评论:研究和了解慢性肾病和糖尿病肾病的单细胞进展。

IF 4.7 2区 医学 Q1 PATHOLOGY American Journal of Pathology Pub Date : 2024-08-02 DOI:10.1016/j.ajpath.2024.07.007
Sagar Bhayana, Philip Andreas Schytz, Emma Tina Bisgaard Olesen, Keng Soh, Vivek Das
{"title":"评论:研究和了解慢性肾病和糖尿病肾病的单细胞进展。","authors":"Sagar Bhayana, Philip Andreas Schytz, Emma Tina Bisgaard Olesen, Keng Soh, Vivek Das","doi":"10.1016/j.ajpath.2024.07.007","DOIUrl":null,"url":null,"abstract":"<p><p>Chronic kidney disease (CKD) and its subset diabetic kidney disease are progressive conditions that affect >850 million people worldwide. Diabetes, hypertension, and glomerulonephritis are the most common causes of CKD, which is associated with significant patient morbidity and an increased risk of cardiovascular events, such as heart failure, ultimately leading to premature death. Despite newly approved drugs, increasing evidence shows that patients respond to treatment differently given the complexity of disease heterogeneity and complicated pathophysiology. This review article presents an integrative approach to understanding and addressing CKD through the lens of precision medicine and therapeutics. Leveraging advancements in single-cell omics technologies and artificial intelligence, we can explore the intricate cellular mechanisms underlying CKD and diabetic kidney disease pathogenesis. By dissecting the cellular heterogeneity and identifying rare cell populations using single-cell approaches, it will be possible to uncover novel therapeutic targets and biomarkers for personalized treatment strategies. Finally, we discuss the potential of artificial intelligence-driven analyses in predicting disease progression and treatment response, thereby paving the way for tailored interventions.</p>","PeriodicalId":7623,"journal":{"name":"American Journal of Pathology","volume":" ","pages":""},"PeriodicalIF":4.7000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Single-Cell Advances in Investigating and Understanding Chronic Kidney Disease and Diabetic Kidney Disease.\",\"authors\":\"Sagar Bhayana, Philip Andreas Schytz, Emma Tina Bisgaard Olesen, Keng Soh, Vivek Das\",\"doi\":\"10.1016/j.ajpath.2024.07.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Chronic kidney disease (CKD) and its subset diabetic kidney disease are progressive conditions that affect >850 million people worldwide. Diabetes, hypertension, and glomerulonephritis are the most common causes of CKD, which is associated with significant patient morbidity and an increased risk of cardiovascular events, such as heart failure, ultimately leading to premature death. Despite newly approved drugs, increasing evidence shows that patients respond to treatment differently given the complexity of disease heterogeneity and complicated pathophysiology. This review article presents an integrative approach to understanding and addressing CKD through the lens of precision medicine and therapeutics. Leveraging advancements in single-cell omics technologies and artificial intelligence, we can explore the intricate cellular mechanisms underlying CKD and diabetic kidney disease pathogenesis. By dissecting the cellular heterogeneity and identifying rare cell populations using single-cell approaches, it will be possible to uncover novel therapeutic targets and biomarkers for personalized treatment strategies. Finally, we discuss the potential of artificial intelligence-driven analyses in predicting disease progression and treatment response, thereby paving the way for tailored interventions.</p>\",\"PeriodicalId\":7623,\"journal\":{\"name\":\"American Journal of Pathology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American Journal of Pathology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.ajpath.2024.07.007\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PATHOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Pathology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.ajpath.2024.07.007","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PATHOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

慢性肾脏病(CKD)及其亚型糖尿病肾病(DKD)是影响全球 8.5 亿多人的渐进性疾病。糖尿病、高血压和肾小球肾炎是导致慢性肾脏病的最常见原因,而慢性肾脏病与患者的严重发病率和心血管事件(如心力衰竭)的风险增加有关,最终导致患者过早死亡。尽管新近批准了一些药物,但越来越多的证据表明,由于疾病的异质性和病理生理学的复杂性,患者对治疗的反应各不相同。本综述论文从精准医学和治疗学的角度,介绍了一种理解和解决慢性肾功能衰竭问题的综合方法。利用单细胞全息技术和人工智能(AI)的进步,我们可以探索 CKD 和 DKD 发病机制背后错综复杂的细胞机制。通过使用单细胞方法剖析细胞异质性和识别罕见细胞群,我们将有可能发现新的治疗靶点和生物标志物,从而制定个性化治疗策略。最后,我们讨论了人工智能驱动的分析在预测疾病进展和治疗反应方面的潜力,从而为量身定制的干预措施铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Single-Cell Advances in Investigating and Understanding Chronic Kidney Disease and Diabetic Kidney Disease.

Chronic kidney disease (CKD) and its subset diabetic kidney disease are progressive conditions that affect >850 million people worldwide. Diabetes, hypertension, and glomerulonephritis are the most common causes of CKD, which is associated with significant patient morbidity and an increased risk of cardiovascular events, such as heart failure, ultimately leading to premature death. Despite newly approved drugs, increasing evidence shows that patients respond to treatment differently given the complexity of disease heterogeneity and complicated pathophysiology. This review article presents an integrative approach to understanding and addressing CKD through the lens of precision medicine and therapeutics. Leveraging advancements in single-cell omics technologies and artificial intelligence, we can explore the intricate cellular mechanisms underlying CKD and diabetic kidney disease pathogenesis. By dissecting the cellular heterogeneity and identifying rare cell populations using single-cell approaches, it will be possible to uncover novel therapeutic targets and biomarkers for personalized treatment strategies. Finally, we discuss the potential of artificial intelligence-driven analyses in predicting disease progression and treatment response, thereby paving the way for tailored interventions.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
11.40
自引率
0.00%
发文量
178
审稿时长
30 days
期刊介绍: The American Journal of Pathology, official journal of the American Society for Investigative Pathology, published by Elsevier, Inc., seeks high-quality original research reports, reviews, and commentaries related to the molecular and cellular basis of disease. The editors will consider basic, translational, and clinical investigations that directly address mechanisms of pathogenesis or provide a foundation for future mechanistic inquiries. Examples of such foundational investigations include data mining, identification of biomarkers, molecular pathology, and discovery research. Foundational studies that incorporate deep learning and artificial intelligence are also welcome. High priority is given to studies of human disease and relevant experimental models using molecular, cellular, and organismal approaches.
期刊最新文献
A Core of Keratocan-Negative Cells Survives in Old Corneal Scars. CDR1as Deficiency Prevents Photoreceptor Degeneration by Regulating miR-7a-5p/α-syn/Parthanatos Pathway in Retinal Detachment. Evidence and Mechanism of Bile Acid-Mediated Gut-Brain Axis in Anxiety and Depression. Genetic Loss of HIF-Prolyl-Hydroxylase (PHD) 1, but not pharmacological Inhibition, mitigates hepatic fibrosis. REGULATION OF ADIPONECTIN AND RESISTIN IN LIVER TRANSPLANTATION PROTECTS GRAFTS FROM EXTENDED-CRITERIA DONORS.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1