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Retinal aging in 3× Tg-AD mice model of Alzheimer's disease
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Orientador(es)
Resumo(s)
The retina, as part of the central nervous system (CNS), can be the perfect target for
in vivo, in situ, and noninvasive neuropathology diagnosis and assessment of therapeutic
efficacy. It has long been established that several age-related brain changes are more
pronounced in Alzheimer’s disease (AD). Nevertheless, in the retina such link is still
under-explored. This study investigates the differences in the aging of the CNS through
the retina of 3×Tg-AD and wild-type mice. A dedicated optical coherence tomograph
imaged mice’s retinas for 16 months. Two neural networks were developed to model
independently each group’s ages and were then applied to an independent set containing
images fromboth groups. Our analysis shows amean absolute error of 0.875±1.1×10−2
and 1.112 ± 1.4 × 10−2 months, depending on training group. Our deep learning
approach appears to be a reliable retinal OCT aging marker. We show that retina aging
is distinct in the two classes: the presence of the three mutated human genes in the
mouse genome has an impact on the aging of the retina. For mice over 4 months-old,
transgenic mice consistently present a negative retina age-gap when compared to wildtype
mice, regardless of training set. This appears to contradict AD observations in the
brain. However, the ‘black-box” nature of deep-learning implies that one cannot infer
reasoning. We can only speculate that some healthy age-dependent neural adaptations
may be altered in transgenic animals.
Descrição
Palavras-chave
Aging Artificial intelligence Age-gap Alzheimer’s disease Deep learning Animal model Retina Optical coherence tomography