University of Manitoba Scholarship
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This collection contains open access research publications authored or co-authored by University of Manitoba researchers. Content within this collection includes pre and post-print versions of articles and book chapters, conference proceedings and technical reports. MSpace is where faculty and students can deposit their research output to meet the open access requirements of grant funding agencies and other related mandates. Deposit is subject to copyright compliance, distribution license and other license restrictions that may be imposed on the work.
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- ItemOpen AccessEarly childhood caries risk prediction using machine learning approaches in Bangladesh(BMC, 2025-01-08) Hasan, Fardous; Tantawi, Maha E.; Haque, Farzana; Foláyan, Moréniké O.; Virtanen, Jorma I.Abstract Background In the last years, artificial intelligence (AI) has contributed to improving healthcare including dentistry. The objective of this study was to develop a machine learning (ML) model for early childhood caries (ECC) prediction by identifying crucial health behaviours within mother-child pairs. Methods For the analysis, we utilized a representative sample of 724 mothers with children under six years in Bangladesh. The study utilized both clinical and survey data. ECC was assessed using ICDAS II criteria in the clinical examinations. Recursive Feature Elimination (RFE) and Random Forest (RF) was applied to identify the optimal subsets of features. Random forest classifier (RFC), extreme gradient boosting (XGBoost), support vector machine (SVM), adaptive boosting (AdaBoost), and multi-layer perceptron (MLP) models were used to identify the best fitted model as the predictor of ECC. SHAP and MDG-MDA plots were visualized for model interpretability and identify significant predictors. Results The RFC model identified 10 features as the most relevant for ECC prediction obtained by RFE feature selection method. The features were: plaque score, age of child, mother’s education, number of siblings, age of mother, consumption of sweet, tooth cleaning tools, child’s tooth brushing frequency, helping child brushing, and use of F-toothpaste. The final ML model achieved an AUC-ROC score (0.77), accuracy (0.72), sensitivity (0.80) and F1 score (0.73) in the test set. Of the prediction model, dental plaque was the strongest predictor of ECC (MDG: 0.08, MDA: 0.10). Conclusions Our final ML model, integrating 10 key features, has the potential to predict ECC effectively in children under five years. Additional research is needed for validation and optimization across various groups.
- ItemOpen AccessResidency training programs to support residents working in First Nations, Inuit, and Métis communities(BMC, 2025-01-28) Rashid, Marghalara; Nguyen, Julie; Clark, Wayne; Foulds, Jessica L.; John, Ida; Chan, Ming-Ka; Whalen-Browne, Molly; Roach, Pamela; Morris, Melanie; Forgie, SarahAbstract Background To gain culturally appropriate awareness of First Nations, Inuit and/or Métis Health, research suggests that programs focus on sending more trainees to First Nations, Inuit and/or Métis communities Working within this context provides experiences and knowledge that build upon classroom education and support trainees’ acquisition of skills to engage in culturally safe healthcare provision. This study examines residents' and faculty members' perceptions of how residency training programs can optimize First Nations, Inuit and/or Métis health training and support residents in gaining the knowledge, skills, and experiences for working in and with First Nations, Inuit and/or Métis communities. Methods A qualitative approach was used, guided by a relational lens for collecting data and a constructivist grounded theory for data interpretation. Theoretical sampling was used to recruit 35 participants from three main study sites across two western Canadian provinces. Recruitment, data collection, and analysis using constructivist grounded theory occurred concurrently to ensure appropriate depth of exploration. Results Our data analysis revealed five themes: Five themes were generated: Complexity of voluntourism as a concept; Diversity of knowledge representation required for developing curriculum; Effective models of care for First Nations, Inuit and/or Métis health; Essential traits that residents should have for working in First Nations, Inuit and/or Métis communities; and Building relationships and trust by engaging the community. Conclusions First Nations, Inuit and/or Métis Health should be prioritized within Canadian postgraduate medical education. Equipping trainees to provide holistic care, immersing in and learning from First Nations, Inuit and/or Métis communities is essential for developing the next generation of clinicians and preceptors. We present educational recommendations for residency programs to optimize First Nations, Inuit and/or Métis health educational experiences and provide residents with skills to provide effective and culturally safe care.
