Machine Learning-Assisted Sensor Array Based on Poly(amidoamine) (PAMAM) Dendrimers for Diagnosing Alzheimer?sDisease

被引：27
|
作者：
Xu, Lian [1 ,2 ]
Wang, Hao [1 ,2 ]
Xu, Yu [1 ,2 ]
Cui, Wenyu [1 ,2 ]
Ni, Weiwei [1 ,2 ]
Chen, Mingqi [1 ,2 ]
Huang, Hui [3 ]
Stewart, Callum [3 ]
Li, Linxian [3 ]
Li, Fei [1 ,2 ]
Han, Jinsong [1 ,2 ]
机构：
[1] China Pharmaceut Univ, State Key Lab Nat Med, Coll Engn, Nanjing 211109, Peoples R China
[2] China Pharmaceut Univ, Natl R&D Ctr Chinese Herbal Med Proc, Coll Engn, Dept Food Qual & Safety, Nanjing 211109, Peoples R China
[3] Karolinska Inst, Ming Wai Lau Ctr Reparat Med, S-17177 Stockholm, Sweden
来源：
ACS SENSORS | 2022年 / 7卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Alzheimer?s disease; PAMAM dendrimers; sensor array; machine learning algorithm; linear discriminant analysis; amyloid-?protein; AMYLOID-BETA; PROTEIN; BACTERIA; IDENTIFICATION; FLUORESCENCE; A-BETA(40); PROBE; MASS;
D O I：
10.1021/acssensors.2c00132
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
Alzheimer's disease (AD) is the most prevalentneurodegenerative disorder, and the early diagnosis of AD remainschallenging. Here we have developed afluorescent sensor arraycomposed of three modified polyamidoamine dendrimers. Proteins ofvarious properties were differentiated via this array with 100%accuracy, proving the rationality of the array's design. The mechanismof thefluorescence response was discussed. Furthermore, the robustthree-element array enables parallel detection of multiple A beta 40/A beta 42aggregates (0.5 mu M) in diverse interferents, serum media, andcerebrospinalfluid (CSF) with high accuracy, through machinelearning algorithms, demonstrating the tremendous potential of thesensor array in Alzheimer's disease diagnosis.
引用
收藏
页码：1315 / 1322
页数：8
相关论文
共 50 条
  • [41] Machine Learning-Assisted Liquid Crystal Optical Sensor Array Using Cysteine-Functionalized Silver Nanotriangles for Pathogen Detection in Food and Water
    Mousavizadegan, Maryam
    Hosseini, Morteza
    Mohammadimasoudi, Mohammad
    Guan, Yiran
    Xu, Guobao
    ACS APPLIED MATERIALS & INTERFACES, 2024, : 70419 - 70428
  • [42] Machine learning-assisted ZnO-based sensor for multi-species recognition of volatile aroma components in tea plant
    Xu, Haiyan
    Jing, Tingting
    Cheng, Youde
    Zheng, Mingjia
    Li, Yuqing
    Gu, Lichuan
    Rao, Yuan
    Song, Chuankui
    Jing, Hua
    Li, Ke
    SENSORS AND ACTUATORS B-CHEMICAL, 2025, 430
  • [43] Synthesis, optical properties, and cell imaging performance of perylene-3,4,9,10-tetracarboxylic diimide (PTCDI)-based poly (amidoamine) (PAMAM) dendrimers
    Golshan, Marzieh
    Salami-Kalajahi, Mehdi
    Roghani-Mamaqani, Hossein
    EUROPEAN POLYMER JOURNAL, 2022, 170
  • [44] Machine learning-assisted wearable triboelectric-electromagnetic vibration sensor for monitoring human rehabilitation training
    Li, Shun
    Qian, Jingui
    Liu, Jiaming
    Xue, Yuhang
    Zhang, Junjie
    Liu, Yansong
    Hu, Xuefeng
    Jing, Xingjian
    Zhang, Wei
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 201
  • [45] Machine learning-assisted SERS sensor for fast and ultrasensitive analysis of multiplex hazardous dyes in natural products
    Lin, Chengqi
    Zheng, Cheng
    Fan, Bo
    Wang, Chenchen
    Zhao, Xiaoping
    Wang, Yi
    JOURNAL OF HAZARDOUS MATERIALS, 2025, 482
  • [46] Machine learning-assisted structure annotation of natural products based on MS and NMR data
    Hu, Guilin
    Qiu, Minghua
    NATURAL PRODUCT REPORTS, 2023, 40 (11) : 1735 - 1753
  • [47] Machine learning-assisted self-powered intelligent sensing systems based on triboelectricity
    Tian, Zhiyu
    Li, Jun
    Liu, Liqiang
    Wu, Han
    Hu, Xiaowei
    Xie, Mingjun
    Zhu, Yirui
    Chen, Xucong
    Wei, Ou-Yang
    NANO ENERGY, 2023, 113
  • [48] Machine learning-assisted signature and heuristic-based detection of malwares in Android devices
    Rehman, Zahoor-Ur
    Khan, Sidra Nasim
    Muhammad, Khan
    Leeb, Jong Weon
    Lv, Zhihan
    Baik, Sung Wook
    Shah, Peer Azmat
    Awan, Khalid
    Mehmood, Irfan
    COMPUTERS & ELECTRICAL ENGINEERING, 2018, 69 : 828 - 841
  • [49] Machine learning-assisted selection of adsorption-based carbon dioxide capture materials
    Al-Sakkari, Eslam G.
    Ragab, Ahmed
    So, Terry M. Y.
    Shokrollahi, Marzieh
    Dagdougui, Hanane
    Navarri, Philippe
    Elkamel, Ali
    Amazouz, Mouloud
    JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING, 2023, 11 (05):
  • [50] Machine learning-assisted triboelectric nanogenerator-based self-powered sensors
    Zhang, Renyun
    CELL REPORTS PHYSICAL SCIENCE, 2024, 5 (04):
12345