Evaluation of the Antidepressant Effect of the Functional Beverage Containing Active Peptides, Menthol and Eleutheroside and Investigation of Its Mechanism of Action in Mice.

RESEARCH BACKGROUND: Depression has become a global threat to human health. In order to solve it, researchers have conducted multi-faceted studies including diet. Many food-derived bioactive substances have shown antidepressant effects. However, there are few studies on the design of industrialized food with antidepressant effect. This study aims to evaluate the antidepressant effect of a functional beverage made from several ingredients with potential antidepressant function and investigate its antidepressant mechanisms. EXPERIMENTAL APPROACH: The beverage consists of peppermint oil, active peptides derived from bovine milk casein and Acanthopanax senticosus extract (ASE) whose active ingredient is eleutheroside. Different amounts of ASE were evaluated to determine the optimal concentration of eleutheroside in this functional beverage to deliver the best antidepressant effect through extensive behavioral testing, including preliminary acute stress experiments and further chronic unpredictable mild stress test. RESULTS AND CONCLUSIONS: The results demonstrated that the beverage with 15 mg/kg of eleutheroside could significantly reduce the mice's immobility time of tail suspension test and forced swimming test, recover mice's sucrose preference and behavior changes in the open field test, improve the contents of dopamine, norepinephrine, 5-hydroxytryptamine and the activity of superoxide dismutase and reduce the content of malondialdehyde in mice's brains, which indicated that the improvement of monoamine neurotransmitter systems and antioxidation was one potential mechanism of antidepressant action. NOVELTY AND SCIENTIFIC CONTRIBUTION: This study provides a design of antidepressant functional beverage and an efficient way for the prevention and treatment of depression.

M-DDC: MRI based demyelinative diseases classification with U-Net segmentation and convolutional network.

Childhood demyelinative diseases classification (DDC) with brain magnetic resonance imaging (MRI) is crucial to clinical diagnosis. But few attentions have been paid to DDC in the past. How to accurately differentiate pediatric-onset neuromyelitis optica spectrum disorder (NMOSD) from acute disseminated encephalomyelitis (ADEM) based on MRI is challenging in DDC. In this paper, a novel architecture M-DDC based on joint U-Net segmentation network and deep convolutional network is developed. The U-Net segmentation can provide pixel-level structure information, that helps the lesion areas location and size estimation. The classification branch in DDC can detect the regions of interest inside MRIs, including the white matter regions where lesions appear. The performance of the proposed method is evaluated on MRIs of 201 subjects recorded from the Children's Hospital of Zhejiang University School of Medicine. The comparisons show that the proposed DDC achieves the highest accuracy of 99.19% and dice of 71.1% for ADEM and NMOSD classification and segmentation, respectively.

Differentiation of MOGAD in ADEM-like presentation children based on FLAIR MRI features.

OBJECTIVE: The differences in magnetic resonance imaging (MRI) between children with classic acute disseminated encephalomyelitis (ADEM) and myelinal oligodendrocyte glycoprotein antibody associated disease (MOGAD) with ADEM-like presentation are controversial. The purpose of this study was to investigate whether the radiological characteristics of the MRI-FLAIR sequence can predict MOGAD in children with ADEM-like presentation and to further explore its imaging differences. METHODS: We extracted 1041 radiomics features from MRI-FLAIR lesions. Then we used the redundancy analysis (Spearman correlation coefficient), significance test (student test or Mann-Whitney U test), least absolute contraction and selection operator (LASSO) to select potential predictors from the feature groups. The selected potential predictors and MOG antibody test results were used to fit the machine learning model for classification. Combined with feature selection and machine learning classifiers, the optimal model for each subgroup was derived. The resulting models have been evaluated using the receiver operator characteristic curve (ROC) at the lesion level and the model performance was evaluated at the case level using decision curve analysis. RESULTS: We retrospectively reviewed and re-diagnosed 70 ADEM-like presentation cases in our center from April 2015 to January 2020. Including 49 cases with classic ADEM and 21 cases with MOGAD. 30(43%) were female, with a median age of 5.3 years. On the four subgroups by age and gender, the area under the curve (AUC) of the optimal models were 89%, 90%, 98%, and 99%, and the MOGAD detection rates (Specificity) were 83%, 83%, 92%, and 75%, respectively. CONCLUSIONS: The machine learning model trained on radiomics features of MR-FLAIR images can effectively predict patients' MOGAD. This study provides a fast, objective, and quantifiable method for MOGAD diagnosis.