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Chao Ji

As seen in: Environmental Health Perspectives, Nature, Wiley Online Library, American Journal of Clinical Dermatology, Frontiers, MDPI, The International Journal of Behavioral Nutrition and Physical Activity, Royal Society of Chemistry, Canadian Journal of Infectious Diseases & Medical Microbiology, Dove Medical Press and
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Articles

A Planarity-Hindrance Co-Balance Strategy to Develop Antiparallel H-Aggregates With Minimal Absorbance Blueshift for Type I Photodynamic Therapy

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By Yubo Liu, Chao Ji, Zhangke Sun
| Wiley Online Library Verified
Yubo Liu and Chao Ji contributed equally to this work. , , , , , and , “A Planarity-Hindrance Co-Balance Strategy to Develop Antiparallel H-Aggregates With Minimal Absorbance Blueshift for Type I Photodynamic Therapy.” Advanced Materials (2026): e73868. https://doi.org/10.1002/adma.73868 Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Correction: Exploring the role of ferroptosis in pemphigus: identification of diagnostic markers and regulatory mechanisms

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By Jing Mao, Jianping Lan, Ying Chen, Xinhong Su, Xueting Zeng, Zequn Tong, Xiaoqing Lv, Hui Ke, Ying Zou, Chao Ji, Ting Gong, Zheyu Zhuang, Yushan Ou, Fuchen Huang, Zhenlan Wu
| Frontiers
A Correction on Exploring the role of ferroptosis in pemphigus: identification of diagnostic markers and regulatory mechanisms by Mao, J., Lan, J., Zhuang, Z., Chen, Y., Ou, Y., Su, X., Zeng, X., Huang, F., Tong, Z., Lv, X., Ke, H., Wu, Z., Zou, Y., Cheng, B., Ji, C., and Gong, T. (2025). Front. Med 12:1615865. doi: 10.3389/fmed.2025.1615865 Affiliation 1 “Department of Dermatology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China” was omitted for author Ting Gong.
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Multimodal deep learning fusion model for assessment of fetal lung development in gestational diabetes mellitus and pre-eclampsia

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By Yanran Du, Chao Ji, Jing Jiao, Fei Xin, Yunyun Ren, Zhenwei Xia, Yi Guo, Jianqiao Zhou
| Frontiers
Abstract Background: Fetal lung development is highly sensitive to adverse intrauterine conditions such as gestational diabetes mellitus (GDM) and pre-eclampsia (PE). Current clinical evaluation mainly relies on ultrasound imaging, but it provides limited information on related histological and molecular changes. This study aimed to develop a multimodal deep learning framework that combined ultrasound imaging features with molecular and histopathological data to assess fetal lung development.
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