Skip To Main Content
Ting Yin on Muck Rack

Ting Yin

As seen in: Color Research & Application, Nature, Wiley Online Library, New Phytologist, PLOS, Frontiers, Frontiers in Public Health, MDPI, ACS Publications, Science Magazine and
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 Ting

Contact Ting, 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

Deep learning prediction of pathological complete response in breast cancer using Mamba architecture

|
By Wenchuan Zhang, Shuwan Zhang, Fengling Li, Yuanyuan Zhao, Jing Fu, Ting Yin, Qingjie Lv, Yuhao Yi, Hong Bu, Xiuli Xiao
| Nature Verified
Abstract Deep learning is capable of efficiently predicting the therapeutic efficacy of neoadjuvant chemotherapy (NAC) in breast cancer. However, current methods predominantly rely on convolutional neural networks or transformer architectures and are often validated in small patient cohorts.
Open in Who Shared Issue with article?

Association of sarcopenia and muscle attenuation with clinical outcomes in patients with diabetic foot: a systematic review and meta-analysis

|
By Lei Wang, Jiaqi Yang, Xuan Li, Bing Wu, Ting Yin, Yi Zhao
| Frontiers
Abstract Background: Diabetic foot (DF) represents a severe and disabling complication of diabetes, closely linked to ulceration, amputation, functional impairment, and an elevated risk of death. Sarcopenia is a syndrome marked by progressive declines in muscle mass and function. Emerging evidence suggests a possible link between DF and sarcopenia. Nonetheless, a systematic evaluation of the influence of sarcopenia on adverse clinical outcomes in individuals with DF remains unavailable.
Open in Who Shared Issue with article?

Performance evaluation of large language models in the diagnosis of emergency internal medicine diseases: a retrospective study

|
By Jintao Wei, Shouyin Jiang, Ting Yin, qiang li, Yu Tian, Mengting Yan, Ximei Ma, Shanxiang Xu, Mao Zhang, Jiangchun Ma, Zhuyi Shen, Xiangkang Lv
| Frontiers in Public Health Verified
Abstract Objective: Medical domain large language models (LLMs) exhibit verified clinical decision-support capabilities in simulated case analyses and standardized tests, yet their diagnostic efficacy in real-world emergency settings remain insufficiently explored. This study evaluates the diagnostic performance of 5 mainstream LLMs (ChatGPT-4o, Gemini-2.0, Grok3, DeepSeek-V3, Doubao) against emergency department junior physicians (EDJP) on real-world emergency internal medicine cases.
Open in Who Shared Issue with article?
See all 26 licensed articles (27 Total)