Browsing by Author "Cheng, Hao"

Now showing 1 - 4 of 4
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    Clinical Predictors of Functional Cure in Children 1–6 Years-old with Chronic Hepatitis B
    (Xia & He, 2022) Pan, Jing; Wang, Haiyan; Yao, Tiantian; Liao, Xuejiao; Cheng, Hao; Liangpunsakul, Suthat; Wang, Yan; Zhang, Min; Zhang, Zheng; Medicine, School of Medicine
    Background and aims: Hepatitis B surface antigen (HBsAg) clearance is significantly more common in children with chronic hepatitis B (CHB) than in adults; however, the possible influencing factors related to HBsAg loss have yet to be found. This study aimed to explore the efficacy of long-term interferon (IFN)α therapy in treating children with CHB and analyzed the factors influencing functional cure after treatment. Methods: A total of 236 children aged 1-6 years and diagnosed with CHB via liver biopsy were included in the study, all receiving IFNα treatment (IFNα-2b monotherapy, IFNα-2b followed by lamivudine [LAM] or IFNα-2b combined with LAM) and followed up for 144 weeks. A comprehensive analysis was conducted on clinical data, including biochemical items, serum markers of hepatitis B virus (HBV) and immunological indexes, and logistic regression analysis was used to screen the influencing factors related to HBsAg loss. Results: The cumulative loss rates of HBsAg were 79.5%, 62.1% and 42.1% at 144 weeks after the start of treatment in the 1-3 years-old group, 3-5 years-old group and 5-7 years-old group, respectively (p<0.05). IFNα-2b combined with LAM treatment displayed the highest HBsAg loss rates compared with monotherapy and sequential treatment (p=0.011). Younger baseline age and lower HBsAg levels were independent factors for the prediction of HBsAg loss (p<0.05). The baseline PreS1 and hepatitis B core antibody levels in the HBsAg loss group were lower than those in the HBsAg non-loss group. In addition, the PreS1 level was positively corelated with the level of HBsAg, HBV DNA and liver inflammation. Conclusions: Long-term treatment with IFNα was effective in achieving HBsAg loss in CHB children aged 1-6 years-old. Age less than 3 years-old and lower HBsAg levels are independent predictors of functional cure in children with CHB.
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    Multiple Metal-Nitrogen Bonds Synergistically Boosting the Activity and Durability of High-Entropy Alloy Electrocatalysts
    (American Chemical Society, 2024) Zhao, Xueru; Cheng, Hao; Chen, Xiaobo; Zhang, Qi; Li, Chenzhao; Xie, Jian; Marinkovic, Nebojsa; Ma, Lu; Zheng, Jin-Cheng; Sasaki, Kotaro; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    The development of Pt-based catalysts for use in fuel cells that meet performance targets of high activity, maximized stability, and low cost remains a huge challenge. Herein, we report a nitrogen (N)-doped high-entropy alloy (HEA) electrocatalyst that consists of a Pt-rich shell and a N-doped PtCoFeNiCu core on a carbon support (denoted as N-Pt/HEA/C). The N-Pt/HEA/C catalyst showed a high mass activity of 1.34 A mgPt-1 at 0.9 V for the oxygen reduction reaction (ORR) in rotating disk electrode (RDE) testing, which substantially outperformed commercial Pt/C and most of the other binary/ternary Pt-based catalysts. The N-Pt/HEA/C catalyst also demonstrated excellent stability in both RDE and membrane electrode assembly (MEA) testing. Using operando X-ray absorption spectroscopy (XAS) measurements and theoretical calculations, we revealed that the enhanced ORR activity of N-Pt/HEA/C originated from the optimized adsorption energy of intermediates, resulting in the tailored electronic structure formed upon N-doping. Furthermore, we showed that the multiple metal-nitrogen bonds formed synergistically improved the corrosion resistance of the 3d transition metals and enhanced the ORR durability.
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    scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data
    (Oxford University Press, 2022) Gu, Haocheng; Cheng, Hao; Ma, Anjun; Li, Yang; Wang, Juexin; Xu, Dong; Ma, Qin; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Motivation: Gene expression imputation has been an essential step of the single-cell RNA-Seq data analysis workflow. Among several deep-learning methods, the debut of scGNN gained substantial recognition in 2021 for its superior performance and the ability to produce a cell-cell graph. However, the implementation of scGNN was relatively time-consuming and its performance could still be optimized. Results: The implementation of scGNN 2.0 is significantly faster than scGNN thanks to a simplified close-loop architecture. For all eight datasets, cell clustering performance was increased by 85.02% on average in terms of adjusted rand index, and the imputation Median L1 Error was reduced by 67.94% on average. With the built-in visualizations, users can quickly assess the imputation and cell clustering results, compare against benchmarks and interpret the cell-cell interaction. The expanded input and output formats also pave the way for custom workflows that integrate scGNN 2.0 with other scRNA-Seq toolkits on both Python and R platforms. Availability and implementation: scGNN 2.0 is implemented in Python (as of version 3.8) with the source code available at https://github.com/OSU-BMBL/scGNN2.0.
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    Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine
    (American Academy of Sleep Medicine, 2023) Bandyopadhyay, Anuja; Bae, Charles; Cheng, Hao; Chiang, Ambrose; Deak, Maryann; Seixas, Azizi; Singh, Jaspal; Pediatrics, School of Medicine
    Since the publication of its 2020 position statement on artificial intelligence (AI) in sleep medicine by the American Academy of Sleep Medicine, there has been a tremendous expansion of AI-related software and hardware options for sleep clinicians. To help clinicians understand the current state of AI and sleep medicine, and to further enable these solutions to be adopted into clinical practice, a discussion panel was conducted on June 7, 2022, at the Associated Professional Sleep Societies Sleep Conference in Charlotte, North Carolina. The article is a summary of key discussion points from this session, including aspects of considerations for the clinician in evaluating AI-enabled solutions including but not limited to what steps might be taken both by the Food and Drug Administration and clinicians to protect patients, logistical issues, technical challenges, billing and compliance considerations, education and training considerations, and other unique challenges specific to AI-enabled solutions. Our summary of this session is meant to support clinicians in efforts to assist in the clinical care of patients with sleep disorders utilizing AI-enabled solutions.