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Your search keyword '"Guan, Yue"' showing total 41 results
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41 results on '"Guan, Yue"'

2. Predicting the Onset of Ischemic Stroke With Fast High‐Resolution 3D MR Spectroscopic Imaging

Author

Lin, Zengping, primary, Meng, Ziyu, additional, Wang, Tianyao, additional, Guo, Rong, additional, Zhao, Yibo, additional, Li, Yudu, additional, Bo, Bin, additional, Guan, Yue, additional, Liu, Jun, additional, Zhou, Hong, additional, Yu, Xin, additional, Lin, David J., additional, Liang, Zhi‐Pei, additional, Nachev, Parashkev, additional, and Li, Yao, additional

Published
2023
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3. Genetic counseling in diabetes mellitus: A practice resource of the National Society of Genetic Counselors

Author

Maloney, Kristin A., primary, Mizerik, Elizabeth, additional, King, Robin H., additional, McGinnis, Erin M., additional, Perkowitz, Susan, additional, Diamonstein, Callie J., additional, Schmanski, Andrew A., additional, Saliganan, Sheila, additional, Shipper, Andrea G., additional, Udler, Miriam S., additional, Guan, Yue, additional, and Pollin, Toni I., additional

Published
2023
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15. IGNITE network: Response of patients to genomic medicine interventions

Author

Orlando, Lori A., primary, Voils, Corrine, additional, Horowitz, Carol R., additional, Myers, Rachel A., additional, Arwood, Meghan J., additional, Cicali, Emily J., additional, McDonough, Caitrin W., additional, Pollin, Toni I., additional, Guan, Yue, additional, Levy, Kenneth D., additional, Ramirez, Andrea, additional, Quittner, Alexandra, additional, and Madden, Ebony B., additional

Published
2019
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25. Assessment of histological differentiation in gastric cancers using whole-volume histogram analysis of apparent diffusion coefficient maps

Author

Zhang, Yujuan, primary, Chen, Jun, additional, Liu, Song, additional, Shi, Hua, additional, Guan, Wenxian, additional, Ji, Changfeng, additional, Guo, Tingting, additional, Zheng, Huanhuan, additional, Guan, Yue, additional, Ge, Yun, additional, He, Jian, additional, Zhou, Zhengyang, additional, Yang, Xiaofeng, additional, and Liu, Tian, additional

Published
2016
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38. Genetic counseling in diabetes mellitus: A practice resource of the National Society of Genetic Counselors.

Author

Maloney KA, Mizerik E, King RH, McGinnis EM, Perkowitz S, Diamonstein CJ, Schmanski AA, Saliganan S, Shipper AG, Udler MS, Guan Y, and Pollin TI

Subjects
Humans, Female, Counselors, Societies, Medical, United States, Pregnancy, Genetic Counseling methods, Diabetes Mellitus genetics, Diabetes Mellitus therapy
Abstract

Diabetes mellitus is a group of diseases characterized by hyperglycemia and its consequences, affecting over 34 million individuals in the United States and 422 million worldwide. While most diabetes is polygenic and is classified as type 1 (T1D), type 2 (T2D), or gestational diabetes (GDM), at least 0.4% of all diabetes is monogenic in nature. Correct diagnosis of monogenic diabetes has important implications for glycemic management and genetic counseling. We provide this Practice Resource to familiarize the genetic counseling community with (1) the existence of monogenic diabetes, (2) how it differs from more common polygenic/complex diabetes types, (3) the advantage of a correct diagnosis, and (4) guidance for identifying, counseling, and testing patients and families with suspected monogenic diabetes. This document is intended for genetic counselors and other healthcare professionals providing clinical services in any setting, with the goal of maximizing the likelihood of a correct diagnosis of monogenic diabetes and access to related care., (© 2023 National Society of Genetic Counselors.)

Published
2024
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39. Mass spectrometry imaging technology in metabolomics: A systematic review.

