Customized 3D-printed cervical orthoses usually offer better fit and support. However, clinical evidence regarding the effectiveness of immobilization and comfort remains limited. This study aims to compare the effectiveness of custom-made cervical orthoses with that of commercially available orthoses in preventing pressure ulcers (PUs), improving immobilization, and providing comfort and ease of use. We measured the risk of PUs associated with six neck postures to assess comfort and immobilization when participants wore their orthosis. The customized cervical orthosis provided significantly greater immobilization than the Aspen Vista collar across all six neck postures (p< 0.05). No significant differences in comfort and user preference were observed between the two orthoses. The customized cervical orthosis offers superior immobilization compared to the Aspen Vista collar; however, it may increase pressure on the mastoid processes. While comfort is comparable to commercial options, customized orthosis improvements are needed to reduce pressure without sacrificing immobilization.

Trabecular bone microarchitecture and tissue heterogeneity strongly influence mechanical behaviour, yet homogeneous micro-finite element (µFE) models neglect tissue heterogeneity, limiting accuracy in local stress-strain predictions. This study evaluated the effects of mesh type (voxel-based linear hexahedral, HEX, vs geometry-based quadratic tetrahedral, TET), mesh resolution, and material property assignment (homogeneous vs heterogeneous) onµFE predictions of apparent elastic modulus, von Mises stress, and principal strains in human femoral trabecular bone cores. Fourteen cores were micro-computed tomography scanned and meshed into HEX and TETµFE models at three resolutions, with tissue modulus assigned either homogeneously or heterogeneously using trilinear interpolation from voxel-level density. Models were calibrated to experimentally measured apparent elastic modulus. All models accurately predicted apparent elastic modulus. However, mesh type and resolution substantially influenced local mechanical outcomes. Tetrahedral models produced higher local principal strains and von Mises stresses, indicating improved sensitivity to localized strain concentrations, whereas hexahedral models yielded smoother stress-strain distributions with lower peak values but greater computational efficiency. Finer meshes enhanced the resolution of local strain concentrations, while coarser meshes underestimated peak responses. Incorporating tissue heterogeneity increased local principal strains and reduced peak von Mises stresses, although differences from homogeneous models were modest. These findings demonstrate that mesh type has a stronger influence on local mechanical predictions than material heterogeneity when models are calibrated to bulk stiffness. Geometry-based tetrahedral meshes are preferable when accurate estimation of local strain concentrations is required, whereas voxel-based hexahedral meshes provide a computationally efficient alternative for bulk property prediction. This work provides practical guidance for optimizingµFE modelling strategies in trabecular bone research.

Diabetic retinopathy (DR) remains a major cause of visual impairment globally, and early, accurate diagnosis is critical for effective intervention. To address the challenges of limited labeled training data, difficulty identifying subtle and dispersed lesions, and redundant feature extraction in retinal images, this paper proposes a DR classification network with a multi-frequency contextual attention module (MFCA-DRNet). First, this paper introduces a self-supervised contrastive learning strategy pre-trained on the EyePACS dataset, eliminating reliance on extensive labeled data and enabling effective feature learning. Next, an adaptive preprocessing method, integrating histogram equalization with non-local means denoising, is designed to enhance image quality by reducing noise and improving lesion visibility. The proposed MFCA module effectively captures long-range contextual relationships and associates dispersed lesion characteristics across retinal images, significantly enhancing lesion recognition. Additionally, the backbone network incorporates an attention mechanism guided by an energy function, emphasizing lesion-specific features while suppressing irrelevant information. Evaluated through downstream classification tasks on DDR, APTOS 2019, and Messidor-2 datasets, MFCA-DRNet achieved strong performance, particularly on the APTOS 2019 dataset, with accuracy, precision, recall, andF1 scores of 87.12%, 81.2%, 85.3%, and 83.16%, respectively. These results highlight MFCA-DRNet's potential to improve DR diagnosis and clinical applicability in diverse imaging conditions.

Aortic mechanical heart valves (MHVs) have been implanted for decades to treat aortic valve disease and remain a viable option when valve durability is prioritized. However, the non-physiological hemodynamics induced by MHVs may lead to adverse clinical outcomes. Fluid-structure interaction (FSI) simulations enable the analysis of the biomechanical interaction between MHVs and blood flow. This study presents a strongly coupled, boundary-fitted FSI framework for aortic MHVs, used to assess the impact of MHV design and aortic curvature on hemodynamics. Nine simulation scenarios were investigated, considering three commercially available MHVs and three idealized aortic geometries (one straight and two curved models). Overall, the framework proved to provide results for flow-rate waveforms, velocity fields, and leaflet kinematics aligning well with previous experimental and computational studies. The framework highlighted that: (i) MHV design influences velocity fields and large-scale vorticity transport in the aorta, with systolic differences among the three devices of up to 41% and 133% in average swirling strength and stretching, respectively; (ii) the straight aortic model underestimates systolic swirling strength (up to 56%) and stretching (up to 91%) compared to curved models. This FSI framework can support MHV development by analyzing different device designs and anatomical scenarios.