- ItemOpen AccessFactors associated with the dental service utilization by enrollees on the Lagos State health insurance scheme, Nigeria(BMC, 2025-01-03) Abodunrin, Olunike R.; Adebayo, Ezekiel T.; Adewole, Ifeoluwa E.; Olagunju, Mobolaji T.; Ekeh, Chibueze; Samuel, Ibitoye O.; Zamba, Emmanuella; Gbaja-Biamila, Titilola; Akinsolu, Folahanmi T.; Eleje, George U.; Tantawi, Maha E.; Ezechi, Oliver C.; Foláyan, Morẹ́nikẹ́ O.Abstract Background Despite assumptions that insurance coverage would boost oral healthcare utilization in Nigeria, there is insufficient evidence supporting this claim. This study investigates the associations between residential location, awareness of the oral health insurance scheme, history of dental service utilization, and acceptance of oral health insurance among individuals benefiting from the Ilera Eko Scheme; a scheme that integrates preventive and curative oral health care into the state health insurance scheme. Methods A cross-sectional survey was conducted from July to November 2023 recruiting from a database of 1520 enrollees aged of 18 and 72-years-old who had been on the scheme for at least three months. An interviewer-administered questionnaire was used to collect the data from participants living in five regions of Lagos State. The dependent variable was dental service utilization. The independent variables were awareness about Ilera Eko health insurance scheme, history of oral health problem, residential location of the respondents (Lagos Island, Badagry, Epe, Ikorodu and Ikeja), and perception about the scheme. The confounding variables were the age at last birthday, sex at birth (male or female), educational level (no education, primary, secondary, and tertiary education), level of income (< 50,000, 50,000–10000, 150,000–200,000, > 200,000), employment status (employed, self-employed and unemployed), marital status (single, married, divorced. widow/widower) and duration on the scheme (< 6 months, 6–12 months, > 12 months). A binary logistic regression analysis was conducted to determine the associations between the dependent and independent variables, controlling for confounders. Results The study recruited 485 participants of which 31 (6.4%) had used the oral health care services. Respondents with oral health problems had higher odds of using the scheme (AOR:21.065; p < 0.001). Residents in Ikeja had significantly lower odds of using the scheme when compared with residents in Lagos Island (AOR: 0.174; p = 0.005). Conclusion Respondents with oral health problems had higher odds of using the oral health insurance scheme. Innovative approaches are needed to drive the utilization of free dental service packages on health insurance schemes in Lagos State, especially for preventive care.
- ItemOpen AccessNavigating ethics in HIV data and biomaterial management within Black, African, and Caribbean communities in Canada(BMC, 2025-01-16) Souleymanov, Rusty; Akinyele-Akanbi, Bolaji; Njeze, Chinyere; Ukoli, Patricia; Migliardi, Paula; Larcombe, Linda; Restall, Gayle; Ringaert, Laurie; Payne, Michael; Kim, John; Tharao, Wangari; Wilcox, AynAbstract Background This study explored the ethical issues associated with community-based HIV testing among African, Caribbean, and Black (ACB) populations in Canada, focusing on their perceptions of consent, privacy, and the management of HIV-related data and bio-samples. Methods A qualitative community-based participatory research (CBPR) approach was employed to actively engage ACB community members in shaping the research process. The design included in-depth qualitative interviews with 33 ACB community members in Manitoba, Canada. The study was guided by a Community Guiding Circle, which contributed to study design, data analysis, and interpretation. A diverse sample was recruited through community agencies, social media, and flyers, with considerations for variations in age, gender, sexual orientation, and geographical location. The study employed iterative inductive thematic data analysis. Findings Participants expressed significant concerns about the collection, sharing, and use of HIV data from healthcare encounters, revealing mistrust towards institutions like police, child welfare, and immigration accessing their health information. Their worries centered on the handling of biological samples, data misuse, potential human rights violations, HIV criminalization, deportations, challenging consent, privacy, and bodily autonomy principles. While open to contributing to medical research, they unanimously demanded greater transparency, informed consent, and control over the secondary use of their health data. Conclusions The study underscores the need for culturally safe approaches in HIV testing and ethical governance in healthcare for ACB communities. It highlights the importance of prioritizing participant empowerment, ensuring transparency, practicing informed consent, and implementing robust data security measures to balance effective HIV information management with the protection of individual rights.
- ItemOpen AccessA graph neural network approach for hierarchical mapping of breast cancer protein communities(BMC, 2025-01-21) Zhang, Xiao; Liu, QianAbstract Background Comprehensively mapping the hierarchical structure of breast cancer protein communities and identifying potential biomarkers from them is a promising way for breast cancer research. Existing approaches are subjective and fail to take information from protein sequences into consideration. Deep learning can automatically learn features from protein sequences and protein–protein interactions for hierarchical clustering. Results Using a large amount of publicly available proteomics data, we created a hierarchical tree for breast cancer protein communities using a novel hierarchical graph neural network, with the supervision of gene ontology terms and assistance of a pre-trained deep contextual language model. Then, a group-lasso algorithm was applied to identify protein communities that are under both mutation burden and survival burden, undergo significant alterations when targeted by specific drug molecules, and show cancer-dependent perturbations. The resulting hierarchical map of protein communities shows how gene-level mutations and survival information converge on protein communities at different scales. Internal validity of the model was established through the convergence on BRCA2 as a breast cancer hotspot. Further overlaps with breast cancer cell dependencies revealed SUPT6H and RAD21, along with their respective protein systems, HOST:37 and HOST:861, as potential biomarkers. Using gene-level perturbation data of the HOST:37 and HOST:861 gene sets, three FDA-approved drugs with high therapeutic value were selected as potential treatments to be further evaluated. These drugs include mercaptopurine, pioglitazone, and colchicine. Conclusion The proposed graph neural network approach to analyzing breast cancer protein communities in a hierarchical structure provides a novel perspective on breast cancer prognosis and treatment. By targeting entire gene sets, we were able to evaluate the prognostic and therapeutic value of genes (or gene sets) at different levels, from gene-level to system-level biology. Cancer-specific gene dependencies provide additional context for pinpointing cancer-related systems and drug-induced alterations can highlight potential therapeutic targets. These identified protein communities, in conjunction with other protein communities under strong mutation and survival burdens, can potentially be used as clinical biomarkers for breast cancer.