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Lili Chen

As seen in: Environmental Health Perspectives, Nature, Wiley Online Library, ScienceDirect, BMJ Global Health, BMJ Open Diabetes Research & Care, Frontiers, Frontiers in Public Health, MDPI, Taylor & Francis Online and

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Articles

Disease landscape–guided design of neutrophil-specific reporters for early and accurate pneumonia detection in vivo and in urine

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By Duncan J Berger, Xinwei Guo, Bingjie Zhang, Zhen Li, Keyang Li, Maochao Zheng, Lili Chen, Jialong Sun, Yanhui Li, Shasha He, Huayu Tian
| Science Magazine Verified
Abstract INTRODUCTION RESULTS DISCUSSION MATERIALS AND METHODS Acknowledgments Supplementary Materials REFERENCES Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Accurate and early detection of pneumonia is crucial for effective treatment; however, current diagnostic methods often lack the necessary specificity and sensitivity.
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Single-Encounter Augmented Reality–Guided Localization for Resection of Suspected Early-Stage Lung Cancer : A Randomized Clinical Trial

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By Ziang Wang, Haixia Yao, Qing Tian, Rang Liu, Lili Chen, Ming Sun, Wenyan Ma, Xiaowei Gong, Chao Sun, Junjie Xi, Zhencong Chen, Yuming Wang, Mario Pezzella, Huining Liu, Xinghua Cheng, Zuodong Song, Lingming Yu, Chan Siang Kan, Congrong Gao
| JAMA Network Verified
Single-Encounter Augmented Reality–Guided Localization for Resection of Suspected Early-Stage Lung Cancer: A Randomized Clinical Trial Key Points Question Is single-encounter, augmented reality (AR)–guided percutaneous localization noninferior to multiple-encounter computed tomography (CT)–guided localization for achieving successful sublobar resection of CT-detected pulmonary nodules suspicious for early-stage lung cancer?
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Interactive recommendation algorithm based on intelligent assistance for project application

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By Changcheng Shao, Liang Luo, Lili Chen, Pinghua Chen, Qianyu Zou, Zhouqiang Qiu, Feixian Guan, Hongsong Zheng
| Nature Verified
Abstract To address the critical issues in project application management, such as inaccurate and inadequate understanding of guidelines, non-standardized preparation of application materials, and time-consuming formal reviews, this paper proposes a Knowledge Graph-Enhanced Interactive Recommendation Intelligent Assistant Model for Project Applications.
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