Assessment of distress and refinement in animal models for gastrointestinal diseases

胃肠道疾病动物模型的痛苦和完善评估

• 批准号: 329654555
• 负责人: Professorin Dr. Brigitte Vollmar
• 金额: --
• 依托单位: Institut für Experimentelle Chirurgie mit Zentraler Versuchstierhaltung
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/329654555?language=en
• 关键词: Assessment; distress; refinement; animal; models

In the first two funding periods, we published 19 manuscripts and reached the following four conclusions: First, telemetry and non-invasive, easy-to-assess methods such as measuring burrowing activity, nesting behaviour, body weight, faecal corticosterone metabolites and evaluating a distress score can be successfully used for analysing distress of animal models. Second, ROC curve analysis is excellent for determining which methods are appropriate to discriminate between specific stress levels. Third, a multifactorial stress analysis by binary logistic regression, k-means-based clustering, or RELSA can define and compare the stress of animal models. Fourth, risk prediction models such as the Cox proportional hazards model are appropriate to define early human endpoints. However, a scientifically based improvement in animal welfare can only be accomplished, if our methods, algorithms and conclusions are robust. Moreover, a broad generalisability of methods and algorithms might be beneficial, when comparing distress levels of various animal models. Thus, it is one major focus during the third funding period to assess the robustness and generalisability of used methods, algorithms and conclusions. For this purpose, we will compare additional data that we are currently collecting with data from our cooperation partners within this DFG research group and with data that we have already published. In addition, we intend to improve the pain management of animals by comparing animal welfare when using different analgesics. In the third funding period, we will focus on the following six specific goals: First, we will validate the reproducibility, robustness, and generalisability of the methods we used to measure stress in mice. Second, we will check the robustness of conclusions derived from the RELSA score and contribute to the creation of a severity map that compares distress between distinct animal models. Third, we want to validate early human endpoints and assess their generalisability. Fourth, together with other FOR 2591 groups, we will describe our experience with different analgesics. Fifth, we will define the pros and cons of using homecage imaging when analysing distress. Sixth, we will test novel analysis methods and algorithms. Thus, the third funding period will enable us to assess whether the burden on mice can be reliably assessed and compared. This funding period will also enable us to make scientifically sound recommendations on the use of analgesics and humane endpoints.

在前两个资金期间，我们出版了19份手稿，并得出了以下四个结论：遥测和无创，易于评估的方法，例如测量挖洞活动，嵌套行为，体重，体重，粪便皮质酮代谢物，并可以成功地用于分析动物模型的遇险遇险分数。其次，ROC曲线分析非常适合确定哪种方法适合区分特定应力水平。第三，通过二进制逻辑回归，基于K-均值的聚类或RERSA进行的多因素应力分析可以定义和比较动物模型的应力。第四，诸如COX比例危害模型之类的风险预测模型适合定义早期人类终点。但是，只有在我们的方法，算法和结论稳健的情况下，才能实现基于科学的动物福利改善。此外，在比较各种动物模型的遇险水平时，方法和算法的广泛普遍性可能是有益的。因此，在第三个融资期内，这是评估使用方法，算法和结论的鲁棒性和普遍性的主要重点。为此，我们将比较目前正在收集的其他数据与该DFG研究小组中的合作合作伙伴的数据以及我们已经发布的数据。此外，我们打算通过比较不同的镇痛药时比较动物福利来改善动物的疼痛管理。在第三个资金期间，我们将重点关注以下六个特定目标：首先，我们将验证用于测量小鼠应力的方法的可重复性，鲁棒性和普遍性。其次，我们将检查从RELSA得分得出的结论的鲁棒性，并有助于创建严重性图，以比较不同动物模型之间的困扰。第三，我们想验证早期人类终点并评估其普遍性。第四，与其他2591组的其他组一起，我们将描述不同的镇痛药的经验。第五，我们将在分析困扰时定义使用归乡成像的利弊。第六，我们将测试新颖的分析方法和算法。因此，第三个资金期限将使我们能够评估小鼠的负担是否可以可靠地评估和比较。这个资金时期还将使我们能够就使用镇痛药和人道终点的使用科学明智的建议。