Author

Gao SQ, Zhao JH, Guan Y, Tang YS, Li Y, and Liu LY

Subjects
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization methods, Metabolomics
Abstract

Mass spectrometry imaging (MSI) is a powerful label-free analysis technique that can provide simultaneous spatial distribution of multiple compounds in a single experiment. By combining the sensitive and rapid screening of high-throughput MS with spatial chemical information, metabolite analysis and morphological characteristics are presented in a single image. MSI can be used for qualitative and quantitative analysis of metabolic profiles and it can provide visual analysis of spatial distribution information of complex biological and microbial systems. Matrix-assisted laser desorption ionization, laser ablation electrospray ionization and desorption electrospray ionization are commonly used in MSI. Here, we summarize and compare these three technologies, as well as the applications and prospects of MSI in metabolomics., (© 2022 John Wiley & Sons Ltd.)

Published
2023
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40. Accelerating T 2 mapping of the brain by integrating deep learning priors with low-rank and sparse modeling.

Author

Meng Z, Guo R, Li Y, Guan Y, Wang T, Zhao Y, Sutton B, Li Y, and Liang ZP

Subjects
Algorithms, Brain diagnostic imaging, Healthy Volunteers, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Deep Learning
Abstract

Purpose: To accelerate T 2 mapping with highly sparse sampling by integrating deep learning image priors with low-rank and sparse modeling., Methods: The proposed method achieves high-speed T 2 mapping by highly sparsely sampling (k, TE)-space. Image reconstruction from the undersampled data was done by exploiting the low-rank structure and sparsity in the T 2 -weighted image sequence and image priors learned from training data. The image priors for a single TE were generated from the public Human Connectome Project data using a tissue-based deep learning method; the image priors were then transferred to other TEs using a generalized series-based method. With these image priors, the proposed reconstruction method used a low-rank model and a sparse model to capture subject-dependent novel features., Results: The proposed method was evaluated using experimental data obtained from both healthy subjects and tumor patients using a turbo spin-echo sequence. High-quality T 2 maps at the resolution of 0.9 × 0.9 × 3.0 mm 3 were obtained successfully from highly undersampled data with an acceleration factor of 8. Compared with the existing compressed sensing-based methods, the proposed method produced significantly reduced reconstruction errors. Compared with the deep learning-based methods, the proposed method recovered novel features better., Conclusion: This paper demonstrates the feasibility of learning T 2 -weighted image priors for multiple TEs using tissue-based deep learning and generalized series-based learning. A new method was proposed to effectively integrate these image priors with low-rank and sparse modeling to reconstruct high-quality images from highly undersampled data. The proposed method will supplement other acquisition-based methods to achieve high-speed T 2 mapping., (© 2020 International Society for Magnetic Resonance in Medicine.)

Published
2021
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41. Evaluation of the Informational Content, Readability and Comprehensibility of Online Health Information on Monogenic Diabetes.

Author

Guan Y, Maloney KA, Roter DL, and Pollin TI

Subjects
Comprehension, Humans, Diabetes Mellitus, Health Literacy standards, Internet standards, Patient Education as Topic standards
Abstract

The purpose of this study was to assess the informational content, readability, suitability and comprehensibility of websites offering educational information about monogenic diabetes available to patients. The top 20 results from 15 queries in four search engines were screened. Content analysis was performed by two independent coders. Readability was determined using Flesch-Kincaid grade level (FKGL) and Simplified Measure of Goobledygook (SMOG). The Comprehensibility Assessment of Materials (SAM + CAM) scale was utilized to evaluate website suitability and comprehensibility. Only 2% (N = 29) of 1200 screened websites met inclusion criteria. Content analysis showed that 16 websites presented information on at least the most common forms of MODY (1, 2 and 3), four addressed the utility of genetic counseling, and none included support resources for patients. All websites exceeded the consensus readability level (6th grade) as assessed by FKGL (10.1 grade) and SMOG (12.8 ± 1.5 grades). Although the majority (N = 20) of websites had an overall "adequate" to "superior" quality score (SAM + CAM score > = 40%), more than one-third scored "not suitable" in categories of content, literacy demand, graphics, and learning motivation. The online educational resources for monogenic diabetes have a high readability level and require improvement in ease of use and comprehensibility for patients with diabetes.

Published
2018
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