Rongbiao Tang, Yanan Fu, Ke Li, Feixiang Wang, Ke-Min Chen
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引用次数: 0
Abstract
Micro and small organisms (MSOs) are essential components of the ecosystem. Many MSOs reproduce by hatching eggs, making it crucial to study the morphology of these eggs and their incubation products (IPs) in related research. Phase-contrast CT (PCCT) is a powerful imaging modality known for its high resolution and sensitivity to soft tissues. In this study, an ultrafast PCCT system was used to scan brine shrimp eggs (BSEs) before hatching to determine their viability. High-resolution PCCT was used to reveal the microstructures of BSEs and IPs. We found that normal BSEs have an exclusively regular structure, making them easily identifiable. The use of ultrafast PCCT not only allowed for quick determination of BSE viability but also significantly reduced the amount of irradiation exposure to the eggs. All of the normal BSEs that were tested successfully hatched into brine shrimp, demonstrating the high safety of ultrafast PCCT. The high-resolution PCCT images clearly showed the formation of hatching membranes, cracks, and deformable bodies during the hatching process. The results suggest that ultrafast PCCT has the potential to assess the viability of MSO eggs, while high-resolution PCCT can provide valuable insight into the morphological changes that occur during the hatching process.
期刊介绍:
APL Bioengineering is devoted to research at the intersection of biology, physics, and engineering. The journal publishes high-impact manuscripts specific to the understanding and advancement of physics and engineering of biological systems. APL Bioengineering is the new home for the bioengineering and biomedical research communities.
APL Bioengineering publishes original research articles, reviews, and perspectives. Topical coverage includes:
-Biofabrication and Bioprinting
-Biomedical Materials, Sensors, and Imaging
-Engineered Living Systems
-Cell and Tissue Engineering
-Regenerative Medicine
-Molecular, Cell, and Tissue Biomechanics
-Systems Biology and Computational Biology