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ABSTRACT Background University is a critical period regarding unhealthy changes in eating behaviors in students. University students often face significant changes in their eating habits and physical activity levels, which can impact their overall health. Aims To investigate the eating habits and sedentary behavior of university students in Dhaka. Methods This research was based on a cross‐sectional study. The snowball sampling technique was applied to survey university students in Dhaka city. Structured questionnaires were used for data collection through an online survey. Data collection was done from November 2022 to April 2023. Descriptive statistics, including frequency, percentage, mean, and standard deviation (SD), were computed for the variables of interest. Chi‐squared test was performed to estimate the association between the participants' eating habits, sedentary behaviors, and BMI and eating habits. In all analyses, a p‐value of

Abstract Electroencephalography (EEG), despite its inherited complexity, is a preferable brain signal for automatic human emotion recognition (ER), which is a challenging machine learning task with emerging applications. In any automatic ER, machine learning (ML) models classify emotions using the extracted features from the EEG signals, and therefore, such feature extraction is a crucial part of ER process. Recently, EEG channel connectivity features have been widely used in ER, where Pearson correlation coefficient (PCC), mutual information (MI), phase-locking value (PLV), and transfer entropy (TE) are well-known methods for connectivity feature map (CFM) construction. CFMs are typically formed in a two-dimensional configuration using the signals from two EEG channels, and such two-dimensional CFMs are usually symmetric and hold redundant information. This study proposes the construction of a more informative CFM that can lead to better ER. Specifically, the proposed innovative technique intelligently combines CFMs’ measures of two different individual methods, and its outcomes are more informative as a fused CFM. Such CFM fusion does not incur additional computational costs in training the ML model. In this study, fused CFMs are constructed by combining every pair of methods from PCC, PLV, MI, and TE; and the resulting fused CFMs PCC + PLV, PCC + MI, PCC + TE, PLV + MI, PLV + TE, and MI + TE are used to classify emotion by convolutional neural network. Rigorous experiments on the DEAP benchmark EEG dataset show that the proposed CFMs deliver better ER performances than CFM with a single connectivity method (e.g., PCC). At a glance, PLV + MI-based ER is shown to be the most promising one as it outperforms the other methods.

The role of aldose reductase (ALR), the key enzyme of the polyol pathway, has been firmly established in hyperglycemia-induced diabetic complications. Therefore, the present study focused on the screening of phytochemicals reported in Coccinia grandis against ALR using in-silico methodologies encompassing molecular docking, pharmacokinetics, molecular dynamic simulation, free energy calculation (MMGBSA), and quantum mechanics. A comprehensive array of 101 compounds from C. grandis documented in IMPPAT database and different literatures have been selected in this study. These compounds were meticulously docked with the ALR (PDB ID: 1EL3), yielding docking scores spanning a range of −5.8 to −11.0 kcal/mol compared to the positive control epalrestat with a score of −7.9kcal/mol. Among them, four compounds have been emerged as the most promising ALR inhibitors: tiliroside, lukianol B, formononetin, and trachelogenin, with docking scores of −11.0, −10.7, −10.4, and −10.2, respectively. Importantly, these compounds exhibited notable stability throughout 100 ns dynamic simulations compared to the control drug, aligning with Lipinski's rule of 5, standard ADME properties, and evincing an absence of anomalous toxic effects. Therefore, these compounds hold great promise as leads to the development of potent ALR inhibitors; however, further studies are needed to warrant their uses in ameliorating diabetic complications.

Pneumonia, an infectious lung disease, is a significant global health concern, claiming the lives of 740,180 children in 2019, which accounts for 14 % of pediatric deaths. Recently, the management of pediatric pneumonia has become complex due to exposure to indoor and outdoor environmental toxins. Notably, exposure to pollutants and antibiotic-resistant bacteria during community and hospital-acquired pneumonia poses a serious threat to patient outcomes. The development of antibiotic resistance against Streptococcus pneumoniae is driven by the DinF efflux pump protein, which expels antibiotics from bacterial cells, leading to multidrug resistance. Therefore, blocking DinF could serve as a strategy to combat antibiotic resistance. This study aimed to identify DinF inhibitors from compounds analogous to verapamil, a well-known DinF inhibitor, through computational techniques including molecular docking, molecular dynamics simulations, ADMET analysis, MMGBSA, quantum mechanics, and Network Pharmacology. After considering docking scores, results of MMGBSA, quantum mechanics, Molecular Dynamics Simulations, ADMET analysis and network pharmacology profiling four compounds namely netarsudil, rimegepant, GSK-1521498 and tariquidar were found as the most promising DinF inhibitors. Therefore, these compounds hold a great promise as leads to the development of potent DinF inhibitors; however, further studies are needed to warrant their clinical uses against antibiotic resistant Streptococcus pneumoniae-induced pneumonia.

