Affective computing, a subcategory of artificial intelligence, detects, processes, interprets, and mimics human emotions. Thanks to the continued advancement of portable non-invasive human sensor technologies, like brain–computer interfaces (BCI), emotion recognition has piqued the interest of academics from a variety of domains. Facial expressions, speech, behavior (gesture/posture), and physiological signals can all be used to identify human emotions. However, the first three may be ineffectual because people may hide their true emotions consciously or unconsciously (so-called social masking). Physiological signals can provide more accurate and objective emotion recognition. Electroencephalogram (EEG) signals respond in real time and are more sensitive to changes in affective states than peripheral neurophysiological signals. Thus, EEG signals can reveal important features of emotional states. Recently, several EEG-based BCI emotion recognition techniques have been developed. In addition, rapid advances in machine and deep learning have enabled machines or computers to understand, recognize, and analyze emotions. This study reviews emotion recognition methods that rely on multi-channel EEG signal-based BCIs and provides an overview of what has been accomplished in this area. It also provides an overview of the datasets and methods used to elicit emotional states. According to the usual emotional recognition pathway, we review various EEG feature extraction, feature selection/reduction, machine learning methods (e.g., k-nearest neighbor), support vector machine, decision tree, artificial neural network, random forest, and naive Bayes) and deep learning methods (e.g., convolutional and recurrent neural networks with long short term memory). In addition, EEG rhythms that are strongly linked to emotions as well as the relationship between distinct brain areas and emotions are discussed. We also discuss several human emotion recognition studies, published between 2015 and 2021, that use EEG data and compare different machine and deep learning algorithms. Finally, this review suggests several challenges and future research directions in the recognition and classification of human emotional states using EEG.
Peptic ulcer disease is an injury of the alimentary tract that leads to a mucosal defect reaching the submucosa. This study aimed to formulate and optimize omega-3 oil as a self-nanoemulsifying drug delivery system (SNEDDS) to achieve oil dispersion in the nano-range in the stomach to augment omega-3 oil gastric ulcer protection efficacy. Three SNEDDS components were selected as the design factors: the concentrations of the oil omega-3 (X1, 10–30%), the surfactant tween 20 and Kolliphor mixture (X2, 20–40%), and the cosurfactant transcutol (X3, 40–60%). The mixture experimental design proposed twenty-three formulations with varying omega-3 SNEDDS formulation component percentages. The optimized omega-3 SNEDDS formula was investigated for gastric ulcer protective effects by evaluating the ulcer index and by the determination of gastric mucosa oxidative stress parameters. Results revealed that optimized omega-3-SNEDDS achieved significant improvement in the gastric ulcer index in comparison with pure omega-3 oil. Histopathological findings confirmed the protective effect of the formulated optimized omega-3 SNEDDS in comparison with omega-3 oil. These findings suggest that formulation of omega-3 in the form of a SNEDDS would be more effective in gastric ulcer protection than the administration of omega-3 as a crude oil.
Background: Peptic ulcer disease, a painful lesion of the gastric mucosa, is considered one of the most common gastrointestinal disorders. This study aims to investigate the formulation of pumpkin seed oil (PSO)-based nanostructured lipid carriers (NLCs) to utilize PSO as the liquid lipid component of NLCs and to achieve oil dispersion in the nano-range in the stomach. Methods: Box-Behnken design was utilized to deduce the optimum formula with minimum particle size. The optimized PSO-NLCs formula was investigated for gastric ulcer protective effects in Wistar rats by evaluating ulcer index and determination of gastric mucosa oxidative stress parameters. Results: PSO was successfully incorporated as the liquid lipid (LL) component of NLCs.The prepared optimum PSO-NLCs formula showed a size of 64.3 nm. Pretreatment of animals using the optimized PSO-NLCs formula showed significantly (p< 0.001) lower ulcer index compared to indomethacin alone group and significantly (p<0.05) less mucosal lesions compared to the raw oil. Conclusion: These results indicated great potential for future application of optimized PSO-NLCs formula for antiulcer effect in non-steroidal anti-inflammatory drug (NSAID)-induced gastric ulcer.
Peripheral neuropathy is a common adverse effect associated with the use of a group of chemotherapeutic agents including paclitaxel (PTX) which negatively affect the quality of life of cancer survivors. In addition, it is considered as a dose-limiting side effect that hinder completion of appropriate chemotherapy regimen. In spite of 27 years of research in mechanisms of PTX neuropathy, there is no approved therapy for prevention of PTX-induced peripheral neuropathy (PIPN). Thus, there is a continuous need to characterize the possible mechanisms associates with PIPN in order to find appropriate targeted therapy for this clinical problem. In this review, most of the recent findings of the cellular targets implicated in PIPN are summarized.
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