健康信念对乳房x光筛查行为采用的影响:路径分析模型

M. Gordin, H. Philip
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摘要

为了研究肿瘤进展过程中的t细胞库,我们对10只雌性小鼠和5只对照小鼠进行了跟踪研究,这些小鼠是一种表达未激活的大鼠新(Erbb2)癌基因的转基因小鼠株。这些小鼠在5-8个月内自发发展为乳腺肿瘤。为了量化外周T细胞库,我们在9个月的时间里每个月从血液中提取T细胞。对这些样本的细胞进行分类,然后通过cDNA TCR С和С单分子条形码文库制备方案进行处理,然后进行NGS测序。我们能够利用它们的免疫库来对肿瘤和非肿瘤小鼠进行分类。使用特征选择算法,我们能够使用T细胞库的一小部分(3到6个克隆)提供优越的分类。因此,机器学习和特征选择使我们能够将在曲目测序过程中获得的数十万个TCR α和β序列减少到一组六个克隆,从而我们可以识别血液样本的来源是肿瘤还是对照。我们可以使用相同的小T细胞克隆亚群,进一步对年龄较大的转基因小鼠(大于5个月)和年龄较大的对照小鼠进行分层。后一种分类仅用3个T细胞克隆获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Health Beliefs on the Behavioral Adoption of Mammography Screening: A Path Analytic Model
To study the T-cell repertoire during tumor progression, we followed 10 female mice of a transgenic mouse strain that expresses the un-activated rat neu (Erbb2) oncogene, along with 5 control mice. These mice develop mammary tumors spontaneously over 5-8 months. To quantify the peripheral T cell repertoire, we extracted T cells from blood, every month, over the period of 9 months. Cells from these samples were sorted and later processed through a cDNA TCR С and С library preparation protocol using single-molecule barcoding and then NGS sequenced. We were able to use the repertoire to classify tumor and non-tumor mice, using their immunological repertoire. Using feature selection algorithms, we were able to provide superior classification using a small subset (3 to 6 clones) of the T cell repertoire. Thus, machine learning and feature selection allowed us to reduce the hundreds of thousands of TCR alpha and beta sequences obtained during repertoire sequencing, to a set of six clones, with which we can identify the source of a blood sample as tumor or control. We can further stratify older transgenic mice (older than 5 months) and those of older control mice, using the same small T cell clones’ subset. This latter classification has been obtained with as little as three T cell clones.
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