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Ying Fang on Muck Rack

Ying Fang

As seen in: Color Research & Application, Nature, Wiley, Wiley Online Library, PLOS ONE, Frontiers, MDPI, Taylor & Francis Online, Biomed Central (BMC), Flipboard and
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Articles

Decoupling Sex from Emotional Commitments? Exploring Sexual Values of Female Sex Buyers in China

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By Ying Fang, Wan-Lan Huang, Cai-Zhen Ma
| Taylor & Francis Online
Abstract Objective This study examines the sexual values of female sex buyers in China, aiming to categorize their value systems and explore the underlying motivations and influencing factors behind their engagement in commercial sex. Method Based on in-depth interviews with 27 female sex buyers recruited through social media platforms and snowball sampling, qualitative data were analyzed to identify key themes and patterns related to sexual values and behaviors.
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Feasibility and safety of endoscopic retrograde appendiceal foreign body removal in children

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By Lina Sun, Bianhua Liu, Dan Ti Nan, Zhao Yang, Ying Fang, Xiaoxia Ren, Tianjiao Gao, Kuku Ge
| Frontiers
Abstract Objective: Endoscopic retrograde appendicitis therapy (ERAT), developed for acute appendicitis, enables minimally invasive access to the appendix for diagnosis and therapy. Based on the ERAT technique, endoscopic retrograde appendiceal foreign body removal has emerged as a logical yet technically advanced extension, offering a potential minimally invasive alternative to surgical appendectomy.
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AI framework for multidisease detection via retinal imaging

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By Xiayin Zhang, Qinyi Li, Yinhao Liang, Chunran Lai, Jiahui Cao, Yangqin Feng, WenYi Hu, Hongyang Jiang, Chunxin Liu, Feng Zhang, Shan Wang Verified, Ying Fang, Lumei Hu, Fan Xu, Kaiyi Chi, Miao Lin, Yih Chung Tham, Yukun Zhou, Carol Cheung, Xiaohong Yang, Zhuoting Zhu, Ching-Yu Cheng, Honghua Yu, Cuomu Duojie, W. Y. Ng
| Nature Verified
Abstract The rising burden of endocrine and metabolic diseases demands scalable and accessible screening tools. Here we developed Reti-Pioneer, a multitask retinal imaging framework that integrates quality-aware modules with pre-trained foundation models for efficient, multidisease detection.
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