FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386697789> ?p ?o ?g. }

• W4386697789 endingPage "44808" @default.
• W4386697789 startingPage "44796" @default.
• W4386697789 abstract "Gelatin methacryloyl (GelMA) hydrogels are promising materials for tissue engineering applications due to their biocompatibility and tunable properties. However, the time-consuming process of preparing GelMA hydrogels with desirable properties for specific biomedical applications limits their clinical use. Visible-light-induced cross-linking is a well-known method for the preparation of GelMA hydrogels; however, a comprehensive investigation on the influence of critical parameters such as Eosin Y (EY), triethanolamine (TEA), and N-vinyl-2-pyrrolidone (NVP) concentrations on the stiffness and gelation time has yet to be performed. In this study, we systematically investigated the effect of these critical parameters on the stiffness and gelation time of GelMA hydrogels. We developed an artificial neural network (ANN) model with three input variables, EY, TEA, and NVP concentrations, and two output variables, Young’s modulus and gelation time, derived from our experimental design. Through the alteration of individual chemical concentrations, [EY] between 0.005 and 0.5 mM and [TEA] and [NVP] between 10 and 1000 mM, we studied the impact of these alterations on the real-time values of stiffness and gelation time. Furthermore, we demonstrated the validity of the ANN model in predicting the properties of GelMA hydrogels. We also studied cell survival to establish nontoxic concentration ranges for each component, enabling safer use of GelMA hydrogels in relevant biomedical applications. Our results showed that the ANN model can accurately predict the properties of GelMA hydrogels, allowing for the synthesis of hydrogels with desirable stiffness for various biomedical applications. In conclusion, our study provides a comprehensive library that characterizes the stiffness and gelation time and demonstrates the potential of the ANN model to predict these properties of GelMA hydrogels depending on the critical parameters. The ANN models developed here can facilitate the optimization of GelMA hydrogels with the most efficient mechanical properties that resemble a native extracellular matrix and better address the need in the in vivo microenvironment. The approach of this study is to bring research about the synthesis of GelMA hydrogels to a new level where the synthesis of these hydrogels can be standardized with minimum cost and effort." @default.
• W4386697789 created "2023-09-14" @default.
• W4386697789 creator A5003748630 @default.
• W4386697789 creator A5080669151 @default.
• W4386697789 creator A5092860044 @default.
• W4386697789 date "2023-09-13" @default.
• W4386697789 modified "2023-09-29" @default.
• W4386697789 title "Optimization of Gelatin Methacryloyl Hydrogel Properties through an Artificial Neural Network Model" @default.
• W4386697789 cites W1120477740 @default.
• W4386697789 cites W1436031433 @default.
• W4386697789 cites W1991918935 @default.
• W4386697789 cites W2008006119 @default.
• W4386697789 cites W2011330152 @default.
• W4386697789 cites W2033518295 @default.
• W4386697789 cites W2043358786 @default.
• W4386697789 cites W2044512869 @default.
• W4386697789 cites W2045775784 @default.
• W4386697789 cites W2062848325 @default.
• W4386697789 cites W2075456396 @default.
• W4386697789 cites W2077443558 @default.
• W4386697789 cites W2086244192 @default.
• W4386697789 cites W2103131794 @default.
• W4386697789 cites W2108128254 @default.
• W4386697789 cites W2136457255 @default.
• W4386697789 cites W2139109539 @default.
• W4386697789 cites W2162052594 @default.
• W4386697789 cites W2167485594 @default.
• W4386697789 cites W2253253071 @default.
• W4386697789 cites W2397349486 @default.
• W4386697789 cites W2521465081 @default.
• W4386697789 cites W2610038212 @default.
• W4386697789 cites W2729949698 @default.
• W4386697789 cites W2792820250 @default.
• W4386697789 cites W2794091348 @default.
• W4386697789 cites W2883462871 @default.
• W4386697789 cites W2884430236 @default.
• W4386697789 cites W2891583892 @default.
• W4386697789 cites W2903972185 @default.
• W4386697789 cites W2905326325 @default.
• W4386697789 cites W3008592843 @default.
• W4386697789 cites W3019837908 @default.
• W4386697789 cites W3023444175 @default.
• W4386697789 cites W3027866862 @default.
• W4386697789 cites W3081892099 @default.
• W4386697789 cites W3104887532 @default.
• W4386697789 cites W3111804553 @default.
• W4386697789 cites W3164199631 @default.
• W4386697789 cites W3200716658 @default.
• W4386697789 cites W3204392606 @default.
• W4386697789 cites W3209623457 @default.
• W4386697789 cites W3215221332 @default.
• W4386697789 cites W4211119846 @default.
• W4386697789 cites W4280492955 @default.
• W4386697789 cites W4285005623 @default.
• W4386697789 cites W4295193457 @default.
• W4386697789 cites W4317932623 @default.
• W4386697789 cites W4322493855 @default.
• W4386697789 cites W4367310714 @default.
• W4386697789 doi "https://doi.org/10.1021/acsami.3c12207" @default.
• W4386697789 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37704030" @default.
• W4386697789 hasPublicationYear "2023" @default.
• W4386697789 type Work @default.
• W4386697789 citedByCount "0" @default.
• W4386697789 crossrefType "journal-article" @default.
• W4386697789 hasAuthorship W4386697789A5003748630 @default.
• W4386697789 hasAuthorship W4386697789A5080669151 @default.
• W4386697789 hasAuthorship W4386697789A5092860044 @default.
• W4386697789 hasConcept C108586683 @default.
• W4386697789 hasConcept C127413603 @default.
• W4386697789 hasConcept C136229726 @default.
• W4386697789 hasConcept C159985019 @default.
• W4386697789 hasConcept C178790620 @default.
• W4386697789 hasConcept C185592680 @default.
• W4386697789 hasConcept C188027245 @default.
• W4386697789 hasConcept C191897082 @default.
• W4386697789 hasConcept C192562407 @default.
• W4386697789 hasConcept C2777230088 @default.
• W4386697789 hasConcept C2778823387 @default.
• W4386697789 hasConcept C2779372316 @default.
• W4386697789 hasConcept C42360764 @default.
• W4386697789 hasConcept C49892992 @default.
• W4386697789 hasConcept C71924100 @default.
• W4386697789 hasConceptScore W4386697789C108586683 @default.
• W4386697789 hasConceptScore W4386697789C127413603 @default.
• W4386697789 hasConceptScore W4386697789C136229726 @default.
• W4386697789 hasConceptScore W4386697789C159985019 @default.
• W4386697789 hasConceptScore W4386697789C178790620 @default.
• W4386697789 hasConceptScore W4386697789C185592680 @default.
• W4386697789 hasConceptScore W4386697789C188027245 @default.
• W4386697789 hasConceptScore W4386697789C191897082 @default.
• W4386697789 hasConceptScore W4386697789C192562407 @default.
• W4386697789 hasConceptScore W4386697789C2777230088 @default.
• W4386697789 hasConceptScore W4386697789C2778823387 @default.
• W4386697789 hasConceptScore W4386697789C2779372316 @default.
• W4386697789 hasConceptScore W4386697789C42360764 @default.
• W4386697789 hasConceptScore W4386697789C49892992 @default.
• W4386697789 hasConceptScore W4386697789C71924100 @default.
• W4386697789 hasFunder F4320320366 @default.

• next