Evaluation of In Vitro Antimicrobial, Cytotoxic, Thrombolytic, and Antiarthritic Property of Different Parts of Bari Orchid.

Many different herbal extracts have historically been utilized to treat microbe-induced infections, injuries, cancer, thrombosis, and arthritis. The purpose of this study was to determine the antibacterial, cytotoxic, in vitro thrombolytic, and in vitro antiarthritic properties of ethanolic extracts of stem and seed of Bari orchid 1 (BO) plant. This orchid plant was developed by the Bangladesh Agriculture Research Institute (BARI) in Gazipur. Fourteen microbes were employed in the antimicrobial investigation, and samples of orchids were compared to ciprofloxacin as a reference. The BO/seed extract was found to possess more antibacterial activity. The lethality test of brine shrimps was used to assess the LC50 values. The BO/stem extract exhibited a higher cytotoxicity potential, in comparison to the BO/seed extract. Two concentrations (1000 and 100 ppm) and two incubation times (24 hours and 1.5 hours) were used to assess the thrombolytic activity of the extracts. Regarding the thrombolytic effect, the BO/stem extract has demonstrated greater promise. Furthermore, the herbal extract's antiarthritic activity was investigated at four different concentrations, and the results were evaluated in comparison with those of diclofenac sodium. When comparing BO/stem extract to other extracts, the greatest values for protein denaturation were obtained.

Examining the decline in modern contraception usage among married women in Bangladesh: Applying Blinder-Oaxaca decomposition analysis.

OBJECTIVES: Controlling population expansion and reducing unintended pregnancies through the use of modern contraceptives is a cost-effective strategy. In recent years, the rate of modern contraceptive use in Bangladesh has been declining. So, this study aimed to investigate the associated factors of the deterioration in modern contraceptive usage. METHODS: This study used data from two successive Bangladesh Demographic and Health Surveys (2014 and 2017-18) and applied the Blinder-Oaxaca decomposition analysis to understand the drivers. A popular binary logistic regression model is fitted to determine the factors that influence the use of modern contraceptive methods over the years. RESULTS: This study revealed that highly educated women were more likely to use modern contraception methods, and their use increased by 3 percent over the years. Factors such as women's working status, husband's education, number of living children, and fertility preference were found significantly associated with decreased usage of modern contraception methods over years. The result of the Blinder-Oaxaca (BO) decomposition analysis found a significant decrease between 2014 and 2018. Respondent's age, working status, husband's age, opinion on decision making, region, and media exposure were the most significant contributors to explaining the shift between 2014 and 2018. The two factors that contributed most to narrowing the difference between the two surveys were women's decision on own health (26%), and employment status (35%). CONCLUSIONS: The factors that influence modern contraceptive prevalence are important to know for policy implication purposes in Bangladesh. The findings indicate the need for further improvement of factors for balancing the usage of modern contraception methods.

Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh.

Technological advancements have long played crucial roles in rice productivity and food security in Bangladesh. Seasonal variation over time and regional differences in rice production, however, pose a threat to agricultural sustainability but remain unexplored. We performed a spatial-temporal mapping of rice cultivation area, production, and yield from 2006-2007 to 2019-2020 using secondary data for disaggregating 64 districts in Bangladesh. Growth and multivariate approaches were employed to analyze time-series data. Results showed that Mymensingh had the highest rice cultivated area and production, while Bandarban had the lowest. The 14 years highest average rice yield was found in Gopalganj and Dhaka (3.63 tons/ha), while Patuakhali (1.73 tons/ha) had the lowest. For the Aus, Aman, and Boro, the rice cultivation area in 19 districts, 11 districts, and 13 districts declined significantly. The overall rice production increased significantly in most districts. For the Aus, Aman, and Boro seasons, the rice yield in 54, 50, and 37 districts demonstrated a significant upward trend, respectively. The adoption rate of modern varieties has risen dramatically. However, there are notable variances between regions and seasons. A significant increasing trend in Aus (0.007% to 0.521%), Aman (0.004% to 0.039%), and Boro (0.013% to 0.584%) were observed in 28, 34, and 36 districts, respectively, with an increase of 1% adaptation of HYV. Predictions revealed that rice cultivation area and production of Aus, Aman, and Boro seasons will be increased in most of the regions of Bangladesh by 2030. Based on spatiotemporal cluster analysis, the five identified cluster groupings illustrated that clusters lack spatial cohesion and vary greatly seasonally. This suggests increasing rice production by expanding cultivable land, adopting high-yielding varieties, and integrating faster technological advancement in research and extension. The findings will assist scientists in developing region-specific production technologies and policymakers in designing decentral region-specific policies to ensure the future sustainability of rice production.

