Bacterial cellulose-collagen nanocomposite for bone tissue engineering

A nanocomposite based on bacterial cellulose (BC) and type I collagen (COL) was evaluated for in vitro bone regeneration. BC membranes were modified by glycine esterification followed by cross-linking of type I collagen employing 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide. Collagen incorporation was studied by spectroscopy analysis. X-Ray diffraction showed changes in the BC crystallinity after collagen incorporation. The elastic modulus and tensile strength for BC-COL decreased, while the strain at failure showed a slight increase, even after sterilization, as compared to pristine BC. Swelling tests and contact angle measurements were also performed. Cell culture experiments performed with osteogenic cells were obtained by enzymatic digestion of newborn rat calvarium revealed similar features of cell morphology for cultures grown on both membranes. Cell viability/proliferation was not different between BC and BC-COL membranes at day 10 and 14. The high total protein content and ALP activity at day 17 in cells cultured on BC-COL indicate that this composite allowed the development of the osteoblastic phenotype in vitro. Thus, BC-COL should be considered as alternative biomaterial for bone tissue engineering.

Multiobjective engineering design optimization problems: a sensitivity analysis approach

This paper proposes two new approaches for the sensitivity analysis of multiobjective design optimization problems whose performance functions are highly susceptible to small variations in the design variables and/or design environment parameters. In both methods, the less sensitive design alternatives are preferred over others during the multiobjective optimization process. While taking the first approach, the designer chooses the design variable and/or parameter that causes uncertainties. The designer then associates a robustness index with each design alternative and adds each index as an objective function in the optimization problem. For the second approach, the designer must know, a priori, the interval of variation in the design variables or in the design environment parameters, because the designer will be accepting the interval of variation in the objective functions. The second method does not require any law of probability distribution of uncontrollable variations. Finally, the authors give two illustrative examples to highlight the contributions of the paper.