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Xinying Wang on Muck Rack

Xinying Wang

As seen in: Nature, Wiley Online Library, New Phytologist, PLOS, BMJ Open, Frontiers, MDPI, Taylor & Francis Online, Royal Society of Chemistry, BioRxiv and
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Articles

Non-pharmacological interventions for blood pressure management in hypertensive patients: selection of exercise regimens—a systematic review and network meta-analysis

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By Renchao Lv, Xinying Wang, Hongfei Li, Zitong Yang, Ming Zhou, Liaokun Ye
| Frontiers
Abstract Objective: To compare and rank the effects of exercise modalities on blood pressure in adults with hypertension. Methods: We conducted a PRISMA-NMA–compliant systematic review and network meta-analysis. PubMed, the Cochrane Library, and Web of Science were searched through May 24, 2026, for randomized controlled trials of exercise interventions. Random-effects models were used, and interventions were ranked using surface under the cumulative ranking curve values.
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Targeting EHMT2 enhances NK cell maturation and antitumor activity

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By Minghang Yu, Xinhua Liu, Wenxiu Zhang, Xuefeng Huang, Yue Wu, Xinying Wang, Yuanyuan Chen, Ning Ding, Zilong Zhao, Xi Wang
| Nature Verified
Abstract Natural killer (NK) cells are critical for both innate and adaptive immunity, and their effective regulation holds significant promise for tumor immunotherapy. The terminal maturation of NK cells is closely associated with enhanced effector functions, but the role of epigenetic mechanisms in this process remains incompletely understood. Here, we identify the histone methyltransferase EHMT2 as an epigenetic regulator that restrains NK cell maturation and function.
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Comprehensive verification and correlation analysis of all factors of line loss data based on machine learning

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By Weiwu Li, Zhengying Liu, Xinying Wang, Tianshou Li
| Frontiers
Abstract Introduction: A comprehensive examination is conducted on the distinctive loss patterns within a 10 kV distribution network, and the intelligent detection of abnormal line loss is realized by combining machine learning method. Methods: The actual line loss data are first preprocessed. The K-means method is enhanced by integrating it with the Canopy algorithm for grouping line loss data. A genetic algorithm (GA)-optimized RBF neural network is then developed for line loss anomaly diagnosis.
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