Classifying distinct human emotions, the fundamental purpose of brain-computer interface research, is essential for providing instant personalized services and assistance to individuals. With such emerging applications for individuals, several techniques have been proposed recently to explore interactions between brain regions, such as correlation, synchronization, and dependence. Notably, functional and effective connectivity methods are applied to assess the relationships between different brain areas. The primary objective of this study is to compare the frequently used functional and effective connectivity methods to recognize emotion using Electroencephalogram (EEG) signals. This paper uses a benchmark emotional EEG dataset consisting of 32 channels of EEG signals collected from 32 subjects while they were watching 40 emotional music videos. Specifically, correlation, phase synchronization, and mutual information are used to measure functional brain connectivity, and transfer entropy is used to acquire effective brain connectivity. After extracting the features, they are represented in a two-dimensional connectivity feature map (CFM). The CFMs are then used to classify emotions by a convolutional neural network model. The results of classified emotions are analyzed regarding compatible EEG bands, accuracy, and time. Notably, the Gamma band is found as the most compatible band. The comparative study has demonstrated that though the connectivity method named Pearson correlation coefficient requires less time, the normalized mutual information is the most accurate method with advantageous detecting capability of nonlinear dependencies.

Objective: Gestational Diabetes Mellitus (GDM) is one of the most common complications of pregnancy and becoming an increasing health problem worldwide. The rate of GDM is increasing in Asian countries including Bangladesh. This study is aimed at investigating the association between the rs12255372 (G/T) polymorphism of the TCF7L2 gene with GDM. Method: To carry out the present research, 63 GDM pregnant women and 60 control pregnant women were randomly selected from the city Chattogram, Bangladesh. During the study data was collected between gestational weeks of 24–28. PCR-RFLP was used for genotyping the rs12253372 (G/T) and for genotype analysis Hardy-Weinberg equation (Hardy, 1908) was applied. Results: The fasting and 2-hour plasma glucose level was significantly higher in GDM than the control. Moreover, in the case of family history women with GDM showed higher percentage in first degree relatives (60.31 %) compared to that of control (38.33 %). The frequency of mutant allele T in GDM is 26.2 % which was however not significant. TT genotype was found only in one subject with GDM. However, the percentage of risk allele GT is higher in GDM (49.2 %) compared to that of NGT (35 %). Conclusions: In our pilot study, we did not find an association between rs12255372 (G/T) polymorphism of TCF7L2 gene and GDM. Studying in a broader group may help to find a conclusive result.

Autism Spectrum Disorder (ASD) is a disorder of brain growth with great variability whose clinical presentation initially shows up during early stages or youth, and ASD follows a repetitive pattern of behavior in most cases. Accurate diagnosis of ASD has been difficult in clinical practice as there is currently no valid indicator of ASD. Since ASD is regarded as a neurodevelopmental disorder, brain signals specially electroencephalography (EEG) are an effective method for detecting ASD. Therefore, this research aims at developing a method of extracting features from EEG signal for discriminating between ASD and control subjects. This study applies six prominent connectivity features, namely Cross Correlation (XCOR), Phase Locking Value (PLV), Pearson's Correlation Coefficient (PCC), Mutual Information (MI), Normalized Mutual Information (NMI) and Transfer Entropy (TE), for feature extraction. The Connectivity Feature Maps (CFMs) are constructed and used for classification through Convolutional Neural Network (CNN). As CFMs contain spatial information, they are able to distinguish ASD and control subjects better than other features. Rigorous experimentation has been performed on the EEG datasets collected from Italy and Saudi Arabia according to different criteria. MI feature shows the best result for categorizing ASD and control participants with increased sample size and segmentation. [ABSTRACT FROM AUTHOR]

