Page 329 - IJB-9-6

International Journal of Bioprinting Rheology-informed machine learning model

compared to the conventional models such as the Visualization: Dageon Oh
concentration-dependent model and printing parameter- Writing – original draft: Dageon Oh, Seung Yun Nam
dependent model. Additionally, the RIHML model Writing – review & editing: Masoud Shirzad, Eun-Jae
also exhibited low error (around 10%) in predicting the Chung, Seung Yun Nam
printing resolution for different concentrations of bioink
constituents, such as Pluronic F-127, gelatin, xanthan gum, Ethics approval and consent to participate
and CaCl . Furthermore, the RIHML model can predict Not applicable.
2
the printing resolution with a new nanomaterial (CNC)
added to the alginate-based bioink, which is hardly possible Consent for publication
with conventional methods. This study demonstrated the
importance of considering the rheological properties of Not applicable.
bioinks in predicting the printability of extrusion-based
bioprinting and highlighted the potential of the RIHML Availability of data
model as a useful tool for predicting the printing resolution of The data presented in this study are available on request
extrusion-based bioprinting. In addition, the results indicate from the corresponding author.
that the RIHML model can be versatile and expandable in
the prediction of bioprinting resolution, and the printing References
and rheological datasets may be accumulated to enhance
the prediction accuracy. The potential for the RIHML model 1. Ozbolat, Yu Y, 2013, Bioprinting toward organ fabrication:
to generalize and embrace new data, even with a small Challenges and future trends. IEEE Trans Biomed Eng, 60(3):
dataset size, is an advantage in the field of 3D bioprinting 691–699.
where data size is limited due to the complexity and time- https://doi.org/10.1109/TBME.2013.2243912
consuming nature of the preparation of bioinks with various
compositions and 3D printing with multiple parameters. 2. Murphy SV, Atala A, 2014, 3D bioprinting of tissues and
organs. Nat Biotechnol, 32(8), 773–785.
Acknowledgments https://doi.org/10.1038/nbt.2958
None. 3. Sun W, Starly B, Daly AC, et al., 2020, The bioprinting
roadmap. Biofabrication, 12(2): 022002.
Funding https://doi.org/10.1088/1758-5090/ab5158
This research was supported by a National 4. Daly AC, Prendergast ME, Hughes AJ, et al., 2021,
Research Foundation of Korea (NRF) grant (NRF- Bioprinting for the biologist. Cell, 184(1): 18–32.
2021R1I1A3040459) funded by the Korean government https://doi.org/10.1016/j.cell.2020.12.002
(MOE). This research was supported by a grant of the 5. Lu D, Yang Y, Zhang P, et al., 2022, Development and
Korea Health Technology R&D Project through the Korea application of three-dimensional bioprinting scaffold in the
Health Industry Development Institute (KHIDI), funded repair of spinal cord injury. Tissue Eng Regen Med, 19(6):
by the Ministry of Health & Welfare, Republic of Korea 1113–1127.
(grant number : HI22C1323).
https://doi.org/10.1007/s13770-022-00465-1
Conflict of interest 6. Tan B, Gan S, Wang X, et al., 2021, Applications of 3D
bioprinting in tissue engineering: advantages, deficiencies,
The authors declare no conflicts of interests. improvements, and future perspectives. J Mater Chem B,
9(27): 5385–5413.
Author contributions
https://doi.org/10.1039/D1TB00172H
Conceptualization: Dageon Oh, Seung Yun Nam 7. Mandrycky C, Wang Z, Kim K, et al., 2016, 3D bioprinting
Data curation: Dageon Oh, Min Chang Kim for engineering complex tissues. Biotechnol Adv, 34(4):
Formal analysis: Dageon Oh 422–434.
Funding acquisition: Eun-Jae Chung, Seung Yun Nam https://doi.org/10.1016/j.biotechadv.2015.12.011
Investigation: Dageon Oh, Seung Yun Nam
Methodology: Dageon Oh, Seung Yun Nam 8. Yilmaz B, Al Rashid A, Mou YA, et al., 2021, Bioprinting:
Project administration: Dageon Oh, Seung Yun Nam A review of processes, materials and applications.
Supervision: Seung Yun Nam Bioprinting, 23: e00148.
Validation: Dageon Oh https://doi.org/10.1016/j.bprint.2021.e00148

Volume 9 Issue 6 (2023) 321 https://doi.org/10.36922/ijb.1280