Relatively high cost of manufacturing and inability to produce modular all tantalum implants has limited its widespread acceptance, in spite of its excellent in vitro and in vivo biocompatibility. In this article, we report how to process Ta to create net shape porous structures with varying porosity using Laser Engineered Net Shaping (LENS™) for the first time. Porous Ta samples with relative densities between 45 to 73% have been successfully fabricated and characterized for their mechanical properties. In vitro cell materials interactions, using human osteoblast cell line hFOB, have been accessed on these porous Ta structures and compared with porous Ti control samples. The results show that the Young's modulus of porous Ta can be tailored between 1.5 to 20 GPa by changing the pore volume fraction between 27 and 55%. In vitro biocompatibility in terms of MTT assay and immunochemistry study showed excellent cellular adherence, growth and differentitation with abundant extracellular matrix formation on porous Ta structures compared to porous Ti control. These results indicate that porous Ta structures can promote enhanced/early biological fixation. The enhanced in vitro cell-materials interactions on porous Ta surface are attributed to chemistry and its high wettability and surface energy relative to porous Ti. Our results show that these laser processed porous Ta structures can find numerous applications, particularly among older patients, for metallic implants because of their excellent bioactivity.

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Cell morphology has been identified as a potential indicator of stem cell response to biomaterials. However, determination of cell shape phenotype in biomaterials is complicated by heterogeneous cell populations, microenvironment heterogeneity, and multi-parametric definitions of cell morphology. To associate cell morphology with cell-material interactions, we developed a shape phenotyping framework based on support vector machines. A feature selection procedure was implemented to select the most significant combination of cell shape metrics to build classifiers with both accuracy and stability to identify and predict microenvironment-driven morphological differences in heterogeneous cell populations. The analysis was conducted at a multi-cell level, where a "supercell" method used average shape measurements of small groups of single cells to account for heterogeneous populations and microenvironment. A subsampling validation algorithm revealed the range of supercell sizes and sample sizes needed for classifier stability and generalization capability. As an example, the responses of human bone marrow stromal cells (hBMSCs) to fibrous vs flat microenvironments were compared on day 1. Our analysis showed that 57 cells (grouped into supercells of size 4) are the minimum needed for phenotyping. The analysis identified that a combination of minor axis length, solidity, and mean negative curvature were the strongest early shape-based indicator of hBMSCs response to fibrous microenvironment.