Magnesium (Mg) and its alloys are modern implant materials that are remarkably similar to natural bone. Biomedical researchers have taken notice of its exceptional mechanical properties, biodegradability, and biocompatibility. Despite their excellent physical and mechanical properties, their alloys are restricted to medical applications because of their low corrosion resistance and rapid structural deterioration in physiological environments. The coating is one of the most effective methods to overcome these limitations and improve corrosion resistance. Hydroxyapatite (HA) is a natural inorganic mineral applied as a coating material that shows superior biocompatibility, bioactivity, high osteoconductivity, non-toxicity, non-inflammatory properties, and a slower degradation and corrosion rate when incorporated on an Mg alloy surface. However, HA applications are limited in scope because of their inherent brittleness. Hybrid coatings are frequently used because they can be combined with inorganic and organic coating materials to overcome the limitations of a single-layer coating, such as corrosion resistance, adhesion, and mechanical integrity. Despite layer-by-layer coating, the hybrid coating has excellent mechanical, physiochemical, and biomedical properties. This review summarizes the most recent developments in hybrid coatings with synthetic polymer coatings (PCL/HA, PLA/HA), natural polymer coatings (SF + HA, collagen/HA), and HA/TiO2-based hybrid coatings on biodegradable Mg alloys. In addition, their biocompatibility, corrosion resistance, and mechanical properties have been discussed. Biopolymer-based hybrid coatings could be a promising means to alter the constraints of Mg alloys in biomedical applications.

Abstract Background A consensus is developing on interventions to improve newborn survival, but little is known about how to reduce socioeconomic inequalities in newborn mortality in low- and middle-income countries. Participatory learning and action (PLA) through women’s groups can improve newborn survival and home care practices equitably across socioeconomic strata, as shown in cluster randomised controlled trials. We conducted a qualitative study to understand the mechanisms that led to the equitable impact of the PLA approach across socioeconomic strata in four trial sites in India, Nepal, Bangladesh, and Malawi. Methods We conducted 42 focus group discussions (FGDs) with women who had attended groups and women who had not attended, in poor and better-off communities. We also interviewed six better-off women and nine poor women who had delivered babies during the trials and had demonstrated recommended behaviours. We conducted 12 key informant interviews and five FGDs with women’s group facilitators and fieldworkers. Results Women’s groups addressed a knowledge deficit in poor and better-off women. Women were engaged through visual learning and participatory tools, and learned from the facilitator and each other. Facilitators enabled inclusion of all socioeconomic strata, ensuring that strategies were low-cost and that discussions and advice were relevant. Groups provided a social support network that addressed some financial barriers to care and gave women the confidence to promote behaviour change. Information was disseminated through home visits and other strategies. The social process of learning and action, which led to increased knowledge, confidence to act, and acceptability of recommended practices, was key to ensuring behaviour change across social strata. These equitable effects were enabled by the accessibility, relevance, and engaging format of the intervention. Conclusions Participatory learning and action led to increased knowledge, confidence to act, and acceptability of recommended practices. The equitable behavioural effects were facilitated by the accessibility, relevance, and engaging format of the intervention across socioeconomic groups, and by reaching-out to parts of the population usually not accessed. A PLA approach improved health behaviours across socioeconomic strata in rural communities, around issues for which there was a knowledge deficit and where simple changes could be made at home.

This is a cross-sectional study that examined the association between fitness trainers’ educational qualifications and experience, and the risk of their trainees’ musculoskeletal pain. The study included 1177 trainees (aged 15–60 years) from 74 fitness centers in Bangladesh. Data were collected by using the Nordic musculoskeletal questionnaire, including potential confounders such as demographic factors (e.g., age, occupation), and training-related factors (e.g., workout knowledge, overweight lifting). Multiple logistic regression was performed for a binary outcome (pain—yes or no), and a generalized linear model was fitted for the ordinal outcome (pain—sites of the body). The trainers’ lower experience (no or ≤1 year) was associated with higher odds of their trainees’ musculoskeletal pain (OR: 2.53, 95% CI: 1.18–5.44) compared to trainers with >5 years of experience; however, no association was found between the trainers’ education and the risk of their trainees’ musculoskeletal pain, after controlling for potential confounders. Similarly, the trainees trained by trainers with lower experience had more than two-time the risk of having pain in different sites (IRR: 2.04, 95% CI: 1.50–2.79). The trainers’ experience may play a pivotal role in the trainees’ musculoskeletal pain. Further study is warranted in this regard.

