Trends in antimicrobial resistance amongst Salmonella Paratyphi A isolates in Bangladesh: 1999-2021.

BACKGROUND: Typhoid and paratyphoid remain common bloodstream infections in areas with suboptimal water and sanitation infrastructure. Paratyphoid, caused by Salmonella Paratyphi A, is less prevalent than typhoid and its antimicrobial resistance (AMR) trends are less documented. Empirical treatment for paratyphoid is commonly based on the knowledge of susceptibility of Salmonella Typhi, which causes typhoid. Hence, with rising drug resistance in Salmonella Typhi, last-line antibiotics like ceftriaxone and azithromycin are prescribed for both typhoid and paratyphoid. However, unlike for typhoid, there is no vaccine to prevent paratyphoid. Here, we report 23-year AMR trends of Salmonella Paratyphi A in Bangladesh. METHODS: From 1999 to 2021, we conducted enteric fever surveillance in two major pediatric hospitals and three clinics in Dhaka, Bangladesh. Blood cultures were performed at the discretion of the treating physicians; cases were confirmed by culture, serological and biochemical tests. Antimicrobial susceptibility was determined following CLSI guidelines. RESULTS: Over 23 years, we identified 2,725 blood culture-confirmed paratyphoid cases. Over 97% of the isolates were susceptible to ampicillin, chloramphenicol, and cotrimoxazole, and no isolate was resistant to all three. No resistance to ceftriaxone was recorded, and >99% of the isolates were sensitive to azithromycin. A slight increase in minimum inhibitory concentration (MIC) is noticed for ceftriaxone but the current average MIC is 32-fold lower than the resistance cut-off. Over 99% of the isolates exhibited decreased susceptibility to ciprofloxacin. CONCLUSIONS: Salmonella Paratyphi A has remained susceptible to most antibiotics, unlike Salmonella Typhi, despite widespread usage of many antibiotics in Bangladesh. The data can guide evidence-based policy decisions for empirical treatment of paratyphoid fever, especially in the post typhoid vaccine era, and with the availability of new paratyphoid diagnostics.

Emotional State Classification from MUSIC-Based Features of Multichannel EEG Signals.

Electroencephalogram (EEG)-based emotion recognition is a computationally challenging issue in the field of medical data science that has interesting applications in cognitive state disclosure. Generally, EEG signals are classified from frequency-based features that are often extracted using non-parametric models such as Welch's power spectral density (PSD). These non-parametric methods are not computationally sound due to having complexity and extended run time. The main purpose of this work is to apply the multiple signal classification (MUSIC) model, a parametric-based frequency-spectrum-estimation technique to extract features from multichannel EEG signals for emotional state classification from the SEED dataset. The main challenge of using MUSIC in EEG feature extraction is to tune its parameters for getting the discriminative features from different classes, which is a significant contribution of this work. Another contribution is to show some flaws of this dataset for the first time that contributed to achieving high classification accuracy in previous research works. This work used MUSIC features to classify three emotional states and achieve 97% accuracy on average using an artificial neural network. The proposed MUSIC model optimizes a 95-96% run time compared with the conventional classical non-parametric technique (Welch's PSD) for feature extraction.

A Narrative Review on Clinical Applications of fNIRS.

Functional near-infrared spectroscopy (fNIRS) is a relatively new imaging modality in the functional neuroimaging research arena. The fNIRS modality non-invasively investigates the change of blood oxygenation level in the human brain utilizing the transillumination technique. In the last two decades, the interest in this modality is gradually evolving for its real-time monitoring, relatively low-cost, radiation-less environment, portability, patient-friendliness, etc. Including brain-computer interface and functional neuroimaging research, this technique has some important application of clinical perspectives such as Alzheimer's disease, schizophrenia, dyslexia, Parkinson's disease, childhood disorders, post-neurosurgery dysfunction, attention, functional connectivity, and many more can be diagnosed as well as in some form of assistive modality in clinical approaches. Regarding the issue, this review article presents the current scopes of fNIRS in medical assistance, clinical decision making, and future perspectives. This article also covers a short history of fNIRS, fundamental theories, and significant outcomes reported by a number of scholarly articles. Since this review article is hopefully the first one that comprehensively explores the potential scopes of the fNIRS in a clinical perspective, we hope it will be helpful for the researchers, physicians, practitioners, current students of the functional neuroimaging field, and the related personnel for their further studies and applications.

Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation.

This paper proposes a novel feature selection method utilizing Rényi min-entropy-based algorithm for achieving a highly efficient brain-computer interface (BCI). Usually, wavelet packet transformation (WPT) is extensively used for feature extraction from electro-encephalogram (EEG) signals. For the case of multiple-class problem, classification accuracy solely depends on the effective feature selection from the WPT features. In conventional approaches, Shannon entropy and mutual information methods are often used to select the features. In this work, we have shown that our proposed Rényi min-entropy-based approach outperforms the conventional methods for multiple EEG signal classification. The dataset of BCI competition-IV (contains 4-class motor imagery EEG signal) is used for this experiment. The data are preprocessed and separated as the classes and used for the feature extraction using WPT. Then, for feature selection Shannon entropy, mutual information, and Rényi min-entropy methods are applied. With the selected features, four-class motor imagery EEG signals are classified using several machine learning algorithms. The results suggest that the proposed method is better than the conventional approaches for multiple-class BCI.

