To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

Objective. Sleep apnea significantly decreases the quality of life. The apnea hypopnea index (AHI) is the main indicator for sleep apnea diagnosis. This study explored a novel automatic algorithm to diagnose sleep apnea from nasal airflow (AF) and pulse oximetry (SpO2) signals. Approach. Of the 988 polysomnography (PSG) records from the sleep heart health study (SHHS), 45 were randomly selected for the development of an algorithm and the remainder for validation (n = 943). The algorithm detects apnea events by a digitization process, following the determination of the peak excursion (peak-to-trough amplitude) from AF envelope. Hypopnea events were determined from the AF envelope and oxygen desaturation with correction to time lag in SpO2. Total sleep time (TST) was estimated from an optimized percentage of artefact-free total recording time. AHI was estimated from the number of detected events divided by the estimated TST. The estimated AHI was compared to the scored SHHS data for performance evaluation. Main results. The validation showed good agreement between the estimated and scored AHI (intraclass correlation coefficient of 0.95 and mean ±95% limits of agreement of −1.6 ±12.5 events h−1). The diagnostic accuracies were found: 90.7%, 91%, and 96.7% for AHI cut-off ≥5, ≥15, and ≥30 respectively. Significance. The new algorithm is accurate over other existing methods for the automatic diagnosis of sleep apnea. It is applicable to any portable sleep screeners especially for the home diagnosis of sleep apnea.

Multidrug-resistant Vibrio parahaemolyticus has become a significant public health concern. The development of effective drugs and vaccines against Vibrio parahaemolyticus is the current research priority. Thus, we aimed to find out effective drug and vaccine targets using a comprehensive genome-based analysis. A total of 4822 proteins were screened from V. parahaemolyticus proteome. Among 16 novel cytoplasmic proteins, 'VIBPA Type II secretion system protein L' and 'VIBPA Putative fimbrial protein Z' were subjected to molecular docking with 350 human metabolites, which revealed that Eliglustat, Simvastatin and Hydroxocobalamin were the top drug molecules considering free binding energy. On the contrary, 'Sensor histidine protein kinase UhpB' and 'Flagellar hook-associated protein of 25 novel membrane proteins were subjected to T-cell and B-cell epitope prediction, antigenicity testing, transmembrane topology screening, allergenicity and toxicity assessment, population coverage analysis and molecular docking analysis to generate the most immunogenic epitopes. Three subunit vaccines were constructed by the combination of highly antigenic epitopes along with suitable adjuvant, PADRE sequence and linkers. The designed vaccine constructs (V1, V2, V3) were analyzed by their physiochemical properties and molecular docking with MHC molecules-results suggested that the V1 is superior. Besides, the binding affinity of human TLR-1/2 heterodimer and construct V1 could be biologically significant in the development of the vaccine repertoire. The vaccine-receptor complex exhibited deformability at a minimum level that also strengthened our prediction. The optimized codons of the designed construct was cloned into pET28a(+) vector of E. coli strain K12. However, the predicted drug molecules and vaccine constructs could be further studied using model animals to combat V. parahaemolyticus associated infections.

Sleep apnea elicits brain and physiological changes and its duration varies across the night. This study investigates the changes in the relative powers in electroencephalogram (EEG) frequency bands before and at apnea termination and as a function of apnea duration. The analysis was performed on 30 sleep records (375 apnea events) of older adults diagnosed with sleep apnea. Power spectral analysis centered on two 10‐s EEG epochs, before apnea termination (BAT) and after apnea termination (AAT), for each apnea event. The relative power changes in EEG frequency bands were compared with changes in apnea duration, defined as Short (between 10 and 20 s), Moderate (between 20 and 30 s) and Long (between 30 and 40 s). A significant reduction in EEG relative powers for lower frequency bands of alpha and sigma were observed for the Long compared to the Moderate and Short apnea duration groups at BAT, and reduction in relative theta, alpha and sigma powers for the Long compared to the Moderate and Short groups at AAT. The proportion of apnea events showed a significantly decreased trend with increased apnea duration for non‐rapid eye movement sleep but not rapid eye movement sleep. The proportion of central apnea events decreased with increased apnea duration, but not obstructive episodes. The findings suggest EEG arousal occurred both before and at apnea termination and these transient arousals were associated with a reduction in relative EEG powers of the low‐frequency bands: theta, alpha and sigma. The clinical implication is that these transient EEG arousals, without awakenings, are protective of sleep. Further studies with large datasets and different age groups are recommended.

On March 8, 2020, the first Covid-19 case was registered in Bangladesh, and the first death occurred on March 18, 2020. Still, the positive corona patients including banking employees are rising around and many negative thoughts are also increasing day by day in mind. These circumstances make the employees worried. Consequently, this paper's fundamental objective is to find out the psychological status of private commercial bank employees during COVID-19. Using a random sampling technique and a questionnaire through "Google Form" the data was gathered from 151 employees. For the completion of the data analysis procedures, the Statistical Package for Social Sciences (SPSS-22 Version) was used. The major findings showed a maximum of 91% of employees felt nervous when s (he) hears someone died from COVID-19. In addition, 83% of employees are always fear of COVID-19 infection, 81% of employees are fear when they hear someone got tested positive for COVID-19. Besides, 49% of bank employees cannot concentrate on their regular activities while 40% of employees are stressed to lose their current job due to COVID-19. The policy-making authorities of private commercial banks in Bangladesh will get an opportunity to know the employees' psychological status during COVID-19. They can also make some necessary measures (based on our recommendations) to overcome these challenges.