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.

在前两个资助期，我们发表了19篇论文，得出了以下四个结论：第一，遥测和非侵入性、易于评估的方法，如测量洞穴活动、筑巢行为、体重、粪便皮质酮代谢物和评估求救分数，可以成功地用于分析动物模型的求救。其次，ROC曲线分析非常适合于确定哪些方法适合区分特定应力水平。第三，二元逻辑回归、基于k-均值聚类或RELSA的多因素压力分析可以定义和比较动物模型的压力。第四，风险预测模型，如考克斯比例风险模型，适用于定义早期人类终点。然而，只有我们的方法、算法和结论是可靠的，才能实现以科学为基础的动物福利改善。此外，在比较不同动物模型的痛苦程度时，方法和算法的广泛普适性可能是有益的。因此，评估所用方法、算法和结论的稳健性和普适性是第三个资助期的一个主要重点。为此，我们将把我们目前正在收集的额外数据与我们在DFG研究小组中的合作伙伴提供的数据以及我们已经公布的数据进行比较。此外，我们打算通过比较使用不同止痛剂时的动物福利来改善动物的疼痛管理。在第三个资助期，我们将重点关注以下六个具体目标：第一，我们将验证我们用于测量小鼠应激的方法的重复性、健壮性和普适性。其次，我们将检查从RELSA评分得出的结论的稳健性，并有助于创建一个严重程度图，比较不同动物模型之间的痛苦。第三，我们希望验证早期人类终结点，并评估它们的普适性。第四，我们将与2591个小组的其他成员一起描述我们使用不同止痛药的经验。第五，我们将定义在分析痛苦时使用回程成像的利弊。第六，我们将测试新的分析方法和算法。因此，第三个资助期将使我们能够评估是否可以可靠地评估和比较MICE的负担。这一资助期还将使我们能够就止痛剂和人道终点的使用提出科学合理的建议。