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Jiaying Chen

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

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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Risk Factors and Adverse Perinatal Outcomes in Severe Preeclampsia Complicated by HELLP Syndrome

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By Wuxi Maternity, Jiaying Chen, Chengfang Sun
| Wiley Online Library Verified
1 Introduction HELLP syndrome is a severe complication of severe preeclampsia, characterized by hemolysis, elevated liver enzymes, and low platelet counts. It can pose life-threatening risks to both mothers and their newborns [1]. The incidence of HELLP syndrome ranges from 0.5% to 0.9% in healthy pregnancies, but rises to 2%–12% among patients with preeclampsia [2].
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Escalating transmissibility of severe fever with thrombocytopenia syndrome in a high-endemic region of China

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By Bingbing Wang, Na Chu, Hongjie Wei, Yulun Xie, Jiaying Chen, Fang Xie, Jia Rui, Tianmu Chen, Yanshu Ke
| Nature Verified
Abstract Severe fever with thrombocytopenia syndrome (SFTS) is an emerging public health concern with increasing incidence and geographic spread. Quantitative assessment of its cross-species transmission among humans, ticks, and animal hosts remains limited. Here, we develop and apply a multi-population, multi-route dynamic model using data from 3,883 reported cases in Anhui Province, China, from 2019 to 2023 to quantify transmissibility and characterize spatiotemporal patterns.
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