Hybrid Volatolomics and Disease Detection

被引:215
|
作者
Broza, Yoav Y. [1 ,2 ]
Mochalski, Pawel [3 ,4 ,5 ]
Ruzsanyi, Vera [3 ,4 ,5 ]
Amann, Anton [3 ,4 ,5 ]
Haick, Hossam [1 ,2 ]
机构
[1] Technion Israel Inst Technol, Dept Chem Engn, IL-3200003 Haifa, Israel
[2] Technion Israel Inst Technol, Russell Berrie Nanotechnol Inst, IL-3200003 Haifa, Israel
[3] Univ Innsbruck, Breath Res Inst, A-6020 Innsbruck, Austria
[4] Univ Innsbruck, Univ Clin Anesthesia, A-6020 Innsbruck, Austria
[5] Med Univ Innsbruck, A-6020 Innsbruck, Austria
基金
比尔及梅琳达.盖茨基金会; 欧洲研究理事会; 奥地利科学基金会; 芬兰科学院;
关键词
breath; diagnosis; skin; volatile organic compounds; volatolomics; VOLATILE ORGANIC-COMPOUNDS; AIR PARTITION-COEFFICIENTS; LUNG-CANCER CELLS; HUMAN-BODY ODOR; CHROMATOGRAPHY-MASS-SPECTROMETRY; SOLID-PHASE MICROEXTRACTION; BRONCHIAL EPITHELIAL-CELLS; EXHALED BREATH; IN-VITRO; GAS-CHROMATOGRAPHY;
D O I
10.1002/anie.201500153
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
This Review presents a concise, but not exhaustive, didactic overview of some of the main concepts and approaches related to "volatolomics"- an emerging frontier for fast, risk-free, and potentially inexpensive diagnostics. It attempts to review the source and characteristics of volatolomics through the so-called volatile organic compounds (VOCs) emanating from cells and their microenvironment. It also reviews the existence of VOCs in several bodily fluids, including the cellular environment, blood, breath, skin, feces, urine, and saliva. Finally, the usefulness of volatolomics for diagnosis from a single bodily fluid, as well as ways to improve these diagnostic aspects by "hybrid" approaches that combine VOC profiles collected from two or more bodily fluids, will be discussed. The perspectives of this approach in developing the field of diagnostics to a new level are highlighted.
引用
收藏
页码:11036 / 11048
页数:13
相关论文
共 50 条
  • [41] An improved hybrid model for cardiovascular disease detection using machine learning in IoT
    Naseer, Arslan
    Khan, Muhammad Muheet
    Arif, Fahim
    Iqbal, Waseem
    Ahmad, Awais
    Ahmad, Ijaz
    EXPERT SYSTEMS, 2025, 42 (01)
  • [42] A hybrid network-based method for the detection of disease-related genes
    Cui, Ying
    Cai, Meng
    Dai, Yang
    Stanley, H. Eugene
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2018, 492 : 389 - 394
  • [43] AttentionLUNet: A Hybrid Model for Parkinson's Disease Detection Using MRI Brain
    Rani Palakayala, Anitha
    Kuppusamy, P.
    IEEE ACCESS, 2024, 12 : 91752 - 91769
  • [44] VGG16-3PGA: A Hybrid Approach for Plant Disease Detection
    Sharma, Rahul
    Singh, Amar
    JOURNAL OF CROP HEALTH, 2024, 76 (06) : 1541 - 1552
  • [45] SUNet: Coffee Leaf Disease Detection Using Hybrid Deep Learning Model
    Thakur, Deepak
    Gera, Tanya
    Aggarwal, Ambika
    Verma, Madhushi
    Kaur, Manjit
    Singh, Dilbag
    Amoon, Mohammed
    IEEE ACCESS, 2024, 12 : 149173 - 149191
  • [46] A Novel Hybrid Deep Learning Model for Crop Disease Detection Using BEGAN
    Orchi, Houda
    Sadik, Mohamed
    Khaldoun, Mohammed
    UBIQUITOUS NETWORKING, UNET 2022, 2023, 13853 : 267 - 283
  • [47] A Hybrid Approach for Plant Disease Detection Using E-GAN and CapsNet
    Vasudevan N.
    Karthick T.
    Computer Systems Science and Engineering, 2023, 46 (01): : 337 - 356
  • [48] Hybrid Ensemble Learning With CNN and RNN for Multimodal Cotton Plant Disease Detection
    Shrotriya, Anita
    Sharma, Akhilesh Kumar
    Bairwa, Amit Kumar
    Manoj, R.
    IEEE ACCESS, 2024, 12 : 198028 - 198045
  • [49] A hybrid approach for the detection of small airways disease from computed tomographic images
    Yang, GZ
    Hansell, DM
    INFORMATION PROCESSING IN MEDICAL IMAGING, 1997, 1230 : 447 - 452
  • [50] Detection of Leaf Disease Using Hybrid Feature Extraction Techniques and CNN Classifier
    Kanabur, Vidyashree
    Harakannanavar, Sunil S.
    Purnikmath, Veena, I
    Hullole, Pramod
    Torse, Dattaprasad
    COMPUTATIONAL VISION AND BIO-INSPIRED COMPUTING, 2020, 1108 : 1213 - 1220