Survival analysis of early intention of antenatal care among women in Bangladesh.

This study focuses on the importance of early and regular Antenatal Care (ANC) visits in reducing maternal and child mortality rates in Bangladesh, a country where such health indicators are a concern. The research utilized data from the Bangladesh Demographic and Health Survey (BDHS) conducted in 2017-18 and employed the Cox proportional hazard model to identify factors influencing women's intention of ANC services. The results revealed that 40.4% of women engaged in at least one ANC activity during the first trimester, which, although higher than in other countries, falls below the global average. Notably, women between the aged of 25 and 29 years took 15% less time for their first ANC visit compared to their younger counterparts, suggesting higher awareness and preparedness in this age group. Education, both for women and their partners, had a significant influence on the intention to visit ANC early. Women in the poor wealth quantile exhibited lower odds of seeking timely ANC, whereas those with a planned pregnancy were more likely to do so. Moreover, access to mass media decreased the timing of ANC visits by 26% compared to women who were not exposed. Moreover, living in rural areas was linked to a 17% delay in the timing of the first ANC visit compared to urban areas. These findings underscore the importance of addressing these determinants to improve the timeliness and accessibility of ANC services, thereby enhancing maternal and child health outcomes in Bangladesh.

Individual- and community-level factors associated with the presence of adequate iodized salt in households in Bangladesh: a multilevel modelling approach.

BACKGROUND: The aim of this study is to estimate the factors at both the individual and community levels related to the adequacy of iodized salt in households in Bangladesh. METHODS: For this study we utilized the 2019 Multiple Indicator Cluster Survey data. A total of 61 242 households were chosen as samples from Bangladesh. In our study of socio-economic disparities, we applied a concentration indexing method. To identify the factors associated with the adequacy of iodine in salt at both the individual and community levels, we employed multilevel logistic regression. Aside from the multilevel regression used in the study, we also applied spatial analysis. RESULTS: The results indicated that the prevalence of iodine adequacy in household salt was found to be 57.8% (95% confidence interval 57.4 to 58.2). Rural areas have a higher concentration of iodine than urban areas. According to the multilevel model, younger women (adjusted odds ratio [aOR] 0.70), Muslim women (aOR 0.89), illiterate women (aOR 0.80) and those from poor households (aOR 0.33) were found to be less likely to consume iodine in concentrated salt compared with their counterparts. Disabled women and those with low media exposure have a lower likelihood of iodine adequacy in salt compared to their reference group. Furthermore, households in urban areas exhibited higher odds of having iodine adequacy in salt compared with households in rural areas. Barisal, Chattogram, Dhaka, Khulna, Mymensingh, Rajshahi and Rangpur Divisions have lower iodine adequacy in salt compared with Sylhet Division. CONCLUSIONS: The findings reveal that religion, physical disability and exposure to media exert an equal influence on the presence of iodized salt intake. Moreover, women's age, wealth status, education level and the educational background of the household head positively contribute to the adequacy of iodine in household salt. In light of these results, policymakers are advised to prioritize efforts aimed at enhancing iodine concentration, with a particular focus on mass media advertising, especially in rural areas (excluding Sylhet Division).

Determinants of household adoption of clean energy with its rural-urban disparities in Bangladesh.

