Toronto Metropolitan University

Background The concept of the metaverse is a virtual world that immerses users, allowing them to interact with the digital environment. Due to metaverse's utility in collaborative and immersive simulation, it can be advantageous for medical education in high‐stakes care settings such as emergency, critical, and acute care. Consequently, there has been a growth in educational metaverse use, which has yet to be characterized alongside other simulation modalities literature. This scoping review aims to provide a comprehensive overview of all research describing metaverse use in education for emergency, critical, and acute care. Methods We used Arksey and O'Malley's framework with the Levac et al. modifications to conduct a scoping review by searching these five databases (MEDLINE, EMBASE, ERIC, Web of Science, and Education Source). The framework comprises six steps: (1) identifying the research question; (2) identifying relevant literature; (3) study selection; (4) data extraction; (5) collating, summarizing, and reporting data; and (6) consultation with key informants. Relevant themes and trends were extracted and mapped for reporting. Results The search yielded 8175 citations, which ultimately led to data extraction from 65 articles. Studies evaluated metaverse programs for the learning and assessment of both technical skills (management of code blue, sepsis, stroke, etc.) and nontechnical skills (e.g., interprofessional collaboration, communication, critical decision making). Barriers to metaverse implementation include technical challenges and difficulty evaluating educational effectiveness. Conclusions The results of this scoping review highlight the current applications of metaverse as an educational tool, its identified strengths and weaknesses, and further comparison between metaverse and other educational modalities such as high‐fidelity simulation. This work provides direction for future primary and secondary research that can aid educational programmers and curriculum planners in maximizing metaverse potential in emergency, critical, and acute medical education.

Objectives Extant research suggests that mindfulness-based interventions (MBIs) support psychosocial well-being in Western, predominantly White populations. However, there is a dearth of research investigating the benefits of MBIs in ethno-racial and culturally diverse samples. A systematic evaluation of how MBIs are culturally adapted is warranted to foster well-being in equity deserving groups. Methods This review consolidated the available literature exploring the benefits of culturally adapted group-based MBIs on psychosocial outcomes within persons 18 + years of age from ethno-racial minority populations. Studies were assessed based on cultural adaptations according to eight dimensions: language, persons, metaphors, content, concepts, goals, methods, and context. Following the standards for systematic reviews, a total of ten studies were included. Results Findings indicated that culturally adapted MBIs are associated with positive outcomes in depressive symptoms, stress, and anxiety within ethno-racial minority populations. Results were inconsistent regarding improvements in mindfulness. The most common adaptation across studies pertained to content and language (60% of studies for both adaptations), demonstrating efforts to enhance intervention accessibility and align with cultural values, customs, and beliefs. The least commonly employed cultural adaptations involved goals and concepts, indicating a lower likelihood of aligning goal setting with clients’ cultural values and formulating treatment in a contextually relevant manner. Conclusions Cultural adaptations can support evidence-based treatment implementation among ethno-racial and culturally diverse populations. However, further research is needed to strengthen and validate these conclusions. Preregistration This study was preregistered with PROSPERO, no. CRD42022365796.

AI has changed the landscape of health professions education. With the hype now behind us, we find ourselves in the phase of reckoning, considering what's next; where do we start and how can educators use these powerful tools for daily teaching and learning. We recognize the great need for training to use AI meaningfully for education. Boyer's model of scholarship provides a pedagogical approach for teaching with AI and how to maximize these efforts towards scholarship. By offering practical solutions and demonstrating their usefulness, this Twelve tips article demonstrates how to apply AI towards scholarship by leveraging the capabilities of the tools. Despite their potential, our recommendation is to exercise caution against AI dependency and to role model responsible use of AI by evaluating AI outputs critically with a commitment to accuracy and scrutinize for hallucinations and false citations.

Since the release of the Canadian Truth and Reconciliation Commission's (2015) report and their 94 Calls to Action, there has been a push to advance truth and reconciliation with Indigenous peoples in Canada. Much of the heavy lifting has been done by Indigenous peoples; but to comprehensively redress injustices there is a need for non‐Indigenous support. In two studies with non‐Indigenous Canadians ( n = 355; n = 341), we investigated post‐colonial ideologies (historical negation, symbolic exclusion), ally/supporter identity and collective guilt as predictors of support for reconciliation and Indigenous collective action movements, and political tolerance of Indigenous peoples. Consistent with hypotheses, higher post‐colonial ideologies, lower ally/supporter identification and lower collective guilt related to less support and less political tolerance. Collective guilt emerged as a mediator for support for reconciliation and Indigenous collective action (except for symbolic exclusion in Study 1); but it moderated the relations for political tolerance. Collective guilt also moderated relations between symbolic exclusion and ally/supporter identity with support for reconciliation in Study 1. Future directions for advancing understanding of post‐colonial ideologies and possible applied interventions aimed at improving intergroup relations are discussed.

