The effects of glutamate transporters on synaptic plasticity in rat models of pilocarpine-induced status epilepticus were investigated. Male Wista rats ((304.06?13.79) g) were randomly divided into 5 groups, short-term seizures (SE) and its control (SC), long-term seizures (LE) and its control(LC), normal control (Sham) groups. Epilepsy rat models were induced by injection of pilocarpine(25 mg/kg, i.d.). Glutamate transporter inhibitor, DL-threo-benzyloxyaspartate (TBOA, 7.5 nmol,1 ?l) was microinjected into right side of hippocampus after 14 days of initial status epilepticus in SE and LE groups. The same volumes of artificial cerebrospinal fluid were injected into same side of hippocampus in SC and LC groups. Electroencephalographys (EEG) were detected in SE and SC groups after 2 h of drug injection. Long term potential (LTP) at perforant pathway and dentate gyrus(PP-DG) and EEG were recorded in LE and LC groups after two weeks of drug injection. Example of Fluoro-Jade-B staining in the rat brain was made at the end of electrophysiological experiment. The results showed that there was a significant decrease in theta band power of EEG in SE group compared with that of SC group (P 0.05). The slope of excitatory postsynaptic potential (EPSP) was significantly increased in LE group compared with that of LC group (P

ObjectiveTo conduct a bibliometric analysis on the research status of occupational pneumoconiosis (hereinafter referred to as "pneumoconiosis") severity assessment indicators. Methods The domestic and foreign articles on the research of pneumoconiosis severity assessment indicators were accessed from China National Knowledge Infrastructure, Wanfang Data, VIP Database, China Biomedical Literature Service System, PubMed, Cochrane Library, and Web of Science. The methodological quality evaluation and analysis of severity assessment indicators were performed with the relevant articles. Results A total of 88 relevant articles on pneumoconiosis severity assessment indicators were included. The overall evaluation of the literature with good-, moderate-, and poor-quality articles accounted for 18.18%, 69.32%, and 12.50%, respectively. The median sample size reported in each article was 86 cases. The articles reporting the stage of pneumoconiosis accounted for 81.82%, and 80.68% reported the types of pneumoconiosis which was mainly simple silicosis and coal worker's pneumoconiosis. Only 12 articles reported two or more types of pneumoconiosis. A total of 122 severity assessment indicators in four categories were reported in 88 articles, including 99 physiological and biochemical indicators, 10 imaging indicators, six symptoms and signs indicators, and seven other indicators. The articles used a single severity assessment indicator to assess the severity of pneumoconiosis accounted for 76.14%, while 23.86% of the articles used multiple severity assessment indicators, and only 5.68% of the articles selected specific severity assessment indicators for pneumoconiosis patients in different stages. Conclusion The quality of research on pneumoconiosis severity assessment is relatively low. The applicability of the combined use of severity assessment indicators is poor and confused.

Emotion plays an important role in people's cognition and communication. By analyzing electroencephalogram (EEG) signals to identify internal emotions and feedback emotional information in an active or passive way, affective brain-computer interactions can effectively promote human-computer interaction. This paper focuses on emotion recognition using EEG. We systematically evaluate the performance of state-of-the-art feature extraction and classification methods with a public-available dataset for emotion analysis using physiological signals (DEAP). The common random split method will lead to high correlation between training and testing samples. Thus, we use block-wise
Arousal ; Electroencephalography ; Emotions ; Humans ; Memory, Short-Term ; Neural Networks, Computer