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. The content validity index (CVI) and content validity ratio (CVR) were calculated quantitatively. To determine the internal consistency and scale reliability, a test-retest was used. RESULTS: The Cronbach's α coefficient was 0.83 and the intraclass correlation coefficient (95% confidence interval) was 0.85 (0.82, 0.88). Cronbach's α in all domains (incontinence, storage and voiding, and consequence) was greater than 0.70. The criterion validity also showed a positive correlation of NBSS with the International Consultation on Incontinence Questionnaire-overactive bladder (p < 0.001 and r = 0.55). The face validity was acceptable according to the opinion of 10 participants. To determine the CVI and the CVR, 10 experts' panels reviewed all items related to the questionnaire in terms of necessity, clarity, transparency, and relevancy. Based on the expert panel all NBSS criteria got a high score, and all criteria were essential to assess NLUTD (CVI: 0.78-1). CONCLUSIONS: Regarding validity and reliability, the Persian version NBSS questionnaire can properly evaluate NLUTD in MS or stroke/CVA patients.

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. Phenotypic and genotyping identification of 80 E. faecalis isolates were performed. Minimum inhibitory concentrations (MICs) to ciprofloxacin (CIP) were measured with and without carbonylcyanide 3-chlorophenylhydrazone (CCCP) by broth microdilution. After DNA extraction and sequencing for detection of efrA and efrB genes, the efrAB efflux positive isolates that were resistant to ciprofloxacin and showed decrease of ciprofloxacin MIC range were identified. Isolates that exhibited decrease in ciprofloxacin MIC range from two to ten folds were assessed for biofilm formation and finally, the expression levels of efrB, efrA genes were measured by quantitative Real-Time PCR (qRT-PCR). High rates of resistance to tetracycline and minocycline and low rates of resistance to the most antibiotics used in this study were detected. The results in this study indicated that the incidence of Multiple drug resistance (MDR) was 23.7% and all isolates that were resistant to ciprofloxacin revealed several degrees of overexpression in efrA and efrB genes. Our study found two ST480 and one ST847 in E. faecalis isolates. In conclusion, despite of low frequency of resistance to the most antibiotics and MDRs in our region, we found one ST480 isolate with resistance to eight antibiotics that also exists in other parts of the world.

Strategies to Overcome Antimicrobial Resistance in Nosocomial Infections, A Review and Update.

Nosocomial infections, also known as healthcare-associated infections, are a signif-icant global concern due to their strong association with high mortality and morbidity in both developed and developing countries. These infections are caused by a variety of pathogens, particularly the ESKAPE group of bacteria, which includes the six pathogens Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudo-monas aeruginosa, and Enterobacter spp. These bacteria have demonstrated noteworthy re-sistance to different antibiotics. Antimicrobial resistance mechanisms can manifest in various forms, including restricting drug uptake, modifying drug targets, inactivating drugs, active drug efflux, and biofilm formation. Accordingly, various strategies have been developed to combat antibiotic-resistant bacteria. These strategies encompass the development of new antibiotics, the utilization of bacterio-phages that specifically target these bacteria, antimicrobial combination therapy and the use of peptides or enzymes that target the genomes or essential proteins of resistant bacteria. Among promising approaches to overcome antibiotic resistance, the CRISPR/Cas system stands out and offers many advantages. This system enables precise and efficient editing of genetic material at specific locations in the genome. Functioning as a bacterial "adaptive im-mune system," the CRISPR/Cas system recognizes, degrades, and remembers foreign DNA sequences through the use of spacer DNA segments that are transcribed into CRISPR RNAs (crRNA). This paper has focused on nosocomial infections, specifically the pathogens involved in hospi-tal infections, the mechanisms underlying bacterial resistance, and the strategies currently em-ployed to address this issue. Special emphasis has been placed on the application of CRISPR/Cas technology for overcoming antimicrobial resistance.

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. To find the optimal choice in some of the steps of these layers, three different tests have been conducted. The first is finding the perfect window in feature extraction section that resulted in superiority of Hamming window to the other windows. The second is choosing the most appropriate number of filter bank and the best result was 26. The third test was also emotion recognition that its accuracy was 92.93 for arousal dimension, 92.64 for valence dimension, 93.14 for dominance dimension in two-class experiment and 76.28 for the arousal, 74.83 for the valence, and 75.64 for dominance in three-class experiment. RESULTS: The results of this method show an improvement of 12.34% and 7.74% in two- and three-class levels in the arousal dimension. This improvement in the valence is 12.77 and 8.52, respectively. CONCLUSION: The results show that the proposed method can be used to improve the accuracy of emotion recognition.

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. In the first part of this research, the statistical community test case included four men and four women, who were 25-30 years old. In the designed task, three groups of traffic signs were considered, in which two groups referred to the concept of danger, and the third one referred to the concept of information. In the second part, the three volunteers, two men and one woman, who had the best results, were chosen from among eight participants. In the second designed task, direction arrows (up, down, left, and right) were used. For the 2/8 volunteers in the rest times, very high-power alpha waves were observed from the back of the head; however, in the thinking times, they were different. According to this result, alpha waves for changing the task from thinking to rest condition took at least 3 s for the two volunteers, and it was at most 5 s until they went to the absolute rest condition. For the 7/8 volunteers, the danger and information signals were well classified; these differences for the 5/8 volunteers were observed in the right hemisphere, and, for the other three volunteers, the differences were observed in the left hemisphere. For the second task, simulations showed that the best classification accuracies resulted when the time window was 2.5 s. In addition, it also showed that the features of the autoregressive (AR)-15 model coefficients were the best choices for extracting the features. For all the states of neural network except hardlim discriminator function, the classification accuracies were almost the same and not very different. Linear discriminant analysis (LDA) in comparison with the neural network yielded higher classification accuracies. ICA is a suitable algorithm for recognizing of the word's concept and its place in the brain. Achieved results from this experiment were the same compared with the results from other methods such as functional magnetic resonance imaging and methods based on the brain signals (EEG) in the vowel imagination and covert speech. Herein, the highest classification accuracy was obtained by extracting the target signal from the output of the ICA and extracting the features of coefficients AR model with time interval of 2.5 s. Finally, LDA resulted in the highest classification accuracy more than 60%.