RESUMEN
Over 100 000 research articles and 9000 patents have been published on tissue engineering (TE) in the past 20 years. Yet, very few TE products have made their way to the market during the same period. Experts have proposed a variety of strategies to address the lack of translation of TE products. However, since these proposals are guided by qualitative insights, they are limited in scope and impact. Machine learning is utilized in the current study to analyze the entire body of patents that have been published over the past twenty years and understand patenting trends, topics, areas of application, and exemplifications. This analysis yields surprising and little-known insights about the differences in research priorities and perceptions of innovativeness of tissue engineers in academia and industry, as well as aids to chart true advances in the field during the past twenty years. It is hoped that this analysis and subsequent proposal to improve translational rates of TE products will spur much needed dialogue about this important pursuit.
Asunto(s)
Aprendizaje Automático , Ingeniería de Tejidos/tendencias , Investigación Biomédica Traslacional/tendencias , Algoritmos , Tratamiento Basado en Trasplante de Células y Tejidos/tendencias , Bases de Datos Factuales , Terapia Genética/tendencias , Humanos , Técnicas de Cultivo de Órganos , Trasplante de Órganos/instrumentación , Medicina Regenerativa/tendencias , Ingeniería de Tejidos/métodos , Investigación Biomédica Traslacional/métodosRESUMEN
Although effects of biochemical modulation of stem cells have been widely investigated, only recent advances have been made in the identification of mechanical conditioning on cell signaling pathways. Experimental investigations quantifying the micromechanical environment of mesenchymal stem cells (MSCs) are challenging while computational approaches can predict their behavior due to in vitro stimulations. This study introduces a 3D cell-specific finite element model simulating large deformations of MSCs. Here emphasizing cell mechanical modulation which represents the most challenging multiphysics phenomena in sub-cellular level, we focused on an approach attempting to elicit unique responses of a cell under fluid flow. Fluorescent staining of MSCs was performed in order to visualize the MSC morphology and develop a geometrically accurate model of it based on a confocal 3D image. We developed a 3D model of a cell fixed in a microchannel under fluid flow and then solved the numerical model by fluid-structure interactions method. By imposing flow characteristics representative of vigorous in vitro conditions, the model predicts that the employed external flow induces significant localized effective stress in the nucleo-cytoplasmic interface and average cell deformation of about 40%. Moreover, it can be concluded that a lower strain level is made in the cell by the oscillatory flow as compared with steady flow, while same ranges of effective stress are recorded inside the cell in both conditions. The deeper understanding provided by this study is beneficial for better design of single cell in vitro studies.