慢性肾脏病的分子生物学研究:诊断机会和治疗潜力。

IF 3.4 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Proteomics Pub Date : 2024-11-11 DOI:10.1002/pmic.202400151
Merita Rroji, Goce Spasovski
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引用次数: 0

摘要

通过提供全面的分子洞察力,组学技术大大推进了慢性肾脏病(CKD)的预测和治疗方法。本文综述了将生物标记物纳入慢性肾脏病临床实践的现状和未来前景,旨在通过有针对性的治疗干预改善患者预后。事实上,基因组、转录组、蛋白质组和代谢组数据的整合增强了我们对慢性肾脏病发病机制的了解,并为早期诊断和针对性治疗确定了新的生物标志物。先进的计算方法和人工智能(AI)进一步完善了多组学数据分析,从而为疾病进展和治疗反应建立了更准确的预测模型。这些发展彰显了通过精确的个体化治疗方案改善慢性肾脏病患者护理的潜力。
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Omics Studies in CKD: Diagnostic Opportunities and Therapeutic Potential.

Omics technologies have significantly advanced the prediction and therapeutic approaches for chronic kidney disease (CKD) by providing comprehensive molecular insights. This is a review of the current state and future prospects of integrating biomarkers into the clinical practice for CKD, aiming to improve patient outcomes by targeted therapeutic interventions. In fact, the integration of genomic, transcriptomic, proteomic, and metabolomic data has enhanced our understanding of CKD pathogenesis and identified novel biomarkers for an early diagnosis and targeted treatment. Advanced computational methods and artificial intelligence (AI) have further refined multi-omics data analysis, leading to more accurate prediction models for disease progression and therapeutic responses. These developments highlight the potential to improve CKD patient care with a precise and individualized treatment plan .

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来源期刊
Proteomics
Proteomics 生物-生化研究方法
CiteScore
6.30
自引率
5.90%
发文量
193
审稿时长
3 months
期刊介绍: PROTEOMICS is the premier international source for information on all aspects of applications and technologies, including software, in proteomics and other "omics". The journal includes but is not limited to proteomics, genomics, transcriptomics, metabolomics and lipidomics, and systems biology approaches. Papers describing novel applications of proteomics and integration of multi-omics data and approaches are especially welcome.
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