Electroencephalography (EEG), despite its inherited complexity, is a preferable brain signal for automatic human emotion recognition (ER), which is a challenging machine learning task with emerging applications. In any automatic ER, machine learning (ML) models classify emotions using the extracted features from the EEG signals, and therefore, such feature extraction is a crucial part of ER process. Recently, EEG channel connectivity features have been widely used in ER, where Pearson correlation coefficient (PCC), mutual information (MI), phase-locking value (PLV), and transfer entropy (TE) are well-known methods for connectivity feature map (CFM) construction. CFMs are typically formed in a two-dimensional configuration using the signals from two EEG channels, and such two-dimensional CFMs are usually symmetric and hold redundant information. This study proposes the construction of a more informative CFM that can lead to better ER. Specifically, the proposed innovative technique intelligently combines CFMs' measures of two different individual methods, and its outcomes are more informative as a fused CFM. Such CFM fusion does not incur additional computational costs in training the ML model. In this study, fused CFMs are constructed by combining every pair of methods from PCC, PLV, MI, and TE; and the resulting fused CFMs PCC + PLV, PCC + MI, PCC + TE, PLV + MI, PLV + TE, and MI + TE are used to classify emotion by convolutional neural network. Rigorous experiments on the DEAP benchmark EEG dataset show that the proposed CFMs deliver better ER performances than CFM with a single connectivity method (e.g., PCC). At a glance, PLV + MI-based ER is shown to be the most promising one as it outperforms the other methods. [ABSTRACT FROM AUTHOR]

This conference report describes the training activities that took place in the frame of the Integrated in Situ Chemical MApping probe (SCHeMA) summer school organized from the 14th to the 16th of June 2016 in Bilbao (Spain).

Background: A consensus is developing on interventions to improve newborn survival, but little is known about how to reduce socioeconomic inequalities in newborn mortality in low- and middle-income countries. Participatory learning and action (PLA) through women's groups can improve newborn survival and home care practices equitably across socioeconomic strata, as shown in cluster randomised controlled trials. We conducted a qualitative study to understand the mechanisms that led to the equitable impact of the PLA approach across socioeconomic strata in four trial sites in India, Nepal, Bangladesh, and Malawi. Methods: We conducted 42 focus group discussions (FGDs) with women who had attended groups and women who had not attended, in poor and better-off communities. We also interviewed six better-off women and nine poor women who had delivered babies during the trials and had demonstrated recommended behaviours. We conducted 12 key informant interviews and five FGDs with women's group facilitators and fieldworkers. Results: Women's groups addressed a knowledge deficit in poor and better-off women. Women were engaged through visual learning and participatory tools, and learned from the facilitator and each other. Facilitators enabled inclusion of all socioeconomic strata, ensuring that strategies were low-cost and that discussions and advice were relevant. Groups provided a social support network that addressed some financial barriers to care and gave women the confidence to promote behaviour change. Information was disseminated through home visits and other strategies. The social process of learning and action, which led to increased knowledge, confidence to act, and acceptability of recommended practices, was key to ensuring behaviour change across social strata. These equitable effects were enabled by the accessibility, relevance, and engaging format of the intervention. Conclusions: Participatory learning and action led to increased knowledge, confidence to act, and acceptability of recommended practices. The equitable behavioural effects were facilitated by the accessibility, relevance, and engaging format of the intervention across socioeconomic groups, and by reaching-out to parts of the population usually not accessed. A PLA approach improved health behaviours across socioeconomic strata in rural communities, around issues for which there was a knowledge deficit and where simple changes could be made at home. [ABSTRACT FROM AUTHOR]