This study aims to investigate factors influencing the adoption of clean energy among households in Bangladesh, using Blinder-Oaxaca decomposition and extended probit regression model with data from the 2019 Bangladesh multiple indicator cluster survey. Small households, primarily Muslim and urban dwellers, who speak the Bengali language and are Internet and mobile users, were likelier to adopt cleaner fuels than their counterparts. On the contrary, households residing in the Barisal, Khulna, Rajshahi, and Rangpur divisions, belonging to poor and middle-class households, with household heads aged 15-64 and without formal education, were less likely to adopt cleaner fuels than their counterparts. The concentration curve revealed socioeconomic inequality in the adoption of clean energy, particularly favouring richer households in urban and rural areas. Further analysis using the Blinder-Oaxaca decomposition showed that urban residents showed a higher probability of adopting clean energy, with a significant difference of 0.508 compared to rural areas. Regarding the endowment effect, poor wealth quintile contributed the most, followed by the ownership of rented dwellings and the middle wealth quintile. The Bengali differential effect made the largest contribution to this aspect of the disparity, followed by the exposure of the Internet and the influence of the Dhaka and Chattogram divisions. The detailed analysis provides valuable insights for policymakers and practitioners on the issue of disparities in the adoption of clean energy between urban and rural areas in Bangladesh.

Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm.

Droughts pose a severe environmental risk in countries that rely heavily on agriculture, resulting in heightened levels of concern regarding food security and livelihood enhancement. Bangladesh is highly susceptible to environmental hazards, with droughts further exacerbating the precarious situation for its 170 million inhabitants. Therefore, we are endeavouring to highlight the identification of the relative importance of climatic attributes and the estimation of the seasonal intensity and frequency of droughts in Bangladesh. With a period of forty years (1981-2020) of weather data, sophisticated machine learning (ML) methods were employed to classify 35 agroclimatic regions into dry or wet conditions using nine weather parameters, as determined by the Standardized Precipitation Evapotranspiration Index (SPEI). Out of 24 ML algorithms, the four best ML methods, ranger, bagEarth, support vector machine, and random forest (RF) have been identified for the prediction of multi-scale drought indices. The RF classifier and the Boruta algorithms shows that water balance, precipitation, maximum and minimum temperature have a higher influence on drought intensity and occurrence across Bangladesh. The trend of spatio-temporal analysis indicates, drought intensity has decreased over time, but return time has increased. There was significant variation in changing the spatial nature of drought intensity. Spatially, the drought intensity shifted from the northern to central and southern zones of Bangladesh, which had an adverse impact on crop production and the livelihood of rural and urban households. So, this precise study has important implications for the understanding of drought prediction and how to best mitigate its impacts. Additionally, the study emphasizes the need for better collaboration between relevant stakeholders, such as policymakers, researchers, communities, and local actors, to develop effective adaptation strategies and increase monitoring of weather conditions for the meticulous management of droughts in Bangladesh.

Rural-urban disparities in nutritional status among ever-married women in Bangladesh: A Blinder-Oaxaca decomposition approach.

This study aims to investigate socioeconomic disparities in nutritional status among ever-married women in Bangladesh and to break down urban-rural differences in the underlying causes of undernutrition. We utilized data from the Bangladesh Demographic and Health Survey 2017-18, a sample size of 18328 ever-married women, including 5170 from urban residences, and 13159 from rural residences. To explore socioeconomic inequality, we employed a concentration indexing measure, while a multiple binary logistic regression model was carried out to identify the determinants associated with the outcome variable. A Blinder-Oaxaca decomposition analysis was performed to decompose the urban-rural gap in women's nutritional status using associated factors. The prevalence of undernutrition among ever-married women in Bangladesh was 12 percent. Notably, this percentage varied by region, with urban residents accounting for 8.6% and rural residents accounting for 13.3%. Our findings confirmed that undernutrition was more prevalent among women with lower wealth indexes in Bangladesh, as indicated by the concentration index (CIX = -0.26). The multivariable analysis investigating the determinants of undernutrition status among ever-married women, with a focus on residence revealed significant associations with respondent age, education, marital status, mass media access, wealth status, and division. According to the Blinder-Oaxaca decomposition and its extension, the prevalence was significantly higher in rural residences of Bangladesh than in urban residences, and the endowment effect explained 86 percent of the total urban-rural difference in undernutrition prevalence. The results of this study indicate that the factors that influence women's nutritional status in rural areas play a significant role in the gap, and the majority of the gap is caused by education and economic position. In order to effectively promote maternal health policies in Bangladesh, intervention techniques should be created that are aimed at the population, that is, the poorest and least educated.

