狗癌症评估,使用cerumen作为挥发性生物标志物前景的来源。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
João Marcos G. Barbosa, Engy Shokry, Lurian Caetano David, Naiara Z. Pereira, Adriana R. da Silva, Vilma F. de Oliveira, Maria Clorinda S. Fioravanti, Paulo H. Jorge da Cunha, Anselmo E. de Oliveira and Nelson Roberto Antoniosi Filho
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引用次数: 0

摘要

癌症是人类和狗最致命的疾病之一。尽管如此,大多数肿瘤类型在犬科动物中传播得更快,早期癌症检测方法对于提高动物存活率是必要的。在这里,耳垢被测试为狗癌症评估的潜在生物标志物的来源。从携带肿瘤和临床健康的狗身上采集狗的耳垢样本,然后进行顶空/气相色谱-质谱(HS/GMS)分析和多变量统计工作流程。基于进化的多变量算法从128种挥发性代谢物中选择18种作为狗的潜在癌症生物标志物。候选生物标志物在测试数据集中以高准确度显示了荷瘤狗和无癌狗之间的完全区分模式:准确度为95.0%(75.1-99.9),敏感性和特异性分别为100.0%和92.9%。总之,这项工作为狗的癌症诊断提供了一个新的视角,它是无痛和无创的,有助于样本采集和定期应用于兽医常规。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Cancer evaluation in dogs using cerumen as a source for volatile biomarker prospection†

Cancer evaluation in dogs using cerumen as a source for volatile biomarker prospection†

Cancer evaluation in dogs using cerumen as a source for volatile biomarker prospection†

Cancer is one of the deadliest diseases in humans and dogs. Nevertheless, most tumor types spread faster in canines, and early cancer detection methods are necessary to enhance animal survival. Here, cerumen (earwax) was tested as a source of potential biomarkers for cancer evaluation in dogs. Earwax samples from dogs were collected from tumor-bearing and clinically healthy dogs, followed by Headspace/Gas Chromatography-Mass Spectrometry (HS/GC-MS) analyses and multivariate statistical workflow. An evolutionary-based multivariate algorithm selected 18 out of 128 volatile metabolites as a potential cancer biomarker panel in dogs. The candidate biomarkers showed a full discrimination pattern between tumor-bearing dogs and cancer-free canines with high accuracy in the test dataset: an accuracy of 95.0% (75.1–99.9), and sensitivity and specificity of 100.0% and 92.9%, respectively. In summary, this work raises a new perspective on cancer diagnosis in dogs, being carried out painlessly and non-invasive, facilitating sample collection and periodic application in a veterinary routine.

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CiteScore
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