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A dysbiosis index to assess microbial changes in fecal samples of dogs with chronic inflammatory enteropathy

机译:一种缺乏症指数,以评估慢性炎症肠病犬粪便样本的微生物变化

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Recent studies have identified various bacterial groups that are altered in dogs with chronic inflammatory enteropathies (CE) compared to healthy dogs. The study aim was to use quantitative PCR (qPCR) assays to confirm these findings in a larger number of dogs, and to build a mathematical algorithm to report these microbiota changes as a dysbiosis index (DI). Fecal DNA from 95 healthy dogs and 106 dogs with histologically confirmed CE was analyzed. Samples were grouped into a training set and a validation set. Various mathematical models and combination of qPCR assays were evaluated to find a model with highest discriminatory power. The final qPCR panel consisted of eight bacterial groups: total bacteria, Faecalibacterium, Turicibacter, Escherichia coli, Streptococcus, Blautia, Fusobacterium and Clostridium hiranonis. The qPCR-based DI was built based on the nearest centroid classifier, and reports the degree of dysbiosis in a single numerical value that measures the closeness in the I-2-norm of the test sample to the mean prototype of each class. A negative DI indicates normobiosis, whereas a positive DI indicates dysbiosis. For a threshold of 0, the DI based on the combined dataset achieved 74% sensitivity and 95% specificity to separate healthy and CE dogs.
机译:最近的研究已经确定了与健康犬相比,慢性炎症肠病(CE)在患有慢性炎症肠病(CE)中的各种细菌组。该研究目的是使用定量PCR(QPCR)测定来确认这些发现在较大数量的狗中,并建立数学算法,以报告这些微生物群作为缺陷指数(DI)的变化。分析了来自95只健康狗的粪便DNA和组织学证实CE的106只狗。将样品分组成训练集和验证集。评估各种数学模型和QPCR测定的组合,以找到具有最高辨别力的模型。最终的QPCR面板包括八个细菌组:总细菌,粪便杆菌,Turicibacter,大肠杆菌,链球菌,Blautia,Fusobacterium和Clostridium Hiranonis。基于QPCR的DI基于最近的质心分类器构建,并在单一数值中报告困难程度的程度,该数值测量测试样本的I-2常态的接近度为每个阶级的平均原型。阴性DI表示源极化,而阳性DI表明脱敏。对于0的阈值,基于组合数据集的DI达到74%的灵敏度和95%的特异性,以分离健康和CE狗。

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