I'm trying to fix a mouse model for my AD experiment which involves multiple cognitive tests at different stages of the disease.
It was mentioned that a considerable amount of CRND8 models don't survive over a year. I don't suppose this should be very common among AD models sine some assays are carried out later. I'm not sure if there are pros and cons worth noticing with this model. Actually I'm wondering how certain models are chosen for particular studies, despite the ones obviously don't fit the experiment design, many models seem to have largely overlapping features.
One major problem here is that I may not be able to work on a long term trial for years with this program, thus I have been concentrating on models that exhibit physiological / cognitive symptoms within 6 months. CRND8 model has a relatively aggressive disease onset and has more information on cognition tests in previous studies than other models found in reviews. Not sure if this is the right way to find models since I haven't got much experience in this.
Id really appreciate it if you can share your experience with Tg AD models.
Thanks.
About 50% of TgCRND8 mice die within the first 6 months of life. The way we deal with this in our long term studies is to account for this loss in our study design. Thus, if you want to end up with n=10 TgCRND8 mice at 6 months, you should start with n=20.
Lynn: thank you! its surely practical to double the group size regarding to the mortality rate, but is there any considerations in experimental outcomes associated with early death of these animals? If substantial number of animals died, shouldn't it imply problems with the ones that survived in the same trail?
Hi Lin, You are right in that you have enriched your population with survivors and whatever that means in terms of physiology and how they might differ from those who died. Thus, they are not the same in some as of yet unidentifed way as those who died. I don't know what you can do about it though besides acknowledge it as a possible interacting factor in your results.
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