Synthesis and Characterization of Thermally and Chemically Gelling Injectable Hydrogels for Tissue Engineering

Novel, injectable hydrogels were developed that solidify through a dual-gelation, physical and chemical, mechanism upon preparation and elevation of temperature to 37°C. A thermogelling, poly(N-isopropylacrylamide)-based macromer with pendant epoxy rings and a hydrolyticallydegradable polyamidoamine-based diamine crosslinker were synthesized, characterized, and combined to produce in situ forming hydrogel constructs. Network formation through the epoxyamine reaction was shown to be rapid and facile, and the progressive incorporation of the hydrophilic polyamidoamine crosslinker into the hydrogel was shown to mitigate the often problematic tendency of thermogelling materials to undergo significant post-formation gel syneresis. The results suggest that this novel class of injectable hydrogels may be attractive substrates for tissue engineering applications due to the synthetic versatility of the component materials and beneficial hydrogel gelation kinetics and stability

Abstracts from the 20th International Symposium on Signal Transduction at the Blood-Brain Barriers

https://deepblue.lib.umich.edu/bitstream/2027.42/138963/1/12987_2017_Article_71.pd

Mechanical and migration properties of PVC/waste PET depolymerization products blends

Waste poly(ethylene terephthalate) (PET) flakes were depolymerized by using trimethylol propane (TMP) in the presence of zinc acetate as catalyst. Molar ratio of PET/TMP was used as 1/2 and 1/4 in the alcoholysis reactions. In order to obtain PVC plastisols, PVC powder was dispersed into a plasticizer mixture composed of di-isononyl phthalate (DINP) and alcoholysis products by using a high-speed mixer (65% PVC, 35% plasticizers w/w). Alcoholysis products were used 20 and 40% of DINP by weight in the plasticizer mixture. Plasticized PVC sheets were prepared from these plastisols. Migration properties, mechanical properties and T-g of plasticized PVC sheets were determined. Results showed that the alcoholysis products of waste PET are compatible for using as secondary plasticizer for PVC

Inversion of a part of the numerator relationship matrix using pedigree information

Background. In recent theoretical developments, the information available (e.g. genotypes) divides the original population into two groups: animals with this information (selected animals) and animals without this information (excluded animals). These developments require inversion of the part of the pedigree-based numerator relationship matrix that describes the genetic covariance between selected animals (A22). Our main objective was to propose and evaluate methodology that takes advantage of any potential sparsity in the inverse of A22 in order to reduce the computing time required for its inversion. This potential sparsity is brought out by searching the pedigree for dependencies between the selected animals. Jointly, we expected distant ancestors to provide relationship ties that increase the density of matrix A22 but that their effect on A22i might be minor. This hypothesis was also tested. Methods. The inverse of A22 can be computed from the inverse of the triangular factor (T-1 ) obtained by Cholesky root-free decomposition of A22 . We propose an algorithm that sets up the sparsity pattern of T-1 using pedigree information. This algorithm provides positions of the elements of T-1 worth to be computed (i.e. different from zero). A recursive computation of A22i is then achieved with or without information on the sparsity pattern and time required for each computation was recorded. For three numbers of selected animals (4000; 8000 and 12 000), A22 was computed using different pedigree extractions and the closeness of the resulting A22i to the inverse computed using the fully extracted pedigree was measured by an appropriate norm. Results. The use of prior information on the sparsity of T-1 decreased the computing time for inversion by a factor of 1.73 on average. Computational issues and practical uses of the different algorithms were discussed. Cases involving more than 12 000 selected animals were considered. Inclusion of 10 generations was determined to be sufficient when computing A22. Conclusions. Depending on the size and structure of the selected sub-population, gains in time to compute A22 are possible and these gains may increase as the number of selected animals increases. Given the sequential nature of most computational steps, the proposed algorithm can benefit from optimization and may be convenient for genomic evaluations.NextGenGE