Porous Materials for Early Diagnosis of Neurodegenerative Diseases

材料科学 纳米技术 医学
作者
Payam Arghavani,Hossein Daneshgar,Soheil Sojdeh,Mohammad Edrisi,Ali Akbar Moosavi‐Movahedi,Navid Rabiee
出处
期刊:Advanced Healthcare Materials [Wiley]
被引量:2
标识
DOI:10.1002/adhm.202404685
摘要

Abstract Neurodegenerative diseases, particularly Alzheimer's disease and Parkinson's disease, present formidable challenges in modern medicine due to their complex pathologies and the absence of curative treatments. Despite advances in symptomatic management, early diagnosis remains essential for mitigating disease progression and improving patient outcomes. Traditional diagnostic methods, such as MRI, PET, and cerebrospinal fluid biomarker analysis, are often inadequate for the early detection of these diseases. Emerging porous materials, including metal–organic frameworks (MOFs), covalent–organic frameworks (COFs), MXene, zeolites, and porous silicon, offer promising new approaches for the early diagnosis of neurodegenerative diseases. These materials, characterized by highly tunable physicochemical properties, have the potential to capture and concentrate disease‐specific biomarkers such as amyloid‐beta (Aβ), tau protein, and alpha‐synuclein (α‐Syn). The integration of these materials into advanced biosensors for real‐time detection holds the promise of revolutionizing neurodiagnostic, enabling non‐invasive, highly sensitive, and specific detection platforms. Furthermore, the incorporation of artificial intelligence (AI) and machine learning (ML) techniques into the analysis of sensor data enhances diagnostic accuracy and allows for more efficient interpretation of complex biomarker profiles. AI and ML can optimize feature selection, improve pattern recognition, and facilitate the prediction of disease progression, making them indispensable tools for personalized medicine. This review explores the potential of porous materials in neurodegenerative disease diagnostics, emphasizing their design, functionality, and the synergistic role of AI and ML in advancing clinical applications.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
华仔应助Mr_X采纳,获得10
刚刚
刚刚
落林樾发布了新的文献求助10
1秒前
归尘完成签到,获得积分10
1秒前
超帅的元柏完成签到,获得积分10
2秒前
宛海发布了新的文献求助10
2秒前
lalala发布了新的文献求助10
2秒前
是我呀小夏完成签到 ,获得积分10
3秒前
3秒前
汪汪队发布了新的文献求助10
3秒前
3秒前
3秒前
3秒前
3秒前
zyt完成签到,获得积分10
4秒前
彭于彦祖应助活泼的南风采纳,获得30
4秒前
4秒前
共享精神应助瘦瘦的鬼神采纳,获得30
5秒前
5秒前
斑马完成签到,获得积分10
5秒前
悦耳玲完成签到 ,获得积分10
6秒前
常常完成签到,获得积分10
6秒前
雪山飞龙发布了新的文献求助10
6秒前
6秒前
ANGHUI发布了新的文献求助10
6秒前
李健的小迷弟应助Dasph7采纳,获得10
7秒前
7秒前
7秒前
7秒前
汉堡包应助Mayday采纳,获得10
7秒前
ymm发布了新的文献求助10
7秒前
7秒前
lalalal完成签到,获得积分10
7秒前
惠归尘发布了新的文献求助10
7秒前
8秒前
8秒前
8秒前
8秒前
9秒前
边夫人发布了新的文献求助10
10秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969322
求助须知:如何正确求助?哪些是违规求助? 3514152
关于积分的说明 11172188
捐赠科研通 3249407
什么是DOI,文献DOI怎么找? 1794832
邀请新用户注册赠送积分活动 875437
科研通“疑难数据库(出版商)”最低求助积分说明 804781