Optimization of Fixations for Additively Manufactured Cranial Implants: Insights from Finite Element Analysis.

With the emergence of additive manufacturing technology, patient-specific cranial implants using 3D printing have massively influenced the field. These implants offer improved surgical outcomes and aesthetic preservation. However, as additive manufacturing in cranial implants is still emerging, ongoing research is investigating their reliability and sustainability. The long-term biomechanical performance of these implants is critically influenced by factors such as implant material, anticipated loads, implant-skull interface geometry, and structural constraints, among others. The efficacy of cranial implants involves an intricate interplay of these factors, with fixation playing a pivotal role. This study addresses two critical concerns: determining the ideal number of fixation points for cranial implants and the optimal curvilinear distance between those points, thereby establishing a minimum threshold. Employing finite element analysis, the research incorporates variables such as implant shapes, sizes, materials, the number of fixation points, and their relative positions. The study reveals that the optimal number of fixation points ranges from four to five, accounting for defect size and shape. Moreover, the optimal curvilinear distance between two screws is approximately 40 mm for smaller implants and 60 mm for larger implants. Optimal fixation placement away from the center mitigates higher deflection due to overhangs. Notably, a symmetric screw orientation reduces deflection, enhancing implant stability. The findings offer crucial insights into optimizing fixation strategies for cranial implants, thereby aiding surgical decision-making guidelines.

Correlative Confocal and 3D Electron Microscopy of a Specific Sensory Cell.

Delineation of a cell's ultrastructure is important for understanding its function. This can be a daunting project for rare cell types diffused throughout tissues made of diverse cell types, such as enteroendocrine cells of the intestinal epithelium. These gastrointestinal sensors of food and bacteria have been difficult to study because they are dispersed among other epithelial cells at a ratio of 1:1,000. Recently, transgenic reporter mice have been generated to identify enteroendocrine cells by means of fluorescence. One of those is the peptide YY-GFP mouse. Using this mouse, we developed a method to correlate confocal and serial block-face scanning electron microscopy. We named the method cocem3D and applied it to identify a specific enteroendocrine cell in tissue and unveil the cell's ultrastructure in 3D. The resolution of cocem3D is sufficient to identify organelles as small as secretory vesicles and to distinguish cell membranes for volume rendering. Cocem3D can be easily adapted to study the 3D ultrastructure of other specific cell types in their native tissue.