CMS

CMS computes composite severity measures from behavioral and biochemical data to quantify and compare distress levels in animal models of epilepsy.

Key Features:

• R package implementation: Implemented as an R package for computational analysis of experimental data.
• Input data: Operates on tabular data sets derived from behavioral and biochemical analyses of animal models.
• Parameter selection via PCA: Uses Principal Component Analysis (PCA) to identify the most informative parameters from the input data.
• Composite Measure Scheme (CMS) development: Integrates top-ranking parameters such as burrowing, open field locomotion, social interaction, and saccharin preference into a composite measure scheme.
• Cluster analysis for severity assessment: Applies cluster analysis to CMS data to allocate severity levels to individual animals.
• Severity comparison across models: Enables direct comparisons and prioritization of epilepsy models including electrical, chemical post-status epilepticus, and kindling models based on severity assessments.
• Validation of refinement measures: Facilitates assessment of novel refinement approaches for reducing distress in seizure-related experiments.

Scientific Applications:

• Ethical evaluation: Provides structured quantitative input for harm-benefit analyses in ethical review of animal experiments.
• Animal welfare-based model prioritization: Supports objective grading and prioritization of animal models according to assessed burden.
• Validation of refinement measures: Enables validation of interventions aimed at reducing animal distress in epilepsy research.

Methodology:

Apply PCA to behavioral and biochemical tabular data to select top-ranking parameters, integrate those parameters (e.g., burrowing, open field locomotion, social interaction, saccharin preference) into a Composite Measure Scheme, and perform cluster analysis on the CMS to allocate severity levels and enable model comparisons.