2 min read

ID: 291714

Discovery Date: 14 May 2022, 22:29:39 UTC

Published Date: 2022-05-09 23:00:00

Source: MS & Rel. Disorders

Link: https://www.msard-journal.com/article/S2211-0348(22)00348-0/fulltext

Manual Selection: none

Machine Learning Gaussian Naive Bayes Model: true



Background: Experimental autoimmune encephalomyelitis (EAE) is the most widely used animal model for multiple sclerosis (MS). It is a rapid model, commonly induced in rodents. Even if EAE does not replicate all MS characteristics, it is appropriate to investigate the development of the disease, including the immune and neuroinflammatory aspects. Besides, EAE has also been shown to be a relevant model for pre-clinical studies, as several drugs effective in the model are beneficial for MS patients. However, despite its widespread use, there is no consensus on the clinical assessment of animals. Most researchers perform a daily evaluation and classify them on a 5-point scale, but many authors also use in-between scores or apply other systems. Besides, among the 5-point scale, different score definitions are used, and most of them do not recapitulate the signs or symptoms each animal can show. Thus, based on our experience with EAE, the aim of the present work was to develop a new scoring system. Methods: We designed the "I AM D EAE" tool that independently evaluates 9 different items – an innovative and detailed scoring system, yet simple for non-experts to use. The new scale was tested in EAE-induced mice at three experiments, and different evaluators assessed the animals blindly. Results: The "I AM D EAE" scoring system highly correlates to the commonly used 5-point scale and, importantly, it enables a more detailed evaluation. Conclusions: Considering its high reproducibility and inter-rater reliability, "I AM D EAE" is a useful tool for EAE monitoring.

Noun Phrases in Title

  • The innovative animal monitoring device
  • experimental autoimmune encephalomyelitis
  • I
  • A more detailed evaluation
  • improved results
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