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 Yingjing
Contact Yingjing, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck RackActions
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.Articles
Predicting Prognosis for Gastric Cancer Patients Receiving Neoadjuvant Treatment With Body Composition-Based Deep Learning
1 Introduction Gastric cancer (GC) is the fifth most common malignancy and the fifth leading cause of cancer-related death worldwide. In China, GC remains a major health burden, with over 358,000 new cases and 260,000 deaths annually, accounting for approximately 40% of GC-related deaths worldwide [1, 2]. Around 80% of cases are diagnosed at an advanced stage, resulting in a poor prognosis [3]. Improving outcomes for these patients is imperative, albeit challenging.
Tailoring ferroelectric-semiconductor heterointerfaces generates lightly correlated polarization for efficient electrocaloric cooling
|
Keywords electrocaloric effect ferroelectric polymer semiconductor heterointerface polarization orientation polar entropy nanoflower COP TEWI Get full text access Log in, subscribe or purchase for full access. References 1. Shi, J. ∙ Han, D. ∙ Li, Z. ... Electrocaloric Cooling Materials and Devices for Zero-Global-Warming-Potential, High-Efficiency Refrigeration Joule. 2019; 3:1200-1225 2. Sherman, P. ∙ Lin, H. ∙ McElroy, M.
By qiang li, Tiannan Yang, Xin Chen, Haixin Qiu, Yingjing Zhang, Donglin Han, Shanyu Zheng, Cenling Huang, Ruhong Luo, Tian Yao, fei chen, Feihong Du, Yifan Zhao, Zhenhua Ma, Yezhan Lin, Chenyu Guo, Haotian Chen, Feiyu Zhang, Chunyu Wang, Lu Yu, Jiangping Chen, Dahong Qian, Guang Meng, Qiulong Wei, Xiaoshi Qian
|
Joule
Verified
Sex-specific adipose-muscle patterns from CT reveal metabolic syndrome risk in the elderly with machine learning
Keywords Metabolic syndrome Muscle Adipose Machine learning Get full text access Log in, subscribe or purchase for full access. References 1. Alipour, P. ∙ Azizi, Z. ∙ Raparelli, V. ... Role of sex and gender-related variables in development of metabolic syndrome: a prospective cohort study Eur J Intern Med. 2024; 121:63-75 2. Huang, P.L. A comprehensive definition for metabolic syndrome Dis Model Mech. 2009; 2:231-237 3. Borga, M. ∙ West, J. ∙ Bell, J.D. ...
Actions
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.Get in touch with Yingjing
Contact Yingjing, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack