32 research outputs found
ANet: Autoencoder-Based Local Field Potential Feature Extractor for Evaluating An Antidepressant Effect in Mice after Administering Kratom Leaf Extracts
Kratom (KT) typically exerts antidepressant (AD) effects. However, evaluating
which form of KT extracts possesses AD properties similar to the standard AD
fluoxetine (flu) remained challenging. Here, we adopted an autoencoder
(AE)-based anomaly detector called ANet to measure the similarity of mice's
local field potential (LFP) features that responded to KT leave extracts and AD
flu. The features that responded to KT syrup had the highest similarity to
those that responded to the AD flu at 85.62 0.29%. This finding presents
the higher feasibility of using KT syrup as an alternative substance for
depressant therapy than KT alkaloids and KT aqueous, which are the other
candidates in this study. Apart from the similarity measurement, we utilized
ANet as a multi-task AE and evaluated the performance in discriminating
multi-class LFP responses corresponding to the effect of different KT extracts
and AD flu simultaneously. Furthermore, we visualized learned latent features
among LFP responses qualitatively and quantitatively as t-SNE projection and
maximum mean discrepancy distance, respectively. The classification results
reported the accuracy and F1-score of 79.78 0.39% and 79.53 0.00%.
In summary, the outcomes of this research might help therapeutic design devices
for an alternative substance profile evaluation, such as Kratom-based form in
real-world applications
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The Committee of Higher Education and the Thailand Research Fun