Application of a count regression model to identify the risk factors of under-five child morbidity in Bangladesh.

BACKGROUND: Bangladesh has seen a significant decline in child mortality in recent decades, but morbidity among children <5 y of age remains high. The aim of this analysis was to examine trends and identify risk factors related to child morbidity in Bangladesh. METHODS: This analysis is based on data from four successive cross-sectional Bangladesh Demographic and Health Surveys for the years 2007, 2011, 2014 and 2017-18. Several count regression models were fitted and the best model was used to identify risk factors associated with morbidity in children <5 y of age. RESULTS: According to the results of the trend analysis, the prevalence of non-symptomatic children increased and the prevalence of fever, diarrhoea and acute respiratory infections (ARIs) decreased over the years. The Vuong's non-nested test indicated that Poisson regression could be used as the best model. From the results of the Poisson regression model, child age, sex, underweight, wasted, stunting, maternal education, wealth status, religion and region were the important determinants associated with the risk of child morbidity. The risk was considerably higher among women with a primary education compared with women with a secondary or greater education in Bangladesh. CONCLUSIONS: This analysis concluded that child morbidity is still a major public health problem for Bangladesh. Thus it is important to take the necessary measures to reduce child morbidity (particularly fever, diarrhoea and ARI) by improving significant influencing factors.

Gender disparity in minimum dietary diversity failure among currently breastfed children aged 6-23 months in Bangladesh: evidence from Bangladesh Multiple Indicator Cluster Survey, 2019.

Research on children's dietary diversity plays a crucial role in designing effective health interventions. Thus, this study aimed to identify the factors contributing to minimum dietary diversity failure (MDDF) among male and female children aged 6-23 months in Bangladesh. The data for this study was obtained from the Bangladesh Multiple Indicator Cluster Survey, 2019, which included children currently breastfed within a specific age range. Multivariable binary logistic regression was employed to assess the strength and significance of the association. The findings revealed that approximately 59⋅4 % of children in Bangladesh experienced MDDF, with 57⋅8 % of male children and 61 % of female children affected. Proportion test uncovered a significant gender disparity (χ2=6⋅58, P-value = 0⋅01) among children aged 6-23 months. However, the multivariable binary logistic regression analysis revealed that both male and female children shared common risk factors for MDDF, which included child age, maternal educational status, wealth status, number of antenatal care visits, and division. In our study, we observed varied spatial patterns in minimal dietary diversity. Sherpur, Netrokona, Sunamganj, and Sylhet districts showed the highest failure rates. Notably, all are flood-affected areas, impacting food availability and diversity. For targeted regional development programmes, district mapping results may offer valuable insights to policymakers, especially in areas with a high prevalence of dietary diversity failure. By understanding these risk factors, policymakers and stakeholders can implement targeted strategies to improve dietary diversity among children, promoting better health and well-being for the young population in Bangladesh.

Inequalities in adequate maternal healthcare opportunities: evidence from Bangladesh Demographic and Health Survey 2017-2018.

