Research Article
Analysis of Socio-demographic Determinants of Maternal Death in Dekina Local Government Area of Kogi State, Nigeria
Haruna Sheidu Abdulkarim*,
Abiola Ebenezer Damilola,
Dangana-Onuche Gloria Ojonoka,
Ibrahim Yusuf Baba,
Omede Enebi Israel,
Audu Mohammed,
Salifu Akoji Israel,
Akpata Oremeyi Grace,
Musa Aboda Bilkisu,
Isah Muniretu Madewo
Issue:
Volume 11, Issue 2, June 2025
Pages:
26-35
Received:
27 March 2025
Accepted:
19 May 2025
Published:
6 June 2025
DOI:
10.11648/j.ash.20251102.11
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Views:
Abstract: Maternal death constitute a major challenge confronting the entire world. It continue to be one of the world's most serious public health issues, especially in low- and middle-income nations like Nigeria and many Sub-Saharan African nations. This study look into the socio-demographic factors determining maternal deaths in Dekina Local Government Area, Kogi State, Nigeria. The investigation was conducted using the Three Delay Model of maternal mortality (3DM) as s theoretical foundation. 384 respondents were asked to complete copies of the questionnaire using a multi-stage sampling technique. A combination of approaches was used in the investigation. This means that it blends the quantitative (using a self-administered structured questionnaire with open-and closed-ended questions as its instrument) and qualitative (using in-depth interviews) methods of data collection. The data were analyzed using the percentages and frequency distribution tables. The study discovered that maternal death in the study is high due to factors that are socio-demographic in nature, which include; maternal age, marital status, religious belief, employment status, income level, occupation, parity, cultural belief, educational qualification, place of residence/geographical location, and healthcare accessibility. The study further found the improvement in access to skilled birth attendants, improving community education/awareness, improving antennal and post-natal care, family planning/child spacing practice, capacity building for healthcare workers, improving healthcare infrastructure, improving nutrition and anemia, tackling of gender inequality, and addressing socio-cultural barriers as strategic measures for reducing maternal death in Dekina Local Government Area.
Abstract: Maternal death constitute a major challenge confronting the entire world. It continue to be one of the world's most serious public health issues, especially in low- and middle-income nations like Nigeria and many Sub-Saharan African nations. This study look into the socio-demographic factors determining maternal deaths in Dekina Local Government Ar...
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Research Article
Artificial Intelligence Reshapes Drug Development: Technological Breakthroughs, Challenges, and Future Pathways
Issue:
Volume 11, Issue 2, June 2025
Pages:
36-41
Received:
30 April 2025
Accepted:
13 May 2025
Published:
16 June 2025
DOI:
10.11648/j.ash.20251102.12
Downloads:
Views:
Abstract: Artificial Intelligence (AI) is revolutionizing the drug development pipeline, significantly improving research and development (R&D) efficiency and success rates. AI's innovative applications span target identification, virtual screening, data integration, and molecular design. By utilizing advanced technologies such as deep learning, graph neural networks, and multimodal learning, AI facilitates the identification of disease targets, prediction of molecular binding modes, and integration of multi-omics data to construct dynamic models. Notable examples include AlphaFold-Multimer for protein structure prediction and Deep Docking for molecular docking. Despite these remarkable advancements, several formidable challenges persist and hinder the widespread adoption of AI in drug development. These include the "black-box" nature of AI models, inconsistent data quality, limited simulation of dynamic biological environments, and fragmented interdisciplinary knowledge. To overcome these obstacles, future developments should focus on three key areas: enhancing model interpretability through the strategic integration of physicochemical constraints, optimizing data sharing via the utilization of federated learning and differential privacy techniques, and constructing highly dynamic prediction frameworks by incorporating molecular dynamics simulations. With continued interdisciplinary collaboration and continuous technological innovations, AI holds the immense potential to reshape drug development, driving the progress of precision medicine, reducing R&D costs, and offering new approaches to addressing complex diseases.
Abstract: Artificial Intelligence (AI) is revolutionizing the drug development pipeline, significantly improving research and development (R&D) efficiency and success rates. AI's innovative applications span target identification, virtual screening, data integration, and molecular design. By utilizing advanced technologies such as deep learning, graph neural...
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