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Yinghao Cao on Muck Rack

Yinghao Cao

As seen in: Color Research & Application, Nature, Wiley Online Library, Frontiers, ACS Publications, BMC Cancer, BioRxiv, Dove Medical Press, International Journal of Medical Sciences
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Articles

Authentic Spatial Metabolomics by Avoiding Hidden Pitfalls in MSI Workflows Click to copy article link Article link copied!

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By JInyu Zhou, Shengxi Li, Yinghao Cao
| ACS Publications
Abstract Click to copy section linkSection link copied! Mass spectrometry imaging (MSI) has emerged as a powerful analytical tool for visualizing the spatial distribution of endogenous molecules in complex biological tissues. However, achieving authentic and reproducible metabolic maps remains challenging due to pitfalls in sample preparation and instrumental limitations that are often overlooked.
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Targeting mitochondrial α-ketoglutarate sequestration disables dual oncogenic drivers and metabolic adaptability in pancreatic ductal adenocarcinoma

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By Yeting Xu, Hongjiang Guo, Shengxi Li, Yucheng Wang, Yinghao Cao, Lin Wang, Qiuxia Chen, Ziyi Qin, Jiaxing Qiu, Yuhan Liu, Yunfan Li, Jiaming Zou, Yingying Wang, Kaihan Zhang, Rui Ju, Lei Guo
| Nature Verified
Abstract Pancreatic ductal adenocarcinoma (PDAC) exhibits hyperactive mitochondrial metabolism, yet how this rewiring spatially restricts the availability of metabolites for oncogenic signaling and drives systemic metabolic dysregulation in PDAC remains unknown. Here, we identify enhanced mitochondrial α-ketoglutarate (α-KG) sequestration as a key metabolic vulnerability in PDAC.
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Artificial intelligence for the prediction of prognosis in colorectal cancer patients using routine blood indices

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By Shan Tian, Jinxiao Li, Qian Liu, Yugang Hu, Zhuolun Sun, Bingjie Yang, Pu Zhou, Jiao Li, Le Qin, Dong Sun, Huan H. Cao, Yinghao Cao
| Nature Verified
Abstract Overall survival (OS) of colorectal cancer (CRC) patients remains suboptimal, especially in advanced disease. This study aimed to construct and validate explainable machine learning (ML) models using routine blood indices for accurate CRC prognosis across multicenter cohorts. The training cohort included 850 CRC patients (demographic and routine blood data) from Union; validation cohorts were 403 patients (Hefei) and 217 (Shihezi).
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