A model-specific simplification of the Mouse Grimace Scale based on the pain response of intraperitoneal CCl4 injections.

Despite its long establishment and applicability in mice pain detection, the Mouse Grimace Scale still seems to be underused in acute pain detection during chronic experiments. However, broadening its applicability can identify possible refinement approaches such as cumulative severity and habituation to painful stimuli. Therefore, this study focuses on two main aspects: First, five composite MGS criteria were evaluated with two independent methods (the MoBPs algorithm and a penalized least squares regression) and ranked for their relative importance. The most important variable was used in a second analysis to specifically evaluate the context of pain after an i.p. injection (intervention) in two treatment groups (CCl4 and oil (control)) at fixed times throughout four weeks in 24 male C57BL/6 N mice. One hour before and after each intervention, video recordings were taken, and the MGS assessment was performed. In this study, the results indicate orbital tightening as the most important criterion. In this experimental setup, a highly significant difference after treatment between week 0 and 1 was found in the CCl4 group, resulting in a medium-sized effect (W = 62.5, p value < 0.0001, rCCl4 = 0.64). The oil group showed no significant difference (week 0 vs 1, W = 291.5, p value = 0.7875, rcontrol = 0.04). Therefore, the study showed that the pain caused by i.p. injections was only dependent on the applied substance, and no significant cumulation or habituation occurred due to the intervention. Further, the results indicated that the MGS system can be simplified.

Best variable identification by means of data-mining and cooperative game theory.

OBJECTIVE: To develop and evaluate methods to assess single and grouped variables impact on measuring intervention severities and support a search for most expressive variables. METHODS: Datasets of cohort studies are analyzed automatically based on algorithms. For this, a metric is developed to compare measured variables in different cohorts in a data-mining process. Variables are measured in all possible combinations to detect possible synergies of certain variable constellations and allow for a ranking of the combinations' expressiveness. Such ranking serves as a basis for a wide range of algorithmic data analysis. In an exemplary application, every group member's impact on the total result is determined based on the principle of the cooperative game theory besides to the total expressiveness of the variable groups. RESULTS: For different types of interventions, the method is applied to experimental data containing multiple recorded medical lab values. The expressiveness of variable combinations to indicate severity is ranked by means of a metric. Within each combination, any variable's contribution to the total effect is determined and accumulated over whole datasets to yield local and global variable importance measures. The computed results have been successfully matched with clinical expectations to prove their plausibility. CONCLUSION: Algorithmic evaluation shows to be a promising approach in automatized quantification of variable expressiveness. It can assess descriptive power of measurements, help to improve future study designs and expose worthwhile research issues.

Design of a joint research data platform: A use case for severity assessment.

Severity assessment in animal models is a data-driven process. We therefore present a use case for building a repository for interlaboratory collaboration with the potential of uploading specific content, making group announcements and internal prepublication discussions. We clearly show that it is possible to offer such a structure with minimal effort and a basic understanding of web-based services, also taking into account the human factor in individual data collection. The FOR2591 Online Repository serves as a blueprint for other groups, so that one day not only will data sharing among consortium members be improved but the transition from the private to the persistent domain will also be easier.