Purpose The design process of a bio-model involves multiple factors including data acquisition technique, material requirement, resolution of the printing technique, cost-effectiveness of the printing process and end-use requirements. This paper aims to compare and highlight the effects of these design factors on the printing outcome of bio-models. Design/methodology/approach Different data sources including engineering drawing, computed tomography (CT), and optical coherence tomography (OCT) were converted to a printable data format. Three different bio-models, namely, an ophthalmic model, a retina model and a distal tibia model, were printed using two different techniques, namely, PolyJet and fused deposition modelling. The process flow and 3D printed models were analysed. Findings The data acquisition and 3D printing process affect the overall printing resolution. The design process flows using different data sources were established and the bio-models were printed successfully. Research limitations/implications Data acquisition techniques contained inherent noise data and resulted in inaccuracies during data conversion. Originality/value This work showed that the data acquisition and conversion technique had a significant effect on the quality of the bio-model blueprint and subsequently the printing outcome. In addition, important design factors of bio-models were highlighted such as material requirement and the cost-effectiveness of the printing technique. This paper provides a systematic discussion for future development of an engineering design process in three-dimensional (3D) printed bio-models.
ARPE-19 and Y79 cells were precisely and effectively delivered to form an in vitro retinal tissue model via 3D cell bioprinting technology. The samples were characterized by cell viability assay, haematoxylin and eosin and immunofluorescent staining, scanning electrical microscopy and confocal microscopy, and so forth. The bioprinted ARPE-19 cells formed a high-quality cell monolayer in 14 days. Manually seeded ARPE-19 cells were poorly controlled during and after cell seeding, and they aggregated to form uneven cell layer. The Y79 cells were subsequently bioprinted on the ARPE-19 cell monolayer to form 2 distinctive patterns. The microvalve-based bioprinting is efficient and accurate to build the in vitro tissue models with the potential to provide similar pathological responses and mechanism to human diseases, to mimic the phenotypic endpoints that are comparable with clinical studies, and to provide a realistic prediction of clinical efficacy.
The biological, structural and functional configuration of Bruch's membrane (BM) is significantly relevant to age-related macular degeneration (AMD) and other chorioretinal diseases, and AMD is one of the leading causes of blindness in the elderly worldwide. The configuration may worsen along with the ageing of retinal pigment epithelium and BM that finally leads to AMD. Thus, the scaffold-based tissue-engineered retina provides an innovative alternative for retinal tissue repair. The cell and material requirements for retinal repair are discussed including cell sheet engineering, decellularised membrane and tissue-engineered membranes. Further, the challenges and potential in realising a whole tissue model construct for retinal regeneration are highlighted herein. This review article provides a framework for future development of tissue-engineered retina as a preclinical model and possible treatments for AMD.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.