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Ying Luo on Muck Rack

Ying Luo

As seen in: Color Research & Application, Nature, Wiley Online Library, Yahoo News Australia, Memory Studies, PLOS, PLOS Medicine, ScienceDirect, BMJ Open, Frontiers and
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Articles

Photo-Generated 1 O 2 Triggers In Situ RNA Crosslinking for High-Contrast Tumor MRI and Effective Tumor Suppression

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By Ying Luo, Weizhong Ding, Ming Jia
| Wiley Online Library Verified
1 Introduction Magnetic resonance imaging (MRI) has emerged as a powerful modality for the detection of hepatocellular carcinoma (HCC) owing to its nonionizing nature, unlimited tissue penetration depth, and high spatial resolution [1, 2]. Nevertheless, the diagnostic performance of MRI critically relies on contrast agents.
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Nuclear class 3 PI3K co-activates fasting-specific chromatin remodelling

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By Nathaniel F. Henneman, Genki Kawamura, Ying Luo, Cátia Dias, Jiaqi Su, Ivan Nemazanyy, Samuel Le Cam, Calvin Rodrigues, Nicolas Cagnard, Karim Hnia, Raphaël Margueron, Dominik Lutter, Takeaki Ozawa, Ganna Panasyuk, Arianna Segaloni, Nicolas Kuperwasser
| Nature Verified
Abstract Transcriptional remodelling during fasting ensures metabolic adaptation and provides health benefits across species. Although several regulators of fasting-induced transcription and chromatin are known, how nutrient levels directly influence RNA polymerase II (RNAPII) and epigenetic writers remains unclear.
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Development and validation of a cytokine-based diagnostic model for Kikuchi–Fujimoto disease

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By Xiaona Zhu, Chen Liu, Zhi Yang, Yanyan Huang, Ying Luo, Jun Yang, Tingyan He
| Frontiers in Immunology
Abstract Objective: The diagnosis of Kikuchi–Fujimoto disease (KFD) is challenging, which is mainly based on typical histopathological findings from a lymph node biopsy. We aimed to develop and validate a predictive model for the diagnosis of KFD based on cytokine profiles and other laboratory findings. Methods: This study included pediatric patients with KFD and controls with other febrile diseases in a retrospective training cohort (n = 673) and a prospective validation cohort (n = 116).
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