A Human Brain Model Mimicking Umbilical Cord Mesenchymal Stem Cells for the Treatment of Hypoxic-Ischemic Brain Injury.

We used an in vitro model of the human brain immune microenvironment to simulate hypoxic-ischemic brain injury (HIBI) and treatment with human umbilical cord mesenchymal stem cells (hUMSCs) to address the transformation barriers of gene differences between animals and humans in preclinical research. A co-culture system, termed hNAME, consisted of human hippocampal neurons (N), astrocytes (A), microglia (M), and brain microvascular endothelial cells (E). Flow cytometry measured the apoptosis rates of neurons and endothelial cells. hNAME-neurons and endothelial cells experienced more severe damage than monolayer cells, particularly after 48 h and 24 h of reoxygenation (OGD48/R24). Western blotting identified neuroinflammatory response markers, including HIF-1α, C1q, C3, TNF-α, and iNOS. Inflammatory factors originated from the glial chamber rather than the neurons and vascular endothelial chambers. A gradual increase in the release of inflammatory factors was observed as the OGD and reoxygenation times increased, peaking at OGD48/R24. The hNAME value was confirmed in human umbilical cord mesenchymal stem cells (hUMSCs). Treatment with hUMSCs resulted in a notable decrease in the severity of neuronal and endothelial cell damage in hNAME. The hNAME is an ideal in vitro model for simulating the immune microenvironment of the human brain because of the interactions between neurons, vessels, astrocytes, and microglia.

Emotion Recognition Based on Dynamic Energy Features Using a Bi-LSTM Network.

Among electroencephalogram (EEG) signal emotion recognition methods based on deep learning, most methods have difficulty in using a high-quality model due to the low resolution and the small sample size of EEG images. To solve this problem, this study proposes a deep network model based on dynamic energy features. In this method, first, to reduce the noise superposition caused by feature analysis and extraction, the concept of an energy sequence is proposed. Second, to obtain the feature set reflecting the time persistence and multicomponent complexity of EEG signals, the construction method of the dynamic energy feature set is given. Finally, to make the network model suitable for small datasets, we used fully connected layers and bidirectional long short-term memory (Bi-LSTM) networks. To verify the effectiveness of the proposed method, we used leave one subject out (LOSO) and 10-fold cross validation (CV) strategies to carry out experiments on the SEED and DEAP datasets. The experimental results show that the accuracy of the proposed method can reach 89.42% (SEED) and 77.34% (DEAP).