Structure-property-processing correlations of longitudinal freeze-cast chitosan scaffolds for biomedical applications

Author(s):  
Kaiyang Yin ◽  
Prajan Divakar ◽  
Ulrike G.K. Wegst
2021 ◽  
Author(s):  
Sandra Michel-Souzy ◽  
Naomi M. Hamelmann ◽  
Sara Zarzuela-Pura ◽  
Jos M. J. Paulusse ◽  
Jeroen J. L. M. Cornelissen

Encapsulin based protein cages are nanoparticles with different biomedical applications, such as targeted drug delivery or imaging agents. These particles are biocompatible and can be produced in bacteria, allowing large scale production and protein engineering. In order to use these bacterial nanocages in different applications, it is important to further explore the potential of their surface modification and optimize their production. In this study we design and show new surface modifications of the Thermotoga maritima (Tm) and Brevibacterium linens (Bl) encapsulins. Two new loops on Tm encapsulin with a His-tag insertion after the residue 64 and the residue 127, and the modification of the C-terminal on Bl encapsulin, are reported. The multi-modification of the Tm encapsulin enables up to 240 different functionalities on the cage surface, resulting from 4 potential modifications per protein subunit. We furthermore report an improved protocol giving a better stability and providing a notable increase of the production yield of the cages. Finally, we tested the stability of different encapsulin variants over a year and the results show a difference in stability arising from the tag insertion position. These first insights in the structure-property relationship of encapsulins, with respect to the position of a function loop, allow for further study of the use of these protein nanocages in biomedical applications.


2013 ◽  
Vol 1 (3) ◽  
pp. 221-227
Author(s):  
Thiago B. Fideles ◽  
Gloria T. F. S. Furtado ◽  
Daniel B. Lima ◽  
Silvia M. P. Borges ◽  
Ítalo M. F. Pinheiro ◽  
...  

2015 ◽  
Vol 133 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Daniela Filip ◽  
Doina Macocinschi ◽  
Stelian Vlad ◽  
Gabriela Lisa ◽  
Mariana Cristea ◽  
...  

2003 ◽  
Vol 804 ◽  
Author(s):  
Jack R. Smith ◽  
Doyle Knight ◽  
Joachim Kohn ◽  
Khaled Rasheed ◽  
Norbert Weber ◽  
...  

ABSTRACTWe have developed an empirical method to model bioresponse to the surfaces of biodegradable polymers in a combinatorial library using Artificial Neural Networks (ANN) in conjunction with molecular modeling and machine learning methodology. We validated the procedure by modeling human fibrinogen adsorption to 22 structurally distinct polymers. Subsequently, the method was used to model the more complicated phenomena of rat lung fibroblast and normal human fetal foreskin fibroblast proliferation in the presence of 24 and 44 different polymers, respectively. In each case, the root mean square (rms) percent error of the prediction was substantially less than the experimental variation, showing that the models can distinguish high and low performing polymers based on structure/property information. Using this method to screen candidate materials in terms of specific bioresponse prior to extensive experimental testing will greatly facilitate materials development for biomedical applications.


Author(s):  
David Safranski ◽  
Ken Gall

The purpose of this study was to understand how the side group dictates thermo-mechanical properties of shape-memory acrylate networks, specifically strain to failure and toughness. A useful parameter in assessing shape memory polymers is the strain to failure because it is critical to know how much recovery strain the material can experience. To understand how the structure is related to mechanical properties, such as strain to failure, materials of differing chain stiffness ratio, C∞, were compared at varying percentages of crosslinker. While the chemical and thermal properties of acrylate networks have been discussed in much detail, methods of toughening networks by the precise choice of certain acrylates have not been thoroughly examined. In order for these networks to be of practical use as biomedical devices, such as minimally invasive shape memory polymer stents, detailed structure-property relationships must be established.


2019 ◽  
Vol 116 (23) ◽  
pp. 11259-11264 ◽  
Author(s):  
Fei Li ◽  
Jinsong Han ◽  
Tian Cao ◽  
William Lam ◽  
Baoer Fan ◽  
...  

Hydrogels that are self-assembled by peptides have attracted great interest for biomedical applications. However, the link between chemical structures of peptides and their corresponding hydrogel properties is still unclear. Here, we showed a combinational approach to generate a structurally diverse hydrogel library with more than 2,000 peptides and evaluated their corresponding properties. We used a quantitative structure–property relationship to calculate their chemical features reflecting the topological and physicochemical properties, and applied machine learning to predict the self-assembly behavior. We observed that the stiffness of hydrogels is correlated with the diameter and cross-linking degree of the nanofiber. Importantly, we demonstrated that the hydrogels support cell proliferation in culture, suggesting the biocompatibility of the hydrogel. The combinatorial hydrogel library and the machine learning approach we developed linked the chemical structures with their self-assembly behavior and can accelerate the design of novel peptide structures for biomedical use.


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