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Recent advances in sex-specific reproductive microphysiological systems for the systematic evaluation of assisted reproductive technology.

PubMed
Authors: Jeong M, Park K, Jung D, Shin S, Lee J, Ko J, Bang S, Ahn J

Year

2026

Paper ID

67486

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

215

Citations

N/A

Abstract

The rising global burden of infertility continues to increase clinical reliance on assisted reproductive technology (ART), yet overall outcomes remain constrained by limited physiological fidelity of conventional two-dimensional (2D) culture and animal models. Microphysiological systems (MPSs) offer an platform with enhanced human physiological relevance that can reconstruct reproductive microenvironments through engineered compartmentalization, quantitative fluid flow control, and stable long-term culture. Here, we systematically map recent advances in sex-specific reproductive MPS across the ART timeline, organizing platforms into (i) pre-IVF models for gamete production, maturation, and selection, (ii) IVF-stage models enabling controlled gamete interaction, manipulation, and fertilization, and (iii) post-IVF models that cover embryo implantation, early endometrial remodeling, and immune adaptation at the maternal-fetal interface. We further evaluate multi-organ and body-on-a-chip approaches that integrate reproductive tissues with systemic modules to quantify reproductive and developmental toxicity, with attention to sex-dependent toxicological responses. Finally, we present a perspective on future directions. Near-term priorities include integrating digital sensors and artificial intelligence (AI) to enable multimodal data acquisition and decision support. Mid-term efforts will need to focus on benchmarking, reproducibility, and clinical validation, and long-term directions aim toward scalable, therapeutic reproductive tissues and organs. Collectively, reproductive MPSs are positioned to bridge mechanistic reproductive clinically actionable ART innovation, enabling more predictive modeling of fertility, implantation competence, and risks associated with pregnancy.

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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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  • The rising global burden of infertility continues to increase clinical reliance on assisted reproductive technology (ART), yet overall outcomes remain constrained by limited...

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