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Improving the predictive value of interventional animal models data

机译:提高介入动物模型数据的预测价值

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摘要

For many chronic diseases, translational success using the animal model paradigm has reached an impasse. Using Alzheimer's disease as an example, this review employs a networks-based method to assess repeatability of outcomes across species, by intervention and mechanism. Over 75% of animal studies reported an improved outcome. Strain background was a significant potential confounder. Five percent of interventions had been tested across animals and humans, or examined across three or more animal models. Positive outcomes across species emerged for donepezil, memantine and exercise. Repeatable positive outcomes in animals were identified for the amyloid hypothesis and three additional mechanisms. This approach supports in silico reduction of positive outcomes bias in animal studies.
机译:对于许多慢性疾病,使用动物模型范例的翻译成功已陷入僵局。以阿尔茨海默氏病为例,本综述采用基于网络的方法,通过干预和机制评估物种间结果的可重复性。超过75%的动物研究报告结果有所改善。应变背景是一个重要的潜在混杂因素。 5%的干预措施已在动物和人类之间进行了测试,或已在三种或多种动物模型中进行了检验。多奈哌齐,美金刚和运动对整个物种都产生了积极的结果。对于淀粉样蛋白假说和三种其他机制,确定了动物中可重复的阳性结果。该方法支持计算机减少动物研究中阳性结果偏倚。

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