OBJECTIVES: This study investigated the inequalities in access to maternal healthcare services in Bangladesh. DESIGN AND SETTING: This study used cross-sectional data from the nationally representative Bangladesh Demographic and Health Survey conducted in 2017-2018. The survey encompassed diverse regions and households across Bangladesh. The study used the Human Opportunity Index (HOI) and Shapley's decomposition technique to measure the inequality in access to maternal healthcare opportunities. PARTICIPANTS: This study included 20 127 women aged 15-49 years. Among them, 5012 women had live births in the preceding 3 years of the survey, forming the study sample. PRIMARY AND SECONDARY OUTCOME MEASURES: This study has no secondary outcome variable. The primary dependent variable is 'adequate maternal healthcare', a dichotomous variable. RESULTS: Household wealth status contributed the highest to inequality in accessing adequate maternal healthcare services (41.4%) such as receiving at least four antenatal care (ANC) visits (39.7%), access to proper ANC (50.7% and 44.0%) and health facility birth (43.4%). Maternal educational status contributes the second highest inequality among all factors in accessing adequate maternal healthcare (29.5%). Adequate maternal healthcare presented the lowest coverage rate and opportunity index among all (approximately 24% with HOI=17.2). CONCLUSIONS: We found that attained adequate maternal healthcare had the lowest coverage and widest dissimilarity, while wealth index, education and place of residence are the major factors that contribute to inequalities in accessibility to maternal healthcare services in Bangladesh. These findings underscore a need for pro-poor interventions to narrow the economic inequalities between the poor and rich in terms of accessibility to maternal healthcare services. The results indicate the need for the Bangladeshi government and its health department to strengthen their commitment to improving female education. Investments should be made in initiatives that facilitate the proximity of maternal healthcare services to women in rural areas.

Exploring genotypic variability and interrelationships among growth, yield, and quality characteristics in diverse tomato genotypes.

Tomato is the most consumed vegetable crop worldwide, with excellent beneficial health properties and high content of vitamins, minerals, carotenoids, total antioxidants, and phenolic compounds. Hence, improving its genotypes is crucial to sustain its production and ensure food security, principally under the fast-growing worldwide population and abrupt global climate change. The present study aimed to explore the genotypic variability associated with specific characteristics in twenty-five diverse tomato genotypes. In addition, the relationships between growth, yield, and quality traits using both univariate (correlation coefficient, path analysis) and multivariate (principal component, principal coordinates, canonical variate) analysis methods were explored. The results indicated that the evaluated genotypes possessed highly significant variation. This is appropriate for future hybridization through tomato breeding programs. All evaluated genotypes demonstrated considerable potential to develop strong hybrid vigour for growth, yield, and quality characteristics. In particular, the genotypes LS009, LS011, and LS014 could be considered promising, high-yielding, and resistant to yellow leaf curl virus infestation (YLCV) disease parents for future breeding schemes. The number of fruits per plant, fruit diameter, and fruit weight proved strong positive relationships with fruit yield. Accordingly, these characteristics demonstrate their importance in improving fruit yield and could be exploited as indirect criteria for selecting high-yielding tomato genotypes through breeding programs.

Pharmacological effect of Heritiera fomes on Long Evans rats against Postprandial Hyperglycemia and adsorption in vitro.

Background: Natural restoratives from traditional medicinal plants are considered to be a convenient, potent, and risk-free substitute treatment for hyperglycaemia. Our objective was to explore the activity of the crude extract of Heritiera fomes on postprandial hyperglycaemia by assessing relative measurements in a laboratory animal model. Methods: The Streptozotocin induced diabetic rat (n = 88, twenty-two per group) was used for the glucose tolerance test as an initial support for the study. BaSO4 was administered orally as a marker to measure gut motility after one hour of methanolic extract (500 mg/kg body weight) administration where, only purified water (10 ml/kg) was used to treat the control group (n = 12) and a dose (500 mg/kg) of H. fomes extract was used for the test group (n = 12 in each group). After 60 min of incubation of the mixture of extract and glucose with 10% (v/v) yeast cell suspension, the absorbance was measured to determine the capacity of glucose absorption by yeast cells. Sixty Long Evans rats (n = 12 in each group) were used to assess disaccharidase enzyme activity as µmol/mg protein per hour by Lowry's protein estimation method. The carbohydrate absorption investigation was executed to evaluate the leftover sucrose content in the gastrointestinal system (n = 64). Results: After oral administration of MHFL (71.84%), MHFB (71.41%), and MHFR (72.55%), GI motility (%) increased significantly (p < 0.001) compared to the control group (59.06%). A significant increase in glucose uptake and adsorption capacity measured by different concentrations of glucose ensures the decrease of glucose bound rate and a significant drop in blood glucose concentration. The significant (p < 0.001) decrease in intestinal disaccharidase activity of MHFL (1.40), MHFB (1.36), and MHFR (1.20) in comparison to the control group (1.50) indicates that the presence of H. fomes may reduce glucose absorption in the small bowel. Significant (p < 0.001 & p < 0.05) accumulation of sucrose content in the six different parts of the GI tract suggests the absorption of sucrose was decreased. Conclusions: The findings of this study provide evidence on probable mechanisms for the anti-diabetic characteristics of H. fomes, and it is predicted that this plant will be studied further for the development of strong anti-hyperglycemic medicines.

