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.

Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach.

The outbreak of COVID-19 is a global problem today, and, to reduce infectious cases and increase recovered cases, it is relevant to estimate the future movement and pattern of the disease. To identify the hotspot for COVID-19 in Bangladesh, we performed a cluster analysis based on the hierarchical k-means approach. A well-known epidemiological model named "susceptible-infectious-recovered (SIR)" and an additive regression model named "Facebook PROPHET Procedure" were used to predict the future direction of COVID-19 using data from IEDCR. Here we compare the results of the optimized SIR model and a well-known machine learning algorithm (PROPHET algorithm) for the forecasting trend of the COVID-19 pandemic. The result of the cluster analysis demonstrates that Dhaka city is now a hotspot for the COVID-19 pandemic. The basic reproduction ratio value was 2.1, which indicates that the infection rate would be greater than the recovery rate. In terms of the SIR model, the result showed that the virus might be slightly under control only after August 2022. Furthermore, the PROPHET algorithm observed an altered result from SIR, implying that all confirmed, death, and recovered cases in Bangladesh are increasing on a daily basis. As a result, it appears that the PROPHET algorithm is appropriate for pandemic data with a growing trend. Based on the findings, the study recommended that the pandemic is not under control and ensured that if Bangladesh continues the current pattern of infectious rate, the spread of the pandemic in Bangladesh next year will increase.

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.

Performance Evaluation of Machine Learning Algorithm for Classification of Unintended Pregnancy among Married Women in Bangladesh.

Intended pregnancy is one of the significant indicators of women's well-being. Globally, 74 million women become pregnant every year without planning. Unintended pregnancies account for 28% of all pregnancies among married women in Bangladesh. This study aimed to investigate the performance of six different machine learning (ML) algorithms applied to predict unintended pregnancies among married women in Bangladesh. From BDHS 2017-18, only 1129 pregnant women aged 15-49 were eligible for this study. An independent χ 2 test had performed before we considered six popular ML algorithms, such as logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), naïve Bayes (NB), and elastic net regression (ENR) to predict the unintended pregnancy. Accuracy, sensitivity, specificity, Cohen's Kappa statistic, and area under curve (AUC) value were used as model evaluation. The bivariate analysis result showed that women aged 30-49 years, poor, not educated, and living in male-headed households had a higher percentage of unintended pregnancy. We found various performance parameters for the classification of unintended pregnancy: LR accuracy = 79.29%, LR AUC = 72.12%; RF accuracy = 77.81%, RF AUC = 72.17%; SVM accuracy = 76.92%, SVM AUC = 70.90%; KNN accuracy = 77.22%, KNN AUC = 70.27%; NB accuracy = 78%, NB AUC = 73.06%; and ENR accuracy = 77.51%, ENR AUC = 74.67%. Based on the AUC value, we can conclude that of all the ML algorithms we investigated, the ENR algorithm provides the most accurate classification for predicting unwanted pregnancy among Bangladeshi women. Our findings contribute to a better understanding of how to categorize pregnancy intentions among Bangladeshi women. As a result, the government can initiate an effective campaign to raise contraception awareness.

Regional variations of child development index in Bangladesh.

Early development is a vital phase in childhood life. The study aimed to identify factors that were associated with the early development of 36-59 months children in Bangladesh. The findings of this study will formulate the design of appropriate policy and programmed responses. Utilizing Multiple Indicator Cluster Survey data, influencing components of child development status were evaluated for both rural and urban areas of Bangladesh. A total of 23,099 children under the age of five were included in this analysis. Chi-square analysis was conducted to assess the association between outcome variables and selected covariates. At the same time, this study uses two separate multivariate binary logistics regression models (respectively for urban areas and rural areas) to determine the risk factors that are primarily related to child development. Our research estimates that more than 70 percent of children develop early throughout the country. The multivariate analysis on the determinants of child development index among children aged between 36 and 59 months old regarding residence discovered a significant impact on child age and sex, maternal education, child education, wealth status, reading children's books. The adjusted odds of child nutrition status, playthings, and maternal functional difficulties have had a major impact on early child development in rural Bangladesh. Based on the findings, educational status, nutritional status, wealth-status, and some determinants of children care the most noteworthy findings in this study. Hence, policymakers should emphasize on such factors for improving children's development in residence.