable predictions.