Mitigating carbon footprint for knowledge distillation based deep learning model compression.

Deep learning techniques have recently demonstrated remarkable success in numerous domains. Typically, the success of these deep learning models is measured in terms of performance metrics such as accuracy and mean average precision (mAP). Generally, a model's high performance is highly valued, but it frequently comes at the expense of substantial energy costs and carbon footprint emissions during the model building step. Massive emission of CO2 has a deleterious impact on life on earth in general and is a serious ethical concern that is largely ignored in deep learning research. In this article, we mainly focus on environmental costs and the means of mitigating carbon footprints in deep learning models, with a particular focus on models created using knowledge distillation (KD). Deep learning models typically contain a large number of parameters, resulting in a 'heavy' model. A heavy model scores high on performance metrics but is incompatible with mobile and edge computing devices. Model compression techniques such as knowledge distillation enable the creation of lightweight, deployable models for these low-resource devices. KD generates lighter models and typically performs with slightly less accuracy than the heavier teacher model (model accuracy by the teacher model on CIFAR 10, CIFAR 100, and TinyImageNet is 95.04%, 76.03%, and 63.39%; model accuracy by KD is 91.78%, 69.7%, and 60.49%). Although the distillation process makes models deployable on low-resource devices, they were found to consume an exorbitant amount of energy and have a substantial carbon footprint (15.8, 17.9, and 13.5 times more carbon compared to the corresponding teacher model). The enormous environmental cost is primarily attributable to the tuning of the hyperparameter, Temperature (τ). In this article, we propose measuring the environmental costs of deep learning work (in terms of GFLOPS in millions, energy consumption in kWh, and CO2 equivalent in grams). In order to create lightweight models with low environmental costs, we propose a straightforward yet effective method for selecting a hyperparameter (τ) using a stochastic approach for each training batch fed into the models. We applied knowledge distillation (including its data-free variant) to problems involving image classification and object detection. To evaluate the robustness of our method, we ran experiments on various datasets (CIFAR 10, CIFAR 100, Tiny ImageNet, and PASCAL VOC) and models (ResNet18, MobileNetV2, Wrn-40-2). Our novel approach reduces the environmental costs by a large margin by eliminating the requirement of expensive hyperparameter tuning without sacrificing performance. Empirical results on the CIFAR 10 dataset show that the stochastic technique achieves an accuracy of 91.67%, whereas tuning achieves an accuracy of 91.78%-however, the stochastic approach reduces the energy consumption and CO2 equivalent each by a factor of 19. Similar results have been obtained with CIFAR 100 and TinyImageNet dataset. This pattern is also observed in object detection classification on the PASCAL VOC dataset, where the tuning technique performs similarly to the stochastic technique, with a difference of 0.03% mAP favoring the stochastic technique while reducing the energy consumptions and CO2 emission each by a factor of 18.5.

Double burden of malnutrition among women of reproductive age in Bangladesh: A comparative study of classical and Bayesian logistic regression approach.

