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Jiaying Chen

As seen in: Nature, Wiley Online Library, Frontiers, Frontiers in Digital Health, MDPI, Taylor & Francis Online, ACS Publications, Royal Society of Chemistry, Cell Press, Canadian Journal of Infectious Diseases & Medical Microbiology and
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Articles

Quality, source transparency, information reliability, and domain coverage of short video-based information on feeding guidance for people with dysphagia: a cross-sectional study on 4 major Chinese social media platforms

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By Jiaying Chen, Ting Xiong, Ying Li, YUE WANG, Chen Yang, Jinghui Huang, Fanfu Fang
| Frontiers in Digital Health
Abstract Background: Standardized feeding guidance is valuable for people with dysphagia, helping prevent aspiration, improve nutritional status, and enhance quality of life. In recent years, short-video platforms have become a key avenue for the public to access health information, providing people with dysphagia with a convenient channel to learn about feeding management.
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Freezing-induced solid-liquid microphase separation for self-assembly of functional nucleic acid nanoparticles

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By Tao Zeng, Jiahui Fang, Jian Hao, Xiuping Cao, Shiqing Li, Shiyan Bai, Jiaying Chen, Chunsen Li, Huanghao Yang, Chunhua Lu
| Nature Verified
Abstract In nature, nuclear DNA complexed with histone form highly condensed chromatin via loop extrusion and microphase separation, yielding physically constrained solid-like structures. Inspired by this natural compaction, here we demonstrate that freezing-induced crystallization pressure and freeze-concentration effects trigger solid-liquid microphase separation for efficient DNA compression.
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Fraud learns too: continual graph learning under strategic adversarial drift in dynamic networks

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By Huijie Fan, Yanan Jiao, Mengdie Wang, Jiaying Chen, Shiyu Yang
| Nature Verified
Abstract Financial transaction networks face a persistent threat from strategic adversarial drift, in which sophisticated actors manipulate graph structure to bypass detection. Conventional temporal graph neural networks tend to fail in this setting because they forget historical patterns when retrained and generalise poorly to novel structural perturbations.
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