Publications by authors named "E F Bamidele"

Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design.

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Background: Musculoskeletal symptoms in welders may affect their health and quality of life. This study determined the prevalence of musculoskeletal symptoms among welders compared to non-welders and determined the body part(s) mostly affected.

Methodology: The study is a comparative cross-sectional study.

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Chronic wounds have been a global health threat over the past few decades, requiring urgent medical and research attention. The factors delaying the wound-healing process include obesity, stress, microbial infection, aging, edema, inadequate nutrition, poor oxygenation, diabetes, and implant complications. Biomaterials are being developed and fabricated to accelerate the healing of chronic wounds, including hydrogels, nanofibrous, composite, foam, spongy, bilayered, and trilayered scaffolds.

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Introduction: HIV, hepatitis B and hepatitis C pose a public health challenge in sub-Saharan Africa and there are only few studies on co-infection of these viruses done in rural areas in Northern Nigeria. This study provides a rural perspective on HIV-hepatitis co-infection in a Northern Nigerian community.

Methods: this cross-sectional study was carried out amongst people living with HIV/AIDS (PLWHA) in a rural community hospital over a three-month period.

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An open multicentre clinical trial of inhaled B-agonist, salmeterol, was carried out to assess its efficacy in adult Nigerian asthmatic patients. Seventy-four patients were recruited to the trial and 64 (86.48%) completed the trials.

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