Although the prevalence of undernutrition among women of reproductive age has declined in Bangladesh, the increase in the prevalence of overnutrition remains a major challenge. To achieve Sustainable Development Goal 2.2, it is important to identify the drivers of the double burden of malnutrition on women in Bangladesh. The Bangladesh Demographic and Health Survey, 2017-2018 was used to model the relationship between the double burden of malnutrition among women and the risk factors using a logistic regression model under the classical and Bayesian frameworks and performed the comparison between the regression models based on the narrowest confidence interval. Regarding the Bayesian application, the Metropolis-Hastings algorithm with two types of prior information (historical and noninformative prior) was used to simulate parameter estimates from the posterior distributions. The Boruta algorithm was used to determine the significant predictors. Almost half of reproductive aged women experienced a form of malnutrition (12% were underweight, 26.1% were overweight, and 6.8% were obese). In terms of the narrowest interval estimate, it was found that Bayesian logistic regression with informative priors performs better than the noninformative priors and the classical logistic regression model. Women who were older, highly educated, from rich families, unemployed, and from urban residences were more likely to experience the double burden of malnutrition. This study recommended using the historical prior as the informative prior rather than the flat/noninformative prior to estimating the parameter uncertainty if historical data are available. The double burden of malnutrition among women is a major public health challenge in Bangladesh. This study was to determine the impact of effective risk factors on the double burden of malnutrition among women by applying the Bayesian framework. Using both informative and noninformative priors, "historical prior" was proposed as informative prior information. The main strength is that the proposed prior (historical prior) provided improved estimation as compared to the flat prior distribution.

Knowledge, attitudes, and factors determining the willingness for COVID-19 vaccination among students in Bangladesh: An online-based cross-sectional study.

Aim: The most effective way to avoid COVID-19 is through immunization against the virus that causes the disease. The primary objective of this study was to assess the extent of knowledge, attitudes, acceptability, and factors influencing COVID-19 vaccination acceptance among higher secondary and university students in Bangladesh. Subject and methods: A structured questionnaire-based online survey was conducted among 451 students residing in Khulna and Gopalganj cities from February to August, 2022. The willingness to accept the COVID-19 vaccine was compared with a few covariates using the chi-square test, and we then used binary logistic regression to identify the determinants that led Bangladeshi students to receive the COVID-19 vaccine. Results: During the study period, almost 70% of the students obtained immunizations, with 56% of men and 44% of women reporting having done so. The age range of 26-30 years had the highest percentage of students who had received vaccinations, and 83.9% of students agreed that the COVID-19 vaccine is crucial for students. The results of the binary logistic regression clearly show that gender, degree of education, and respondents' willingness, encouragement, and beliefs have a significant impact on students' eagerness to receive COVID-19 vaccination. Conclusion: The rising trend in vaccination status among Bangladeshi students is highlighted by this study. Additionally, our results eloquently demonstrate that vaccination status varies by gender, education level, willingness, encouragement, and respondent's viewpoint. The outcomes of this study are essential for health policy makers and other interested parties to successfully organize their immunization program for young adults and children at various levels.

4-Aminoquinolines modulate RNA structure and function: Pharmacophore implications of a conformationally restricted polyamine.

RNA structure plays an important role in regulating cellular function and there is a significant emerging interest in targeting RNA for drug discovery. Here we report the identification of 4-aminoquinolines as modulators of RNA structure and function. Aminoquinolines have a broad range of pharmacological activities, but their specific mechanism of action is often not fully understood. Using electrophoretic mobility shift assays and enzymatic probing we identified 4-aminoquinolines that bind the stem-loop II motif (s2m) of SARS-CoV-2 RNA site-specifically and induce dimerization. Using fluorescence-based RNA binding and T-box riboswitch functional assays we identified that hydroxychloroquine binds the T-box riboswitch antiterminator RNA element and inhibits riboswitch function. Based on its structure and riboswitch dose-response activity we identified that the antagonist activity of hydroxychloroquine is consistent with it being a conformationally restricted analog of the polyamine spermidine. Given the known role that polyamines play in RNA function, the identification of an RNA binding ligand with the pharmacophore of a conformationally restricted polyamine has significant implications for further elucidation of RNA structure-function relationships and RNA-targeted drug discovery.

50 years of rice breeding in Bangladesh: genetic yield trends.

