journal article·2026-06-01Open access

Dynamic 3D in vitro platform engineered via low-cytotoxic DLP 3D printing for enhanced ovarian follicle culture

Jeong-Hun Kang, Min-Kyung Seo, Mi-Seon Park, Jung-Hye Kim, Woo-Sik Lee, Sook-Young Yoon, Suk-Hee Park

Biofabrication· Volume 18· Issue 2

Abstract

Abstract To mimic the in vivo culture environment, an in vitro platform should not only provide structural support but also recreate the cellular microenvironment. However, for enhanced cell growth and function, it is essential to incorporate a three-dimensional (3D) architecture and dynamic stimulation that resemble the actual physiological conditions. Here, a dynamic 3D culture platform is developed using digital light processing (DLP) 3D printing with sub-millimeter-scale features. A custom-formulated resin with reduced photoinitiator and UV absorber was selected to minimize cytotoxicity and improve printing resolution. Grayscale modulation and structural reinforcement are employed to mitigate overcuring and undercuring in DLP-based fabrication, thereby enabling the fabrication of geometrically precise and mechanically stable microstructures. The platform incorporates a buoyant hollow structure, which facilitates fluid-induced gentle motion, providing dynamic stimulation that mimics in vivo mechanical cues. Mouse ovarian follicle growth is significantly enhanced by the dynamic 3D culture platform compared to static culture and platform-free conditions. This enhancement is attributed to both mechanical stimulation and lattice-based architecture, which supports nutrient diffusion and inter-follicular signaling. Overall, the proposed platform provides a robust strategy for physiologically relevant, long-term in vitro follicle culture, offering broad utility in reproductive tissue engineering, fertility preservation, and pharmacological screening.
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Research context

OpenAlex topics: 3D Printing in Biomedical Research · Advanced Materials and Mechanics · Reproductive Biology and Fertility

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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