Zejun Fan

ORCID: 0000-0002-9866-853X
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About
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Research Areas
  • 3D Printing in Biomedical Research
  • Yersinia bacterium, plague, ectoparasites research
  • Bacillus and Francisella bacterial research
  • Microfluidic and Bio-sensing Technologies
  • Osteoarthritis Treatment and Mechanisms
  • Proteoglycans and glycosaminoglycans research
  • Electrohydrodynamics and Fluid Dynamics
  • AI in cancer detection

King Abdullah University of Science and Technology
2024

Center for Life Sciences
2023-2024

Tsinghua University
2023-2024

The regeneration of hierarchical osteochondral units is challenging due to difficulties in inducing spatial, directional and controllable differentiation mesenchymal stem cells (MSCs) into cartilage bone compartments. Emerging organoid technology offers new opportunities for regeneration. In this study, we developed gelatin-based microcryogels customized using hyaluronic acid (HA) hydroxyapatite (HYP), respectively (denoted as CH-Microcryogels OS-Microcryogels) through vivo self-assembly...

10.1016/j.bioactmat.2023.04.002 article EN cc-by-nc-nd Bioactive Materials 2023-04-10

Abstract Cell encapsulation technology, crucial for advanced biomedical applications, faces challenges in existing microfluidic and electrospray methods. Microfluidic techniques, while precise, can damage vulnerable cells, conventional methods often encounter instability capsule breakage during high‐throughput encapsulation. Inspired by the transformation of working state from unstable dripping to stable jetting triggered local electric potential, this study introduces a superimposed field...

10.1002/adhm.202400780 article EN Advanced Healthcare Materials 2024-06-08

Abstract Recent advances in human blastoids have opened new avenues for modeling early development and implantation. Human can be generated large numbers, making them suitable high-throughput screening, which often involves analyzing vast numbers of images. However, automated methods evaluating characterizing blastoid morphology are still underdeveloped. We developed a deep-learning model capable recognizing classifying brightfield images into five distinct quality categories. The processes...

10.1101/2024.12.05.627041 preprint EN cc-by-nc-nd bioRxiv (Cold Spring Harbor Laboratory) 2024-12-09
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