Page 331 - IJB-9-6
P. 331

International Journal of Bioprinting                              Rheology-informed machine learning model




            33.  Song K, Zhang D, Yin J, et al., 2021, Computational study of   44.  Bone JM, Childs CM, Menon A, et al., 2020, Hierarchical
               extrusion bioprinting with jammed gelatin microgel-based   machine learning for high-fidelity 3D printed biopolymers.
               composite ink. Addit Manuf, 41: 101963.            ACS Biomater Sci Eng, 6(12): 7021–7031.
               https://doi.org/10.1016/j.addma.2021.101963        https://doi.org/10.1021/acsbiomaterials.0c00755
            34.  Leppiniemi  J, Lahtinen P, Paajanen  A,  et al., 2017,   45.  Menon A, Póczos B, Feinberg AW, et al., 2019, Optimization
               3D-printable bioactivated nanocellulose–alginate hydrogels.   of silicone 3D printing with hierarchical machine learning.
               ACS Appl Mater Interfaces, 9(26): 21959–21970.     3D Print Addit Manuf, 6(4): 181–189.
               https://doi.org/10.1021/acsami.7b02756             https://doi.org/10.1089/3dp.2018.0088
            35.  Kim MH,  Lee YW, Jung W-K,  et  al., 2019,  Enhanced   46.  Jin Z, Zhang Z, Shao X, et al., 2021, Monitoring anomalies
               rheological behaviors of alginate hydrogels with carrageenan   in 3D bioprinting with deep neural networks. ACS Biomater
               for  extrusion-based  bioprinting.  J Mech Behav Biomed   Sci Eng, 9(7): 3945–3952.
               Mater, 98: 187–194.
               https://doi.org/10.1016/j.jmbbm.2019.06.014        https://doi.org/10.1021/acsbiomaterials.0c01761
            36.  Göhl J, Markstedt K, Mark A, et al., 2018, Simulations of 3D   47.  Mancha Sánchez E, Gómez-Blanco JC, López Nieto, et al.,
               bioprinting: Predicting bioprintability of nanofibrillar inks.   2020, Hydrogels for bioprinting: A systematic review of
               Biofabrication, 10(3): 034105.                     hydrogels synthesis, bioprinting parameters, and bioprinted
                                                                  structures behavior. Front Bioeng Biotechnol, 8: 776.
               https://doi.org/10.1088/1758-5090/aac872
                                                                  https://doi.org/10.3389/fbioe.2020.00776
            37.  Bonatti AF, Chiesa I, Vozzi G,  et  al., 2021, Open-source
               CAD-CAM simulator of the extrusion-based bioprinting   48.  Zhang Z,  Jin  Y, Yin  J,  et  al.,  2018, Evaluation of  bioink
               process. Bioprinting, 24: e00172.                  printability for bioprinting applications. Appl Phys Rev, 5(4):
                                                                  041304.
               https://doi.org/10.1016/j.bprint.2021.e00172
            38.  Suntornnond R, Tan EYS, An J, et al., 2016, A mathematical   https://doi.org/10.1063/1.5053979
               model on the resolution of extrusion bioprinting for the   49.  Chimene D, Lennox KK, Kaunas RR, et al., 2016, Advanced
               development of new bioinks. Materials, 9(9): 756.  bioinks for 3D printing: A materials science perspective.
               https://doi.org/10.3390/ma9090756                  Annal Biomed Eng, 44(6): 2090–2102.
            39.  Bonatti AF, Vozzi G, Chua CK,  et al., A deep learning   https://doi.org/10.1007/s10439-016-1638-y
               approach for error detection and quantification in extrusion-  50.  Cooke ME, Rosenzweig DH, 2021, The rheology of direct and
               based bioprinting. Mater Today: Proc, 70: 131–135.  suspended extrusion bioprinting. APL Bioeng, 5(1): 011502.
               https://doi.org/10.1016/j.matpr.2022.09.006        https://doi.org/10.1063/5.0031475
            40.  Ruberu K, Senadeera M, Rana S,  et al., 2021, Coupling   51.  Paxton N, Smolan W, Böck T, et al., 2017, Proposal to assess
               machine learning with 3D bioprinting to fast track   printability of bioinks for extrusion-based bioprinting
               optimisation of extrusion printing.  Appl Mater Today,  22:   and evaluation of rheological properties governing
               100914.
                                                                  bioprintability. Biofabrication, 9(4): 044107.
               https://doi.org/10.1016/j.apmt.2020.100914
                                                                  https://doi.org/10.1088/1758-5090/aa8dd8
            41.  Lee J, Oh SJ, An SH, et al., 2020, Machine learning-based
               design strategy for 3D printable bioink: Elastic modulus   52.  Ouyang  L, Yao  R,  Zhao Y,  et al.,  2016, Effect of  bioink
               and yield stress determine printability. Biofabrication, 12(3):   properties on printability and cell viability for 3D bioplotting
               035018.                                            of embryonic stem cells. Biofabrication, 8(3): 035020.
               https://doi.org/10.1088/1758-5090/ab8707           http://dx.doi.org/10.1088/1758-5090/8/3/035020
            42.  Conev A, Litsa EE, Perez MR, et al., 2020, Machine learning-  53.  Xu X, Jagota A, Peng S,  et al., 2013, Gravity and surface
               guided three-dimensional printing of tissue engineering   tension effects on the shape change of soft materials.
               scaffolds. Tissue Eng Part A, 26(23–24): 1359–1368.  Langmuir, 29(27): 8665–8674.
               https://doi.org/10.1089/ten.tea.2020.0191          https://doi.org/10.1021/la400921h
            43.  Fu Z, Angeline V, Sun W, 2021, Evaluation of printing   54.  Gao T, Gillispie GJ, Copus JS,  et al., 2018, Optimization
               parameters on 3D extrusion printing of pluronic hydrogels   of  gelatin–alginate  composite  bioink  printability
               and machine learning guided parameter recommendation.   using  rheological  parameters:  A  systematic  approach.
               Int J Bioprint, 7(4): 179–189.                     Biofabrication, 10(3): 034106.
               https://doi.org/10.18063%2Fijb.v7i4.434            https://doi.org/10.1088/1758-5090/aacdc7


            Volume 9 Issue 6 (2023)                        323                          https://doi.org/10.36922/ijb.1280
   326   327   328   329   330   331   332   333   334   335   336