Electrokinetic sensing in cartilage: a porous-material perspective on joint mechanics

TL;DR

This study models electrokinetic signals in cartilage by translating histological images into pore-network simulations, revealing how structural differences affect fluid flow and electrical responses, with implications for cartilage health assessment.

Contribution

It introduces a computational framework that links cartilage microstructure to electrokinetic behavior, enabling predictions of electrical signals based on histological features.

Findings

Pathological cartilage shows fragmented connectivity and weaker signals.

Healthy cartilage exhibits coherent transport and stronger electrical responses.

Predicted depth-dependent and anisotropic electrical signals across cartilage layers.

Abstract

Mechanical loading in articular cartilage drives interstitial fluid flow through the porous collagen proteoglycan matrix, generating electrokinetic signals. We investigate whether the structural organization of cartilage histology can be translated into a computational representation capable of predicting its electrokinetic behavior. Histological pictures were analyzed to build a pore-network graph representing potential pathways for interstitial fluid transport. Pressure driven flow was simulated using hydraulic conductance relations, while electrical potentials were estimated through electrokinetic coupling between pressure gradients and ion displacement. Simulations comparing networks derived from healthy and degenerative cartilage showed that pathological structures exhibited fragmented connectivity and lower predicted signal amplitudes, whereas physiological architecture generated more coherent transport trajectories and stronger electrical responses. Our simulations yield testable predictions, depth-dependent electrical signals across cartilage layers with directional anisotropy relative to collagen orientation. Potential applications include improved experimental assessment of cartilage transport biomechanics and integration of microstructural imaging with computational models of charged porous biomaterials.