Germacrone alleviates neurological deficits following traumatic brain injury by modulating neuroinflammation and oxidative stress.

BACKGROUND: Germacrone (GM) is a terpenoid compound which is reported to have anti-inflammatory and anti-oxidative effects. However, its role in treating traumatic brain injury (TBI) remains largely unknown. METHODS: Male C57BL/6 mice were divided into the following groups: control group, TBI group [controlled cortical impact (CCI) model], CCI + 5 mg/kg GM group, CCI + 10 mg/kg GM group and CCI + 20 mg/kg GM group. GM was administered via intraperitoneal injection. The neurological functions (including motor coordination, spatial learning and memory abilities) and brain edema were measured. Nissl staining was used to detect the neuronal apoptosis. Colorimetric assays and enzyme linked immunosorbent assay (ELISA) kits were used to determine the expression levels of oxidative stress markers including myeloperoxidase (MPO), malondialdehyde (MDA) and superoxide dismutase (SOD), as well as the expressions of inflammatory markers, including tumor necrosis factor α (TNF-α), interleukin-1ß (IL-1ß) and interleukin-6 (IL-6). Additionally, protein levels of Nrf2 and p-p65 were detected by Western blot assay. RESULTS: GM significantly ameliorated motor dysfunction, spatial learning and memory deficits of the mice induced by TBI and it also reduced neuronal apoptosis and microglial activation in a dose-dependent manner. Besides, GM treatment reduced neuroinflammation and oxidative stress compared to those in the CCI group in a dose-dependent manner. Furthermore, GM up-regulated the expression of antioxidant protein Nrf2 and inhibited the expression of inflammatory response protein p-p65. CONCLUSIONS: GM is a promising drug to improve the functional recovery after TBI via repressing neuroinflammation and oxidative stress.

Confused or not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks.

Brain fog, also known as confusion, is one of the main reasons for low performance in the learning process or any kind of daily task that involves and requires thinking. Detecting confusion in a human's mind in real time is a challenging and important task that can be applied to online education, driver fatigue detection and so on. In this paper, we apply Bidirectional LSTM Recurrent Neural Networks to classify students' confusion in watching online course videos from EEG data. The results show that Bidirectional LSTM model achieves the state-of-the-art performance compared with other machine learning approaches, and shows strong robustness as evaluated by cross-validation. We can predict whether or not a student is confused in the accuracy of 73.3%. Furthermore, we find the most important feature to detecting the brain confusion is the gamma 1 wave of EEG signal. Our results suggest that machine learning is a potentially powerful tool to model and understand brain activity.