For the next‐generation communication systems, to improve spectral efficiency and increase the data rate, new multiple access techniques have been investigated. Orthogonal multiple access techniques are widely used in traditional communication systems while nonorthogonal multiple access (NOMA), proposed in 5G, has been a promising technology for satisfying the demand for future wireless communication networks. Sparse code multiple access (SCMA) is a code‐domain NOMA method that provides diversity gain with signal constellation coding. However, to increase the performance of SCMA, there are only limited works provided in the literature in terms of codebook design and receiver design. In this paper, a new multiple‐access model is proposed by applying various diversity techniques for downlink SCMA. The performance of the proposed model is evaluated with both computer simulations and theoretical analysis. Results show that the proposed model provides a 1.6 dB gain in terms of the bit error rate (BER) under the Rayleigh fading channel.

Objectives: In this mixed methods program of research, we investigated Indigenous participants’ experiences with racism at a Canadian postsecondary institution. Method: In Study 1 (N = 8), we interviewed Indigenous students or recent graduates about their experiences with racism and thematically analyzed their responses. We asked questions about what participants thought racism was, how frequently they experienced racism, how experiencing racism made them feel, which racist incidents were the most important to challenge, how they dealt with racism, and their positive experiences on campus as an Indigenous person. In Study 2 (N = 485), we surveyed Indigenous students about their experiences with racism. Participants responded to items about the frequency of potentially racist incidents, how those incidents made them feel, and if they considered those incidents as racist. They also responded to items about positive race-based experiences and their feelings about their on-campus experience. Results: In Study 1, participants experienced many different types of racism: internalized (including racial microaggressions, modern racism, and old-fashioned racism), interpersonal, institutional, and structural. They also shared the negative impacts of experiencing racism and the ways they challenged and coped with racism. In Study 2, participants indicated that they experienced racism on campus regularly and that these experiences tended to make them feel bad. Participants also experienced positive race-based experiences and felt good in these cases. Conclusions: Anti-Indigenous racism happens with alarming regularity at the institution and negatively impacts Indigenous participants, though participants actively push back against racism. We discuss the implications and future research directions.

This study explores mesoporous bioactive glasses (MBGs) that show promise as advanced therapeutic delivery platforms owing to their tailorable porous properties enabling enhanced drug loading capacity and biomimetic chemistry for localized, sustained release. This work systematically investigates the complex relationship between MBG composition and surfactant templating on structural evolution, in vitro bioactive response, resultant drug loading efficiency and release. A total of 12 samples of sol-gel-derived MBG were synthesized using cationic and non-ionic structure-directing agents (cetyltrimethylammonium bromide, Pluronic F127 and P123) while modulating the SiO 2 /CaO content, generating MBG with surface areas of 60–695 m ² /g. Electron microscopy and nitrogen desorption studies verified the successful synthesis of the 12 ordered MBG formulations. Assessment of hydroxyapatite conversion kinetics via FTIR spectroscopy and SEM demonstrated accelerated deposition for 70–80% SiO 2 formulations, independent of the surfactant used. However, the templating agent had an impact on drug loading as observed in this study where MBG synthesized by the templating agent Pluronic P123 had higher drug loading compared to the other surfactants. To determine the drug release mechanisms, the in vitro kinetic profiles were fitted to various mathematical models including ze-ro. Most compositions exhibited release properties closest to zero-order, indicating a concentration-independent drug elution rate. These results in this study explain the relationship between tailored hierarchical architecture and intrinsic ion release rates to enable advanced functionality.

Pregnancy loss is a traumatic event that has far-reaching consequences for affected women and their families. While most studies have focused on the immediate stressors Black women face after a pregnancy loss, little is known about the consequences beyond this period. The purpose of this study was to explore the experiences of Black Canadian women beyond the immediate period of pregnancy loss. Using an exploratory qualitative design, we purposely recruited and interviewed 32 Black Canadian women with lived experience of pregnancy loss. Guided by thematic analysis approach, we identified three themes: (a) experiencing mental health breakdown, (b) struggling to maintain emotional connection with family, and (c) dealing with the stress of returning to work. The findings provide a comprehensive understanding of the long-term impact of pregnancy loss on Black Canadian women's mental health, family, and work-life. Providing ongoing follow-up care is crucial to identifying and addressing the risk of depression and suicide that Black Canadian women experience after a pregnancy loss.

Pathology provides the definitive diagnosis, and Artificial Intelligence (AI) tools are poised to improve accuracy, inter-rater agreement, and turn-around time (TAT) of pathologists, leading to improved quality of care. A high value clinical application is the grading of Lymph Node Metastasis (LNM) which is used for breast cancer staging and guides treatment decisions. A challenge of implementing AI tools widely for LNM classification is domain shift, where Out-of-Distribution (OOD) data has a different distribution than the In-Distribution (ID) data used to train the model, resulting in a drop in performance in OOD data. This work proposes a novel clustering and sampling method to automatically curate training datasets in an unsupervised manner with the aim of improving model generalization abilities. To evaluate the generalization performance of the proposed models, we applied a novel use of the Two One-sided Tests (TOST) method. This method examines whether the performance on ID and OOD data is equivalent, serving as a proxy for generalization. We provide the first evidence for computing equivalence margins that are data-dependent, which reduces subjectivity. The proposed framework shows the ensembled models constructed from models that generalized across both tumor and normal patches enhanced performance, achieving an F1 score of 0.81 for LNM classification on unseen ID and OOD samples. Interactive viewing of slide-level segmentations can be accessed on PathcoreFlow™ through https://web.pathcore.com/folder/18555?s=QTJVHJuhrfe5. Segmentation models are available at https://github.com/IAMLAB-Ryerson/OOD-Generalization-LNM.

