category
Nature BME
date
Mar 3, 2026
slug
status
Published
summary
创新性地整合高通量低输入定量瓜氨酸化组分析与深度学习技术，揭示瓜氨酸化组特征与类风湿关节炎严重程度的关联，并发现可作为治疗分层标志物的瓜氨酸化肽段自身抗原。
tags
蛋白质组学
type
Post
📄 原文题目
Low-input deep learning platform for citrullinated peptide identification, autoantigen discovery and rheumatoid arthritis treatment stratification
🔗 原文链接
💡 AI 核心解读
创新性地整合高通量低输入定量瓜氨酸化组分析与深度学习技术，揭示瓜氨酸化组特征与类风湿关节炎严重程度的关联，并发现可作为治疗分层标志物的瓜氨酸化肽段自身抗原。
📝 英文原版摘要
<p>Nature Biomedical Engineering, Published online: 03 March 2026; <a href="https://www.nature.com/articles/s41551-026-01628-4">doi:10.1038/s41551-026-01628-4</a></p>High-throughput and low-input quantitative citrullinome analysis integrated with deep learning reveals association between citrullinome landscape and rheumatoid arthritis severity, as well as rheumatoid arthritis-sera reactivity of identified citrullinated peptides.
- 作者:NotionNext
- 链接:https://tangly1024.com/article/31948bd6-1f96-811f-91c5-e00eda133326
- 声明:本文采用 CC BY-NC-SA 4.0 许可协议，转载请注明出处。
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