Antibacterial activity and mechanism of action of chitosan nanofibers against toxigenic Clostridioides (Clostridium) difficile Isolates

Background. Clostridioides difficile a Gram-positive, obliged anaerobic, rod-shaped spore-former bacterium, causes a wide spectrum of diseases, ranging from mild, self-limiting diarrhoea to serious diarrhea. Chitosan, a natural polysaccharide, is largely known for its activity against a wide range of microorganisms. Chitosan, in the form of nanofibrils (nanofibrilated chitosan), consists of separated fibers which can be suspended easily in aqueous media. Study design. This paper, for the first time, aims to evaluate the antimicrobial activity of chitosan nanofibers against C. difficile isolates. Methods. Chitosan nanofibers were characterized through scanning electron microscopy and atomic force microscopy. Minimum inhibitory concentration and minimum bactericidal concentration of chitosan nanofibers against toxigenic C. difficile isolates (with resistance gene: ermB, tetM and tetW) was determined by the standard broth microdilution method. Results. The Miniumum Inhibitory Concentration of chitosan nanofibers for two toxigenic isolates with resistance genes ermB, tetM and tetW, two toxigenic isolates ermB+ tetM+ and the standard strain ATCC 700057 was similar and equal to 0.25 mg/mL. The minimum bactericidal concentration for all isolates was 0.5 mg/mL. Conclusions. The results demonstrated that chitosan nanofibers exhibit potent antimicrobial activities against multiple toxigenic C. difficile isolates, and the antibacterial effect of chitosan nanofibers against C. difficile isolates with ermB, tetM and tetW resistance genes indicates that interfering with the synthesis of proteins is not the mechanism of action of chitosan nanofibers

Automatically Identified EEG Signals of Movement Intention Based on CNN Network (End-To-End)

Movement-based brain–computer Interfaces (BCI) rely significantly on the automatic identification of movement intent. They also allow patients with motor disorders to communicate with external devices. The extraction and selection of discriminative characteristics, which often boosts computer complexity, is one of the issues with automatically discovered movement intentions. This research introduces a novel method for automatically categorizing two-class and three-class movement-intention situations utilizing EEG data. In the suggested technique, the raw EEG input is applied directly to a convolutional neural network (CNN) without feature extraction or selection. According to previous research, this is a complex approach. Ten convolutional layers are included in the suggested network design, followed by two fully connected layers. The suggested approach could be employed in BCI applications due to its high accuracy

Automatically Identified EEG Signals of Movement Intention Based on CNN Network (End-To-End)

Movement-based brain–computer Interfaces (BCI) rely significantly on the automatic identification of movement intent. They also allow patients with motor disorders to communicate with external devices. The extraction and selection of discriminative characteristics, which often boosts computer complexity, is one of the issues with automatically discovered movement intentions. This research introduces a novel method for automatically categorizing two-class and three-class movement-intention situations utilizing EEG data. In the suggested technique, the raw EEG input is applied directly to a convolutional neural network (CNN) without feature extraction or selection. According to previous research, this is a complex approach. Ten convolutional layers are included in the suggested network design, followed by two fully connected layers. The suggested approach could be employed in BCI applications due to its high accuracy