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Yining Chen

As seen in: Nature, Wiley Online Library, arXiv, PLOS, Frontiers, MDPI, PNAS, ACS Publications, Royal Society of Chemistry, BioRxiv and
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Articles

Developmental and MAPK-responsive transcription factors regulate distinct malignant cell states and associated genetic dependencies in pancreatic cancer

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By Pasquale Laise, Mikko Turunen, Alvaro Curiel-Garcia, Lorenzo Tomassoni, H. Carlo Maurer, Ela Elyada, Bernhard Schmierer, Jeremy M. Worley, Jordan Kesner, Xiangtian Tan, Ester Calvo Fernández, Yining Chen, Kelly Wong, Urszula Wasko, Somnath Tagore, Alexander Wang, Sabrina Ge, Alina Iuga, Aaron Griffin, Winston Wong Verified, Gulam A. Manji, Mariano J. Alvarez, Faiyaz Notta, David A. Tuveson, Kenneth Olive, Andrea Califano, Yuanqing Xue
| Nature Verified
Abstract There is broad consensus that the malignant epithelial cells of human pancreatic ductal adenocarcinoma (PDA) comprise multiple, molecularly distinct states. Yet precise characterization of how these are regulated—including their mechanistic determinants, dependencies, plasticity and functional properties—remains elusive.
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Regulating Hydrogen Bond Competition to Break Ultrawide-Temperature (−70°C to 100°C) Limits for All-Climate Zinc Batteries

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By Qiuyuan Feng, Yining Chen, Shaoxing Li
| Wiley Online Library Verified
Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that supports the findings of this study are available in the Supporting Information of this article. Supporting Information Filename Description anie73706-sup-0001-SuppMat.docx7 MB Supporting File: anie73706-sup-0001-SuppMat.docx.
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Assessing the Impact of MULLET AI System on Focal Liver Lesion Detection: A Multicenter, Multi-reader, Multi-case Clinical Trial

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By Zhongquan Sun, Yining Chen, Hongfan Ding, Ying Chen, Haoze Cao, Xin Han, Wanyi Chen, Lei Wu, Feng Fang, Fan Wu, Jiajun Bu, Yuan Ding, Weilin Wang, Lianyue Yang
| Nature Verified
Abstract Early detection of focal liver lesions is critical for patient outcomes, but current imaging techniques have limitations. This study developed MULLET, an AI system using contrast-enhanced CT, and assessed its performance in assisting radiologists with FLL detection and characterization. A retrospective multicenter, multi-reader, multi-case trial was conducted, in which 10 radiologists independently interpreted 375 patients’ clinical images with and without MULLET assistance.
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