Dr Nicholas Farr

Learning from the past: Investigating medical devices (surgical mesh):

Surgical mesh is one of the biomaterials for which Nicholas has characterised and published studies. In 2021, he developed and published a study presenting a novel characterisation approach to reveal the mechano–chemical effects of oxidation and dynamic distension on polypropylene surgical mesh. This paper is significant in the field as it not only shows evidence that polypropylene surgical mesh is subject to oxidative degradation but also provides insights into how the degradation of polypropylene can affect the functional properties of the mesh in vivo. Following this study, he has published various supplementary papers outlining the process of oxidative degradation of polypropylene surgical mesh. These publications include the first papers to characterise, quantify, and identify particles released from polypropylene mesh after exposure to an oxidative stress environment, the first study to characterise titanium-coated polypropylene surgical mesh explanted from humans, the first study to study macrophages (cells involved in the immune response) cultured directly onto surgical mesh, the first study to modify the formation of surface cracking/crazing on surgical mesh using plasma modification, and the first paper to show time-related degradation of polypropylene surgical mesh in vivo.

He continues to conduct research within this field, working with internationally renowned clinicians and academics from a host of institutions including NHS teaching Hospital trusts, Institute for Pathology at Düren Hospital, University of Witten Herdecke, Leibniz Institute for Plasma Science and Technology, University of Queensland, University of Newcastle, Freeman Hospital, and University Hospitals KU Leuven.

Informing the future: Uncovering the relationship between cells and medical devices:

Biomaterials exhibit significant variability in their cellular response, yet existing material characterisation methods have failed to elucidate a clear mechanism for this response. As a consequence, Nicholas and his colleagues have developed a systematic characterisation analysis strategy. They employ machine learning approaches to adapt and extend this strategy, aiming to identify specific spectral signatures indicative of biomaterial/cell chemistry or topography, respectively.

To date, Nicholas's research has been successful in linking actual cell growth behavior with nanoscale structural, chemical, and bulk mechanical information captured from biomaterials of dissimilar compositions. Publications resulting from international and UK-based collaborations (including NHS teaching Hospital trusts, Leibniz Institute for Plasma Science and Technology, University of Oxford, University of Warwick, Cracow University of Technology, University of British Columbia, Monash University, University of Antwerp, New York Structural Biology Centre) continue to receive much interest.