To assess the efficiency of genetic improvement programs, it is essential to assess the genetic trend in long-term data. The present study estimates the genetic trends for grain yield of rice varieties released between 1970 and 2020 by the Bangladesh Rice Research Institute. The yield of the varieties was assessed from 2001-2002 to 2020-2021 in multi-locations trials. In such a series of trials, yield may increase over time due to (i) genetic improvement (genetic trend) and (ii) improved management or favorable climate change (agronomic/non-genetic trend). In both the winter and monsoon seasons, we observed positive genetic and non-genetic trends. The annual genetic trend for grain yield in both winter and monsoon rice varieties was 0.01 t ha-1, while the non-genetic trend for both seasons was 0.02 t ha-1, corresponding to yearly genetic gains of 0.28% and 0.18% in winter and monsoon seasons, respectively. The overall percentage yield change from 1970 until 2020 for winter rice was 40.96%, of which 13.91% was genetic trend and 27.05% was non-genetic. For the monsoon season, the overall percentage change from 1973 until 2020 was 38.39%, of which genetic and non-genetic increases were 8.36% and 30.03%, respectively. Overall, the contribution of non-genetic trend is larger than genetic trend both for winter and monsoon seasons. These results suggest that limited progress has been made in improving yield in Bangladeshi rice breeding programs over the last 50 years. Breeding programs need to be modernized to deliver sufficient genetic gains in the future to sustain Bangladeshi food security.

Socioeconomic correlates of Adequate Maternal Care in Bangladesh: Analysis of the Bangladesh Demographic and Health Survey 2017-18.

Introduction: In recent times, Bangladesh has made significant improvements in various health outcomes, but not so much in maternal death. The current flat trend in reducing maternal mortality in Bangladesh has been mainly due to the lower coverage of maternal health care. To improve the coverage, it is essential to find biosocial factors related to adequate maternal health care. Therefore, this study is aimed at finding out the socioeconomic correlates of adequate maternal health care in Bangladesh. Methods: The study used data from the Bangladeshi demographic and health survey 2017-18. The total unweighted sample of 4012 women who reported pregnancy before three years of the survey was analyzed. A composite binary indicator of adequate maternal care has been constructed using the variables-access to maternal care service, four antenatal care visits, at least one visit with qualified providers, and institutional delivery. A binary logistic regression model was employed to find out the socioeconomic correlate of adequate maternal care. Results: Only 24.4% percent of sample women received adequate maternal care. The result of the logistic regression model shows that urban, Khulna, Rajshahi, and Rangpur were associated with an increase in the odds of having adequate maternal care. High education and health care decisions taken by the partner or husband were also associated with an increased odd of adequate maternal care. Islam and lower wealth status were associated with a lower probability of adequate maternal care. Conclusions: Policymakers and health administration should pay attention to the variation in the utilization of maternal health care across residence, region, religion, education, and wealth status to ensure safe motherhood.

Determinants and Trends of Health Facility Delivery in Bangladesh: A Hierarchical Modeling Approach.

Background: Most maternal deaths occur during childbirth and after childbirth. This study was aimed at determining the trends of health facilities during delivery in Bangladesh, as well as their influencing factors. Methods: This study used secondary data from three Bangladesh Multiple Indicator Cluster Surveys (MICSs) in 2006, 2012-13, and 2019. The study's target sample was those women who gave birth in the last two years of the survey. A two-level logistic regression was applied to determine the effects on health facility delivery separately in these two survey points (MICSs 2012-13 and 2019). Results: The results show that the delivery of health facilities has increased by almost 37.4% in Bangladesh, from 16% in 2006 to 53.4% in 2019. The results of two-level logistic regression show that the total variation in health facility delivery across the community has decreased over recent years. After adding community variables, various individual-level factors such as women with secondary education (OR = 0.55 in 2012-13 vs. OR =0.60 in 2019), women from middle wealth status (OR = 0.49 in 2012-13 vs. OR = 0.65 in 2019), religion, and child ever born showed a strong relationship with health facility delivery in both survey years. At the community level, residents showed significant association only in the 2012-13 survey and indicated a 43% (OR = 1.43 for 2012-13) greater availability of health facilities in urban residences than in rural residences. Using media showed a highly significant connection with health facility delivery in both years as well as an increasing trend over the years in Bangladesh (OR = 1.19 in 2012-13 vs. OR = 1.38 in 2019). However, division, prenatal care, and skilled services all contribute greatly to increasing the delivery of health facilities in Bangladesh. Conclusions: The results of this study suggest that policymakers need to pay attention to individual and community-level factors, especially women's education, poverty reduction, and adequate prenatal care provided by well-trained caregivers.