Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules.
Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules.
复制标题
通过呼出分子的模式分析对1404名受试者的17种疾病的诊断和分类。
DOI：
10.1021/acsnano.6b04930
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发表时间：
2017-01-24
期刊：
影响因子：
17.1
通讯作者：
Haick H
中科院分区：
文献类型：
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作者：
Nakhleh MK;Amal H;Jeries R;Broza YY;Aboud M;Gharra A;Ivgi H;Khatib S;Badarneh S;Har-Shai L;Glass-Marmor L;Lejbkowicz I;Miller A;Badarny S;Winer R;Finberg J;Cohen-Kaminsky S;Perros F;Montani D;Girerd B;Garcia G;Simonneau G;Nakhoul F;Baram S;Salim R;Hakim M;Gruber M;Ronen O;Marshak T;Doweck I;Nativ O;Bahouth Z;Shi DY;Zhang W;Hua QL;Pan YY;Tao L;Liu H;Karban A;Koifman E;Rainis T;Skapars R;Sivins A;Ancans G;Liepniece-Karele I;Kikuste I;Lasina I;Tolmanis I;Johnson D;Millstone SZ;Fulton J;Wells JW;Wilf LH;Humbert M;Leja M;Peled N;Haick H
我们报告了一种基于分子修饰的金纳米颗粒和单壁碳纳米管随机网络的人工智能纳米阵列，用于对呼出气体中的一些疾病进行非侵入性诊断和分类。这种人工智能纳米阵列的性能在临床上对从1404名受试者收集的呼吸样本进行了评估，这些受试者患有研究中包括的17种不同疾病之一或没有任何疾病的证据（健康对照）。盲法实验表明，人工智能纳米阵列可以达到86%的准确率，允许检测和区分不同的疾病状况。对人工智能纳米阵列的分析还表明，每种疾病都有自己独特的呼吸纹，一种疾病的存在不会筛选出其他疾病。聚类分析表明，从同一类别的疾病的合理的分类能力。混杂的临床和环境因素对纳米阵列性能的影响没有显著改变所获得的结果。纳米阵列的诊断和分类能力也通过独立的分析技术进行了验证，即，气相色谱法与质谱法联用。这项分析发现，13种呼出的化学物质，称为挥发性有机化合物，与某些疾病有关，这种挥发性有机化合物组合的组成因疾病而异。总的来说，这些发现可能有助于现代成功健康干预的最重要标准之一，即易于使用，廉价（负担得起）和小型化的工具，也可用于个性化筛查，诊断和随访许多疾病，这显然可以通过进一步发展来扩展。
We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development.