Resolving curling and swelling in cartilage and other deformation-prone tissue explant models: a scalable, simple load-predictive flattening method enabling standardized mechanical compression.

Introduction: Methods: We developed a simple, regression-guided flattening step that computes explant-specific loads from each explant's swelling state to restore native thickness and flatness prior to loading. Using adult articular cartilage, we tested the hypothesis that mitigating initial deformation amplifies injury model readouts and permits standardized injury of the deformation-prone superficial zone (SZ), which many models exclude. Results: Discs spanning 250-1200 µm exhibited curling; 250 µm SZ discs were chosen for calibration. Post-equilibration thickness (T Discussion: Together, regression-guided flattening restores native geometry, standardizes the mechanical stimulus, increases readout reliability, and enables inclusion of the clinically important SZ. Because deformation artifacts are common across hydrated soft tissues, and prior pre-compression practices were uncalibrated, this framework establishes a cartilage-based proof of concept for regression-guided flattening that may be extendable to other deformation-prone tissues through tissue-specific recalibration and experimental validation, thereby providing a more standardized and generalizable route to faithful