Ning Li
Guangzhou Medical University
11 Papers
Ning Li is an academic researcher from Guangzhou Medical University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 2, co-authored 5 publications.
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Papers
Krebs Von den Lungen-6 as a predictive indicator for the risk of secondary pulmonary fibrosis and its reversibility in COVID-19 patients.
Mingshan Xue,Teng Zhang,Hao Chen,Yifeng Zeng,Runpei Lin,Yingjie Zhen,Ning Li,Zhifeng Huang,Haisheng Hu,Luqian Zhou,Hui Wang,Xiaohua Douglas Zhang,Baoqing Sun +12 more
TL;DR: KL-6 could be used as an important predictor to evaluate the secondary pulmonary fibrosis degree for COVID-19 and the survival curves for days in hospital show that the higher the KL-6 levels, the longer the hospital stay (P<0.0001).
The sensitization characteristics of adult Chinese patients diagnosed with chronic respiratory diseases.
TL;DR: Patients with CRD had high sensitization, and Asthma patients who work indoors were more susceptible to allergies, and atopy was associated with COPD pulmonary function.
Value of immune factors for monitoring risk of lung cancer in patients with interstitial lung disease
TL;DR: Optimal scaling analysis demonstrated that lung cancer was closely associated with CRP, CER, C3, and C4, and patients with ILD at high risk of developing lung cancer were identified.
5
Soluble form of suppression of tumorigenicity-2 predicts clinical stability of inpatients with community-acquired pneumonia.
Yifeng Zeng,Mingshan Xue,Teng Zhang,Shixue Sun,Runpei Lin,Ning Li,Peiyan Zheng,Yingjie Zhen,Haisheng Hu,Xiaohua Douglas Zhang,Baoqing Sun +10 more
TL;DR: The soluble form of the suppression of tumorigenicity-2 (sST2) is a biomarker for risk classification and prognosis of heart failure, and its production and secretion in the alveolar epithelium are...
5
Incorporation of Suppression of Tumorigenicity 2 into Random Survival Forests for Enhancing Prediction of Short-Term Prognosis in Community-ACQUIRED Pneumonia
Teng Zhang,Yifeng Zeng,Runpei Lin,Mingshan Xue,Mingtao Liu,Yusi Li,Yingjie Zhen,Ning Li,Wenhan Cao,Sixiao Wu,Huiqing Zhu,Qiang Zhao,Bao-hua Sun +12 more
TL;DR: The RSF model by incorporating sST2 was more accurate than traditional methods in assessing the short-term prognosis of CAP patients and was associated with adverse clinical events during hospitalization, ICU admissions, and short- term mortality.