Saudi Cultural Missions Theses & Dissertations
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Item Restricted Oral biofilm and host-pathogen models: a semi- systematic review and future perspectives(University Of Glasgow, 2024-08) Alshehri, Khalid; Brown, JasonAbstract Introduction: Oral biofilms, complex microbial communities found on various surfaces within the oral cavity, play a critical role in the development and progression of oral diseases such as dental caries, periodontal diseases, and mucosal infections. Understanding the formation, structure, and pathogenicity of these biofilms is essential for improving prevention and treatment strategies. Aims: This review aims to evaluate recent advancements in the development and application of in vitro multi-species oral biofilm models, with a focus on studies published between January 2019 and July 2024. The review seeks to identify gaps in current research and suggest future directions for enhancing the physiological relevance of these models. Methods: A systematic literature search was conducted in the PubMed database, following PRISMA guidelines. Studies were selected based on predefined inclusion and exclusion criteria, focusing on multi-species biofilm models in vitro. The review analyzed methodologies, findings, and limitations of the selected studies. Findings: The review identified six key studies employing various in vitro models, ranging from continuous flow systems to static models. These studies highlighted the importance of specific microbial interactions, biofilm maturation processes, and the impact of different substrates on biofilm formation. However, limitations were noted in replicating the complexity of the in vivo oral environment, particularly in capturing the dynamic conditions and microbial diversity. Discussion: While significant progress has been made in the development of in vitro biofilm models, challenges remain in creating systems that accurately mimic the oral microenvironment. Advances in microfluidic devices and 'OMICs' technologies offer promising avenues for future research. Additionally, there is a need for long-term studies that better reflect the chronic nature of biofilm-related infections. Conclusion: The development of in vitro models that closely replicate the in vivo conditions of the oral cavity is crucial for advancing our understanding of oral biofilms and their role in disease progression. Future research should focus on integrating advanced technologies and improving model complexity to enhance the predictive value of these systems for clinical applications.14 0Item Restricted Analysing Aspects of General Health in Scotland(Saudi Digital Library, 2023-08-30) Alshehri, Khalid; Miller, BarryThe Scottish Health Survey (SHS), recognised as National Statistics, provides an invaluable data source for understanding health trends and disparities in Scotland (Scottish Government, 2023). Meanwhile, the principles of machine learning, a facet of artificial intelligence grounded in mathematics, statistics, and computer science, offered us the tools to analyse these large datasets effectively. In this study, we set out to identify factors correlated to the general health of the Scottish population by using Cramér's V and to predict the general health within the 2021 data based on these factors from 2019 and 2020. Through our analysis, we found that a wide range of factors, from Longstanding illnesses and Lifestyle behaviours to Socioeconomic status, Cardiovascular Disease and Diabetes, Asthma, Adult physical activity, Height and weight and Education, had varying degrees of correlation with general health. Three different machine learning models were trained and tested for each year in this study. Our research revealed that the Logistic Regression model, with an accuracy of 60%, performed optimally in predicting the general health of the population for the year 2021, with data from 2019 proving to be more efficacious in prediction. This study has a few limitations that could have influenced the findings. Firstly, the 2020 dataset had fewer cases in specific health categories, which could have affected the prediction accuracy for 2021. Secondly, a significant overlap was observed between Good and Very Good health cases, which may be due to respondents' subjective evaluations of their general health. Expanding the research to include data from other years could provide a more comprehensive view of health trends over time. Also, investigating alternative machine learning models might offer opportunities to improve prediction accuracy. Lastly, conducting similar studies in different geographical areas could yield valuable comparisons and insights.13 0