Psychometric properties of the Persian version of neurogenic bladder symptom score questionnaire: A methodological study on patients with multiple sclerosis and stroke.

Objectives: Concerning the impact of neurogenic lower urinary tract dysfunction (NLUTD) on quality of life, besides the lack of standard psychometrically-analyzed Persian tools, the present study investigated the validity and reliability of the Persian version of the neurogenic bladder symptom score (NBSS) questionnaire.

Methods: A total of 279 multiple sclerosis (MS) or stroke/cerebrovascular accident (CVA) patients over 18 years of age with NLUTD referred to the neurology clinics entered the study in 2021-2022. After translation, back-translation, and developing a Persian version, its validity and reliability were determined.

Upregulation of efrAB efflux pump among Enterococcus faecalis ST480, ST847 in Iran.

Antibiotic resistance and especially multiresistance in Enterococci, is a serious public health issue especially in infections of immunocompromised patients. EfrAB is a heterodimeric multidrug ATP-binding cassette (ABC) transporter that causes endogenous resistance to antimicrobials including fluoroquinolones in Enterococcus spp. The aim of this study was to seek the gene expression rate and role of efrAB efflux pump in ciprofloxacin resistant Enterococcus faecalis and Multilocus Sequence Typing (MLST) of multiresistant isolates.

Providing a Four-layer Method Based on Deep Belief Network to Improve Emotion Recognition in Electroencephalography in Brain Signals.

Background: One of the fields of research in recent years that has been under focused is emotion recognition in electroencephalography (EEG) signals. This study provides a four-layer method to improve people's emotion recognition through these signals and deep belief neural networks.

Methods: In this study, using DEAP dataset, a four-layer method is established, which includes (1) preprocessing, (2) extracting features, (3) dimension reduction, and (4) emotion identification and estimation.

ICA-Based Imagined Conceptual Words Classification on EEG Signals.

Independent component analysis (ICA) has been used for detecting and removing the eye artifacts conventionally. However, in this research, it was used not only for detecting the eye artifacts, but also for detecting the brain-produced signals of two conceptual danger and information category words. In this cross-sectional research, electroencephalography (EEG) signals were recorded using Micromed and 19-channel helmet devices in unipolar mode, wherein Cz electrode was selected as the reference electrode.