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Rong Yin

As seen in: Color Research & Application, Nature, Wiley Online Library, arXiv, PLOS, PLOS ONE, Frontiers, MDPI, Cell Reports, Cell Stem Cell and
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Articles

Usability and feasibility of Mai 20, a user-centered mHealth App for secondary stroke prevention in China: mixed methods study

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By Jing Yuan, Shujian Zhang, Sun Run, Minghui Li, Rong Yin, Jing Zhao, Shiliang Xia, Jialong Yun, Xiuman Xu
| Nature Verified
Abstract Stroke survivors in China face substantial barriers to secondary prevention, including limited access to education and risk-factor self-management support. This study aimed to evaluate the short-term usability and feasibility of Mai 20, a user-centered mobile health (mHealth) app designed to support secondary stroke prevention and risk management. A prospective mixed-methods feasibility study was conducted at Minhang Hospital in Shanghai, China.
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Stress granules restrain ferroptosis by sequestering ferritin

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By Zehe Ge, Zihan Wang, Mengdie Li, Yunfei Ma, Fangshu Ding, Liuguijie He, Shuhong Sun, Minfeng Tong, Wenxia Xu, Xiefeng Wang, Junxia Zhang, Lingxiang Wu, Yudan Zhang, Lili Niu, Qianghu Wang, Rong Yin, Xiao Li, Yongping You, Jeremy Rich, Xiuxing Wang, Bing Yao, Zhumei Shi, Zhou Yang, Xin Ge, Fang Liu, Bo Chu, Xu Qian, Erjie Zhao, Yizhuo Guo, Longxiu Zhang, Fuwei Tian
| Nature Verified
Abstract Glioblastoma stem cells (GSCs) are refractory to first-line treatment in the clinic, which includes irradiation (IR) and temozolomide (TMZ). Here we find that disrupting stress granules (SGs) sensitizes GSCs to IR/TMZ through ferroptosis. The profiling of SG proteins reveals the recruitment of iron-related proteins including ferritin.
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Integrative fragmentomic and mutational signature profile of plasma cfDNA for early lung cancer detection

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By Jianxing He, Huiting Wang, Yi Feng, Jianfu Li, Peiling Chen, Xin Zheng, Wenhai Fu, Caichen Li, Hua Bao, Song Wang, Shuang Chang, Dongqin Zhu, Shanshan Yang, Yang Shao, Wen Hui Zhong, Weisheng Guo, Rong Yin, Wenhua Liang
| Nature Verified
Abstract Detecting lung cancer effectively in the general population is essential for optimizing treatment outcomes and improving the 5-year survival rate. While low-dose computed tomography (LDCT) is the current standard, it has limitations in broader populations. We developed a blood-based multi-omics model using whole-genome cell-free DNA (cfDNA) features to distinguish lung cancer from non-cancer individuals.
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