This review provides an in-depth exploration of organ-on-a-chip (OOC) technology with a particular focus on kidney-on-a-chip systems. It examines the core principles of OOC, including the variety of materials, microfluidic designs, and the potential applications in drug testing, disease modeling, and personalized medicine. The review delves into the unique challenges in developing OOC systems, including fabrication methods, cost, and the pressing need for standardization across platforms. Specific attention is given to kidney-on-a-chip technology, emphasizing the modeling of the glomerular filtration barrier (GFB)—a crucial element in kidney function. The GFB’s structure, comprising the fenestrated endothelium, basement membrane, and podocytes, is analyzed in the context of its role in filtration. While advancements in GFB modeling offer significant insights into renal disease mechanisms and drug toxicity testing, challenges remain, including achieving accurate GFB replication, sourcing high-quality human kidney cells, and optimizing culture conditions. The review concludes by highlighting the transformative potential of kidney-on-a-chip systems, while emphasizing the necessity for standardization and reproducibility to fully realize their biomedical applications.

This study elucidated the radiation response characteristics of a Gafchromic radiochromic film subjected to low photon doses of ≤50 mSv, which corresponds to the annual whole body effective dose limit for radiation workers in Canada. Radiochromic films are investigated for possible use as a complementary tool for the Canadian Armed Forces that can be worn in addition to their existing personal dosimetry to quickly assess personal radiation dose received from radiological hazards without reliance on electronics. The films were exposed to varying photon energies emanating from x-ray generators and radioisotopes, specifically cesium-137, cobalt-60, and americium-241. The resultant radiation-induced film darkening was quantitatively assessed employing three analytical methodologies: net optical density analysis, UV/Visible spectroscopic analysis, and Fourier Transform Infrared spectroscopic analysis. Ideally, a film dosimeter necessitates a pronounced chromatic alteration and the capability to accurately quantify doses ≤50 mSv where net optical density analysis was identified as the optimal modality for interpreting the film darkening into a dose approximation. This new approach established a lower detection threshold of 7.6 mSv for the films when exposed to cesium-137 radiation. Notably, the film exhibited a linear dose response relationship in terms of net optical density; however, a photon energy-dependent variability was observed within the 0–100 mSv dose range. In conclusion, these Gafchromic radiochromic films present a promising candidate for military dosimetry applications. They offer a real-time, visual dose response that can be discerned by military personnel or analyzed using mobile spectroscopic instrumentation. Moreover, these films demonstrate proficiency in the accurate quantification of photon doses ≤50 mSv. Future investigations will evaluate the film's performance under heterogeneous and indeterminate radiation environments, as well as the impact of environmental conditions on the film’s performance.

Despite the expanding therapeutic options available to cancer patients, therapeutic resistance, disease recurrence, and metastasis persist as hallmark challenges in the treatment of cancer. The rise to prominence of generative artificial intelligence (GenAI) in many realms of human activities is compelling the consideration of its capabilities as a potential lever to advance the development of effective cancer treatments. This article presents a hypothetical case study on the application of generative pre-trained transformers (GPTs) to the treatment of metastatic prostate cancer (mPC). The case explores the design of GPT-supported adaptive intermittent therapy for mPC. Testosterone and prostate-specific antigen (PSA) are assumed to be repeatedly monitored while treatment may involve a combination of androgen deprivation therapy (ADT), androgen receptor-signalling inhibitors (ARSI), chemotherapy, and radiotherapy. The analysis covers various questions relevant to the configuration, training, and inferencing of GPTs for the case of mPC treatment with a particular attention to risk mitigation regarding the hallucination problem and its implications to clinical integration of GenAI technologies. The case study provides elements of an actionable pathway to the realization of GenAI-assisted adaptive treatment of metastatic prostate cancer. As such, the study is expected to help facilitate the design of clinical trials of GenAI-supported cancer treatments.

Fake news poses a significant threat to public opinion and social stability in modern society. This study presents a comparative evaluation of BERT-like encoder-only models and autoregressive decoder-only large language models (LLMs) for fake news detection. We introduce a dataset of news articles labeled with GPT-4 assistance (an AI-labeling method) and verified by human experts to ensure reliability. Both BERT-like encoder-only models and LLMs were fine-tuned on this dataset. Additionally, we developed an instruction-tuned LLM approach with majority voting during inference for label generation. Our analysis reveals that BERT-like models generally outperform LLMs in classification tasks, while LLMs demonstrate superior robustness against text perturbations. Compared to weak labels (distant supervision) data, the results show that AI labels with human supervision achieve better classification results. This study highlights the effectiveness of combining AI-based annotation with human oversight and demonstrates the performance of different families of machine learning models for fake news detection.