Zhike pingchuan granules improve bronchial asthma by regulating the IL-6/JAK2/STAT3 pathway.

The present study aimed to investigate the effects of zhike pingchuan granules (ZKPC) on bronchial asthma and the underlying mechanism. A bronchial asthma mouse model was established by aerosol inhalation of ovalbumin. The changes in lung pathomorphology were observed by hematoxylin and eosin staining.

Audiological Findings in Patients with Vestibular Migraine and Migraine: History of Migraine May Be a Cause of Low-Tone Sudden Sensorineural Hearing Loss.

Introduction: The aim of this study was to investigate and compare the auditory findings in migraine, vestibular migraine (VM), and healthy controls.

Methods: Twenty-eight migraine patients (56 ears), 18 VM (36 ears), and 25 healthy controls (50 ears) were included. Audiometry, speech discrimination scores, distortion product optoacoustic emission (DPOAE), and auditory brainstem response were tested.

Resveratrol ameliorates hypoxia/ischemia-induced brain injury in the neonatal rat via the miR-96/Bax axis.

Objective: This study was aimed to investigate the mechanism of resveratrol on amelioration of hypoxia/ischemia (H/I)-induced brain injury.

Methods: The RT-PCR and western blot were used to detect the mRNA and protein expressions, respectively. The PC12 cell induced by OGD/R was as in vitro H/I brain injury model.

[A Novel Channel Selection Method for Brain-computer Interface Based on Relief-SBS].

Regarding to the channel selection problem during the classification of electroencephalogram(EEG)signals,we proposed a novel method,Relief-SBS,in this paper.Firstly,the proposed method performed EEG channel selection by combining the principles of Relief and sequential backward selection(SBS)algorithms.And then correlation coefficient was used for classification of EEG signals.

A novel channel selection method for optimal classification in different motor imagery BCI paradigms.

Background: For sensorimotor rhythms based brain-computer interface (BCI) systems, classification of different motor imageries (MIs) remains a crucial problem. An important aspect is how many scalp electrodes (channels) should be used in order to reach optimal performance classifying motor imaginations. While the previous researches on channel selection mainly focus on MI tasks paradigms without feedback, the present work aims to investigate the optimal channel selection in MI tasks paradigms with real-time feedback (two-class control and four-class control paradigms).

EEG-based motor imagery classification accuracy improves with gradually increased channel number.

The question of how many channels should be sed for classification remains a key issue in the study of Brain-Computer Interface. Several studies have shown that a reduced number of channels can achieve the optimal classification accuracy in the offline analysis of motor imagery paradigm, which does not have real-time feedback as in the online control. However, for the cursor movement control paradigm, it remains unclear as to how many channels should be selected in order to achieve the optimal classification.