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Xiaoying Sun on Muck Rack

Xiaoying Sun

As seen in: Nature, Wiley Online Library, Harm Reduction Journal, Annals of the Rheumatic Diseases, Archives of Disease in Childhood, MDPI, Arthritis Research & Therapy, Royal Society of Chemistry, Cell Press, The Lancet and
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Articles

Distinct immune microenvironment in HER2-low triple-negative breast cancer underlies inferior response to immunotherapy

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By Hewei Ge, Yuhan Wei, Yalong Qi, Hongnan Mo, Jingtong Zhai, Cheng Zeng, Xiaoying Sun, Fei Ma
| Nature Verified
Abstract Effective predictive biomarkers for immunotherapy in breast cancer remain elusive, limiting precision treatment strategies. Although the biological significance of HER2‑low expression has gained increasing attention, its impact on immunotherapy response remains unknown.
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Microtopography-Driven Nitrogen Pollution in Rice Fields Under Extreme Rainfall: Mechanisms and Environmental Risks Revealed by Experiments and HYDRUS-3D Simulation

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By Jin Wu Verified, Xiaoying Sun, Juxiu Tong
| Wiley Online Library Verified
Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The datasets generated and/or analysed during the current study are not publicly available due to further dissertation writing but are available from the corresponding author on reasonable request. Supporting Information Filename Description hyp70564-sup-0001-Tables.docxWord 2007 document , 34.5 KB Table S1: NH4–N Peak concentrations and occurrence times after each fertilization.
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A natural language processing to causality framework for robust knowledge extraction of CO₂ hydrogenation with batch effect control

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By Weihang Xu, Lihua Bai, Ji Qi, Xiaoying Sun, BaiRan Wang, Zhen Zhao
| Nature Verified
Abstract The combination of natural language processing and machine learning is transforming materials discovery paradigm through scientific literature extraction and intelligent materials discovery. However, batch effects, systematic inter-laboratory differences, represent critical confounding factors that compromise the prediction of reliable structure-activity relationships.
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