Modeling and classification of voluntary and imagery movements for brain-computer interface from fNIR and EEG signals through convolutional neural network.

Practical brain-computer interface (BCI) demands the learning-based adaptive model that can handle diverse problems. To implement a BCI, usually functional near-infrared spectroscopy (fNIR) is used for measuring functional changes in brain oxygenation and electroencephalography (EEG) for evaluating the neuronal electric potential regarding the psychophysiological activity. Since the fNIR modality has an issue of temporal resolution, fNIR alone is not enough to achieve satisfactory classification accuracy as multiple neural stimuli are produced by voluntary and imagery movements. This leads us to make a combination of fNIR and EEG with a view to developing a BCI model for the classification of the brain signals of the voluntary and imagery movements. This work proposes a novel approach to prepare functional neuroimages from the fNIR and EEG using eight different movement-related stimuli. The neuroimages are used to train a convolutional neural network (CNN) to formulate a predictive model for classifying the combined fNIR-EEG data. The results reveal that the combined fNIR-EEG modality approach along with a CNN provides improved classification accuracy compared to a single modality and conventional classifiers. So, the outcomes of the proposed research work will be very helpful in the implementation of the finer BCI system.

Study on adsorption of Congo red onto chemically modified egg shell membrane.

Consumption of eggs leads to generation of huge amount of waste in the form of egg shell, which consists of calcined shell and fibrous membrane. In this study, egg shell membrane (ESM) were chemically modified and used to adsorb congo red from its synthetic aqueous solution. Fourier Transform Infra-red (FTIR) spectra confirmed the presence of hydroxyl, carbonyl and methylene groups in egg shell membrane. Scanning Electron Micrographs (SEM) were also performed to characterize the modified egg shell membrane. Concentration of congo red was measured by using UV spectrophotometer. Effects of various parameters such as initial pH, ESM dosage, contact time and initial congo red concentration were investigated. Highest percentage of adsorption (98%) was obtained at pH 4.5 at a solid to liquid ratio of 1 g-100 ml of congo red solution of concentration of 100 mg/l. Pseudo-second order kinetic model was the best fitted model for this study. The reaction rate constant was found to be 58.04 × 10-3 g mg-1·min-1. The adsorption mechanism was supposed to happen via film diffusion as well as via intraparticle diffusion. Langmuir isotherm gave a better fit for the adsorption compared to the Freundlich isotherm. The maximum monolayer adsorption capacity was determined to be 117.65 mg/g. Thermodynamic parameters (ΔG, ΔH and ΔS) were also studied in this work. The change in enthalpy and change in entropy was found to be 29.217 kJ/mol and 0.124 kJ/mol·K, respectively. The values of ΔG were found to become more negative with increasing temperature.

A combined view of B-cell epitope features in antigens.

B-cell epitope mapping is a promising approach to identify therapeutics and vaccine candidates in antigenic proteins. We used MATLAB programming to view in combination different features such as beta turn region, surface accessibility, antigenicity and hydrophilicity in an antigen sequence to help predict a discontinuous, conformational B-cell epitope. We analyzed, grouped, compared, matched and superposed these features for a combined visualization using MATLAB programming for identifying and illustrating a potential B-cell epitope region in an antigen protein. This protocol finds application in the design and development of an effective B cell epitope candidate.

Maternal health care-seeking behaviour of married adolescent girls: A prospective qualitative study in Banke District, Nepal.

BACKGROUND: Nepal has one of the highest rates of maternal mortality in the South Asia region, partly due to the underutilization of maternal health services and the high number of adolescent pregnancies. This study explores married Nepali adolescent girls' healthcare-seeking behaviour throughout their pregnancies, during their delivery and postpartum. METHODS: We conducted a prospective qualitative study in Banke district, Nepal. In-depth interviews were conducted with 27 married adolescent girls before and after delivery. In addition, a focus group discussion was conducted with community health works and key-informant interviews were conducted with family members of adolescent girls, representatives from the government and health care providers. We applied the Social-Ecological Model (SEM) as a framework to guide thematic content analysis and presentation of our qualitative data. RESULTS: Several factors in the SEM influenced maternal health care-seeking behaviour of adolescents. At the individual level, girls' perceptions, their lack of knowledge about maternal and reproductive health, certain traditional practices, their sole dependency on their husbands and mothers-in-laws and their low decision-making autonomy towards their own health care negatively influenced their utilization of skilled maternal health services. Mothers-in-law and other family members played a critical role in either encouraging or discouraging the use of skilled maternal health services. At the health systems level, lack of adolescent-friendly maternal health services, difficulties in accessing quality maternal health services, and the fixed operating hours of public health facilities restricted their ability to obtain services. The existence of the Safe Motherhood Programme, knowledge sharing platforms such as "women's groups" and the active role of Female Community Health Volunteers (FCHVs) positively influenced utilization of skilled maternal health services among these girls. CONCLUSION: Influences on married adolescent girls' use of skilled maternal health services in Banke District, Nepal were multi-factoral. Ensuring easy access and availability of adolescent-friendly maternal health services are important to encourage adolescent girls to use skilled maternal health services. Moreover, interventions are needed to improve adolescent girls' knowledge of maternal health, keep them in school, involve family members (mainly mothers-in-law) in health interventions, as well as overcome negative traditional beliefs within the community that discourage care-seeking for skilled maternal health services.