Skip to main content
Xueliang Zeng on Muck Rack

Xueliang Zeng

As seen in: Nature, Frontiers in Immunology, MDPI
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Xueliang

Contact Xueliang, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

PLSCR1 drives chemoresistance in TNBC via METTL3/IGF2BP3-mediated mRNA stabilization and EGFR-MAPK pathway activation

|
By Yao Lu, Xueliang Zeng, Shixiong Peng, Yangyang Zhan, Wenyu Liu, Rui Zhao, Jing Li, Qiang Huang, Tingting Ye, Zixuan Yuan, Panpan Huang
| Nature Verified
Abstract Triple-negative breast cancer (TNBC) frequently acquires chemoresistance, leading to poor clinical outcomes. Phospholipid scramblase 1 (PLSCR1) has been implicated in breast cancer progression, yet its precise role and underlying mechanisms in TNBC chemoresistance remain elusive. Here, we demonstrate that PLSCR1 is significantly upregulated in chemoresistant TNBC cell lines and patient samples.
Open in Who Shared Issue with article?

Crohn’s disease: research progress in decoding pathogenic multi-network and precision management of artificial intelligence radiomics

|
By Hua Xie, Xueliang Zeng, Xiaomin Liao, He Zeng, Qinghua Zou, Fan Meng, Wumiao Zhang, Shuyan Ying, Shengyan Hu, Dingcheng Zeng
| Frontiers in Immunology
Abstract Crohn’s disease (CD) is a chronic, relapsing inflammatory bowel disease characterized by transmural inflammation. Its clinical presentation and disease course are highly heterogeneous across individuals, and the global disease burden continues to rise.
Open in Who Shared Issue with article?

High-Accuracy Mapping of Soil Parent Material Types in Hilly Areas at the County Scale Using Machine Learning Algorithms

|
By Xi Guo, Yefeng Jiang, Weifeng Li, Xueliang Zeng
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Open in Who Shared Issue with article?
See all 3 articles