Current approaches and future perspectives on strategies for the development of personalized tissue engineering therapies

Personalized tissue engineering and regenerative medicine (TERM) therapies propose patient-oriented effective solutions, considering individual needs. Cell-based therapies, for example, may benefit from cell sources that enable easier autologous set-ups or from recent developments on IPS cells technologies towards effective personalized therapeutics. Furthermore, the customization of scaffold materials to perfectly fit a patientâ s tissue defect through rapid prototyping technologies, also known as 3D printing, is now a reality. Nevertheless, the timing to expand cells or to obtain functional in vitrotissue substitutes prior to implantation prevents advancements towards routine use upon patient´s needs. Thus, personalized therapies also anticipate the importance of creating off-the-shelf solutions to enable immediately available tissue engineered products. This paper reviews the main recent developments and future challenges to enable personalized TERM approaches and to bring these technologies closer to clinical applications.The authors wish to acknowledge the financial support of the Portuguese Foundation for Science and Technology for the post-doctoral grant (SFRH/BPD/111729/2015) and Recognize (UTAP-ICDT/CTM-BIO/0023/2014), and the project RL3 -TECT -NORTE-07-0124-FEDER-000020 co-financed by ON.2 (NSRF), through ERDF

Causes of evolutionary rate variation among protein sites

It has long been recognized that certain sites within a protein, such as sites in the protein core or catalytic residues in enzymes, are evolutionarily more conserved than other sites. However, our understanding of rate variation among sites remains surprisingly limited. Recent progress to address this includes the development of a wide array of reliable methods to estimate site-specific substitution rates from sequence alignments. In addition, several molecular traits have been identified that correlate with site-specific mutation rates, and novel mechanistic biophysical models have been proposed to explain the observed correlations. Nonetheless, current models explain, at best, approximately 60% of the observed variance, highlighting the limitations of current methods and models and the need for new research directions.Fil: Echave, Julián. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Spielman, Stephanie J.. University of Texas at Austin; Estados UnidosFil: Wilke, Claus O.. University of Texas at Austin; Estados Unido