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International Journal of Bioprinting                                         AI for sustainable bioprinting




               doi: 10.1115/1.4040619                             doi: 10.1016/j.jmsy.2023.05.007
            92.  Chen H, Bansal S, Plasencia DM, et al. Omnidirectional and   104. Finnveden G, Potting J. Life cycle assessment. In: Wexler
               multi-material in situ 3D printing using acoustic levitation.   P, ed. Encyclopedia of Toxicology (Third Edition). Academic
               Adv Mater Technol. 2025;10(9):2401792.             Press. Elsevier, Oxford, United Kingdom; 2014:74-77.
               doi: 10.1002/admt.202401792
                                                               105. Luengo-Valderrey M-J, Pando-García J, Periáñez-Cañadillas
            93.  Wang X, Yang C, Yu Y, Zhao Y. In situ 3D bioprinting living   I, Cervera-Taulet A. Analysis of the impact of the triple helix
               photosynthetic scaffolds for autotrophic wound healing.   on  sustainable  innovation  targets  in  spanish  technology
               Research. 2022;2022:9794745.                       companies. Sustainability. 2020;12(8):3274.
               doi: 10.34133/2022/9794745                         doi:  10.3390/su12083274
            94.  Zhao W, Hu C, Lin S, et al. A closed-loop minimally invasive   106. Hakeem MM, Chin GH, Frendy, Ito H. Regional sustainable
               3D printing strategy with robust trocar identification and   development using a Quadruple Helix approach in Japan.
               adaptive alignment. Addit Manuf. 2023;73:103701.   Regl Stud Regl Sci. 2023;10(1):119-138.
               doi: 10.1016/j.addma.2023.103701                   doi: 10.1080/21681376.2023.2171313
            95.  Montano-Murillo R, Hirayama R, Plasencia DM. OpenMPD:   107. Ektefaie Y, Shen A, Bykova D, Marin MG, Zitnik
               a low-level presentation engine for multimodal particle-  M, Farhat M. Evaluating generalizability of artificial
               based displays. ACM Trans Graph. 2023;42(2):Article 24.  intelligence models for molecular datasets. Nat Mach Intell.
               doi: 10.1145/3572896                               2024;6(12):1512-1524.
                                                                  doi: 10.1038/s42256-024-00931-6
            96.  Hirayama R, Christopoulos G, Martinez Plasencia D,
               Subramanian S. High-speed acoustic holography with   108. Liu S, Chen Y, Wang Z, et al. The cutting-edge progress in
               arbitrary scattering objects. Sci Adv. 2022;8(24):eabn7614.  bioprinting for biomedicine: principles, applications, and
               doi: 10.1126/sciadv.abn7614                        future perspectives. MedComm. 2024;5(10):e753.
                                                                  doi: 10.1002/mco2.753
            97.  Chen H, Bansal S, Plasencia DM, et al. Omnidirectional
               and multi-material in situ 3D printing using acoustic   109. Goetz L, Seedat N, Vandersluis R, van der Schaar M.
               levitation (Adv Mater Technol 9/2025). Adv Mater Technol.   Generalization—a key challenge for responsible AI
               2025;10(9):2570049.                                in patient-facing clinical applications.  NJP Digit Med.
               doi: 10.1002/admt.202570049                        2024;7(1):126.
                                                                  doi: 10.1038/s41746-024-01127-3
            98.  Zboinska MA, Sämfors S, Gatenholm P. Robotically
               3D printed architectural membranes from ambient   110. Bonatti AF, Vozzi G, Chua CK, Maria CD. A deep learning
               dried cellulose nanofibril-alginate hydrogel.  Mater  Des.   quality control loop of the extrusion-based bioprinting
               2023;236:112472.                                   process. IJB. 2022;8(4):620.
               doi: 10.1016/j.matdes.2023.112472                  doi: 10.18063/ijb.v8i4.620
            99.  Arora A, Alderman JE, Palmer J, et al. The value of   111. Seol Y-J, Kang H-W, Lee SJ, Atala A, Yoo JJ. Bioprinting
               standards for health datasets in artificial intelligence-based   technology and its applications.  Eur  J  Cardiothorac  Surg.
               applications. Nat Med. 2023;29(11):2929-2938.      2014;46(3):342-348.
               doi: 10.1038/s41591-023-02608-w                    doi: 10.1093/ejcts/ezu148
            100. Aldoseri A, Al-Khalifa KN, Hamouda AM. Re-thinking data   112. Mahadik  B, Margolis  R, McLoughlin  S, et al. An open-
               strategy and integration for artificial intelligence: concepts,   source  bioink  database  for  microextrusion 3D  printing.
               opportunities, and challenges. Appl Sci. 2023;13(12):7082.  Biofabrication. 2023;15(1):015008.
               doi:  10.3390/app13127082                          doi: 10.1088/1758-5090/ac933a
            101. Shin J, Lee Y, Li Z, Hu J, Park SS, Kim K. Optimized 3D   113. Saalfeld S, Cardona A, Hartenstein V, Tomančák P.
               bioprinting technology based on machine learning: a   CATMAID: collaborative annotation toolkit for massive
               review of recent trends and advances.  Micromachines.   amounts of image data.  Bioinformatics. 2009;25(15):
               2022;13(3):363.                                    1984-1986.
               doi:  10.3390/mi13030363                           doi: 10.1093/bioinformatics/btp266
            102. Narodoslawsky  M,  Krotscheck  C.  The  sustainable  process   114. Finny AS. 3D bioprinting in bioremediation: a
               index (SPI): evaluating processes according to environmental   comprehensive review of principles, applications, and future
               compatibility. J Hazard Mater. 1995;41(2):383-397.  directions. Peer J. 2024;12:e16897.
               doi: 10.1016/0304-3894(94)00114-V                  doi: 10.7717/peerj.16897
            103. Kokare S, Oliveira JP, Godina R. Life cycle assessment of   115. Almadan A, Li W, Al Ibrahim M. Transfer learning with
               additive manufacturing processes: a review.  J Manuf Syst.   domain adaptation for palynological image segmentation.
               2023;68:536-559.                                   Microsc Microanal. 2023;29(Supplement_1):1898-1899.



            Volume 11 Issue 4 (2025)                       152                            doi: 10.36922/IJB025170164
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