Yong Zeng on Muck Rack

Yong Zeng

  • Contributor, Pubs.rsc.org
Lawrence
As seen in: Nature, Wiley Online Library, PLOS, ScienceDirect, Frontiers, Frontiers in Public Health, MDPI, Taylor & Francis Online, ACS Publications, Biomed Central (BMC) and
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Articles

Detection and classification of lymphoma from cell-free methylome data

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By Victoria Shelton, Ting Liu, Davidson Zhao, Pamela Alamilla, Althaf Singhawansa, Michael Hong, Yong Zeng, Vanessa Murad, Ur Metser, David Hodgson, Tomohiro Aoki, Anca Prica, John Kuruvilla, Michael Crump, Bernard Lam, Michael Hoffman, Scott V. Bratman, Robert Kridel, Mohamed Alias, Ibrahim Alrekhais
| Nature
Abstract Diagnosing lymphoma traditionally relies on invasive tissue biopsies, which can yield insufficient material for histopathological evaluation and carry a risk of complications. Minimally invasive assessment of cell-free DNA (cfDNA) in plasma offers a promising alternative for lymphoma detection that could aid the rapid evaluation of malignant vs. benign lymphadenopathy.
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An interpretable machine learning model for predicting stroke in patients with hypertension: insights from the SPRINT

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By Jiayi Han, Li Lin, Yang Zhang, Zehao Zhao, Wenjie Wang, Yaodong Ding, Yong Zeng, Tengxiao Zhao, Ruixiang Feng
| Nature
Abstract Stroke is a leading cause of mortality worldwide, with hypertension being its most significant risk factor. However, few studies have specifically developed predictive models for stroke risk in hypertensive patients with elevated cardiovascular risk. This study aims to develop and validate an interpretable predictive model based on machine learning (ML) methods to assess stroke risk in hypertensive patients, enabling early prevention and improved prognosis.
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Widefield SS-OCTA-detected retinal vascular changes and their correlation with DME in diabetes

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By Chuanzhen Zheng, Yang Liu, Miao Liu, Wenting Sun, Shiya Tang, Chuntao Lei, Yong Zeng, Ruolan Ling, Huaqin Xia
| Frontiers
Abstract Purpose: This study investigated retinal blood flow alterations in diabetic macular edema (DME) using ultra-widefield optical coherence tomography angiography (OCTA). Methods: We enrolled 31 healthy controls (NC, 56 eyes), 40 diabetes patients without diabetic retinopathy (DM, 79 eyes), 25 non-proliferative diabetic retinopathy patients (NPDR, 47 eyes), and 42 DME patients (DME, 54 eyes).
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