Identification of natural antiviral drug candidates against Tilapia Lake Virus: Computational drug design approaches.

Tilapia Lake Virus (TiLV) is a disease that affects tilapia fish, causing a high rate of sudden death at any stage in their life cycle. Unfortunately, there are currently no effective antiviral drugs or vaccines to prevent or control the progression of this disease. Researchers have discovered that the CRM1 protein plays a critical function in the development and spreading of animal viruses. By inhibiting CRM1, the virus's spread in commercial fish farms can be suppressed. With this in mind, this study intended to identify potential antiviral drugs from two different tropical mangrove plants from tropical regions: Heritiera fomes and Ceriops candolleana. To identify promising compounds that target the CRM1 protein, a computer-aided drug discovery approach is employed containing molecular docking, ADME (absorption, distribution, metabolism and excretion) analysis, toxicity assessment as well as molecular dynamics (MD) simulation. To estimate binding affinities of all phytochemicals, molecular docking is used and the top three candidate compounds with the highest docking scores were selected, which are CID107876 (-8.3 Kcal/mol), CID12795736 (-8.2 Kcal/mol), and CID12303662 (-7.9 Kcal/mol). We also evaluated the ADME and toxicity properties of these compounds. Finally, MD simulation was conducted to analyze the stability of the protein-ligand complex structures and confirm the suitability of these compounds. The computational study demonstrated that the phytochemicals found in H. fomes and C. candolleana could potentially serve as important inhibitors of TiLV, offering practical utility. However, further in vivo investigations are necessary to investigate and potentially confirm the effectiveness of these compounds as antiviral drugs against the virus TiLV.

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.

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process.

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

Determinants of the Early Childhood Development Index among children aged < 5 years in Bangladesh, Costa Rica and Ghana: a comparative study.

BACKGROUND: Early child development is a crucial factor for children that controls health and well-being in later life. AIMS: To determine the influence of sociodemographic factors on the Early Child Development Index (ECDI) among children aged < 5 years. METHODS: The analysis was performed using cross-sectional survey data from 2019, 2017-2018 and 2018 Multiple Indicator Cluster Surveys from Bangladesh, Ghana and Costa Rica, respectively. We used the χ2 test for bivariate analysis and binary logistic regression model for multivariate analysis for all 3 countries. All the statistical analyses were performed with IBM SPSS version 25 and R version 4.0.0. RESULTS: Child age and sex, followed by maternal education level, economic status, child nutritional status, reading children's books, and maternal functional difficulties had the greatest effect on ECDI. Children aged 36-47 months had lower odds of development than those aged 48-59 months, and boys had lower odds of development than girls in Bangladesh, Costa Rica and Ghana. Urban children had lower odds of development than rural children in Costa Rica but higher odds in Ghana. CONCLUSION: We recommend that governments should take the necessary steps to enhance children's early development and well-being in all 3 countries by raising education, improving economic conditions and providing balanced nutrition.