Extremely strong and tough hydrogels as prospective candidates for tissue repair – A review

Ideal candidates for the repair of robust biological tissues should exhibit diverse features such as biocompatibility, strength, toughness, self-healing ability and a well-defined structure. Among the available biomaterials, hydrogels, as highly hydrated 3D-crosslinked polymeric networks, are promising for Tissue Engineering purposes as result of their high resemblance with native extracellular matrix. However, these polymeric structures often exhibit a poor mechanical behavior, hampering their use in load-bearing applications. During the last years, several efforts have been made to create new strategies and concepts to fabricate strong and tough hydrogels. Although it is already possible to shape the mechanical properties of artificial hydrogels to mimic biotissues, critical issues regarding, for instance, their biocompatibility and hierarchical structure are often neglected. Therefore, this review covers the structural and mechanical characteristics of the developed methodologies to toughen hydrogels, highlighting some pioneering efforts employed to combine the aforementioned properties in natural-based hydrogels.

A critical evaluation of methods for the reconstruction of tissue-specific models

Under the framework of constraint based modeling, genome-scale metabolic models (GSMMs) have been used for several tasks, such as metabolic engineering and phenotype prediction. More recently, their application in health related research has spanned drug discovery, biomarker identification and host-pathogen interactions, targeting diseases such as cancer, Alzheimer, obesity or diabetes. In the last years, the development of novel techniques for genome sequencing and other high-throughput methods, together with advances in Bioinformatics, allowed the reconstruction of GSMMs for human cells. Considering the diversity of cell types and tissues present in the human body, it is imperative to develop tissue-specific metabolic models. Methods to automatically generate these models, based on generic human metabolic models and a plethora of omics data, have been proposed. However, their results have not yet been adequately and critically evaluated and compared. This work presents a survey of the most important tissue or cell type specific metabolic model reconstruction methods, which use literature, transcriptomics, proteomics and metabolomics data, together with a global template model. As a case study, we analyzed the consistency between several omics data sources and reconstructed distinct metabolic models of hepatocytes using different methods and data sources as inputs. The results show that omics data sources have a poor overlapping and, in some cases, are even contradictory. Additionally, the hepatocyte metabolic models generated are in many cases not able to perform metabolic functions known to be present in the liver tissue. We conclude that reliable methods for a priori omics data integration are required to support the reconstruction of complex models of human cells.