Canada

Objective: This scoping review maps the existing global evidence on the met and unmet cognitive, environmental, financial, physical, personal, psychological, and social needs of community-dwelling Black older adults. Methods: Following Arksey and O’Malley’s framework and the Joanna Briggs Institute methodology, we conducted a comprehensive search across 12 databases for peer-reviewed articles published between 2000 and March 2025. Eligible studies reported on care needs within seven domains. Data were extracted, categorized, and summarized descriptively using PRISMA-ScR guidelines. Results: From 30,905 citations, 552 studies across 28 countries (83.5% in the U.S.) identified 5,023 needs, primarily in the personal and physical domains, based on mostly small, urban, quantitative studies. The most unmet needs within their domain were processing speed, transportation by bike, financial status, coordination, apathy,caregiving and assisted burden, and social isolation. In contrast, the most met need within their domains were nationality/birthplace, muscle power, insurance, social capital, self-efficacy, language and land use mix. Conclusion: This scoping review highlights that Black older adults face widespread unmet needs, particularly in physical, cognitive, financial, psychological, and environmental domains, largely due to structural inequities and the erosion of traditional support systems. Addressing these issues requires culturally grounded, equity-driven approaches that center the lived experiences of Black older adults and inform inclusive, responsive aging policies globally.

Indicium 2025

The Met and Unmet Needs of Black Older Adults: A Scoping Review

needs

black

older adults

ageing

This annual scholarly program is designed to provide undergraduate students with a structured introduction to laboratory research within their home institution. Participants are matched with principal investigators and mentored by graduate students, enabling them to engage directly in ongoing research projects and develop foundational skills in scientific inquiry.

This annual scholarly program is designed to provide undergraduate students with a structured introduction to laboratory research within their home institution.
This year’s cohort focused on projects in the field of longevity science, exploring diverse topics such as bioinformatics, molecular biology, and cognitive science.

technical paper

Large language models are a fascinating and powerful technology, but are not faultless. Iteratively selecting the most probable next word can result in a sentence that sounds coherent, but contains content that is fabricated, taken out of context, or in self-conflict. This can create particular danger when end users rely on LLMs for information on critical issues. Many countries lack equal or affordable access to healthcare. This can lead to some viewing popular LLM chatbots such as ChatGPT as being an online doctor; available to answer their healthcare needs. People with chronic diseases are particularly vulnerable to this, as the chronicity of their disease presents a consistent healthcare cost. Our research seeks to take the first step of many in creating a framework within which LLMs can self-check for hallucinations, resulting in more reliable information. We worked in reference to an existing paper to create a dataset containing pairs of questions and hallucinated answers, specifically in reference to chronic disease. We also conduct an experiment on our dataset in which we determine whether LLMs can accurately detect their own hallucinations. We find that, while some LLMs can identify a hallucinated answer with incredible accuracy, it is highly inconsistent from model to model. We endorse the future research of hallucination detection technologies in LLMs in order to mitigate risk of harm caused by the dissemination of inaccurate information by LLMs.

A Reflection on Hallucinations in LLM Responses to Chronic Disease Queries

Fragile X syndrome (FXS) is a neurodevelopmental disorder caused by the expansion of cysteine-guanine-guanine (CGG) repeats in the fragile X messenger ribonucleoprotein 1 (FMR1) gene. The increase in repeats silences the transcription of fragile X messenger ribonucleoprotein (FMRP), resulting in intellectual and developmental disabilities. Despite the development of symptom management therapies, there is no cure for FXS. In the past decades, several studies have demonstrated the importance of age-related alterations in the processing of amyloid precursor protein (APP) in FXS pathology. Hence, this systematic review was conducted to summarize and evaluate FXS treatments targeting components of the APP pathway. Using PubMed, a literature search was performed and uncovered 88 results. 9 articles related to the APP pathway were selected, and they were further categorized by their targets: glutamate, metabotropic glutamate receptor 5 (mGluR5), brain cytoplasmic RNA (BC1), and A disintegrin and metalloproteinase 10 (ADAM10). The majority of these results focused on treating the symptoms in young patients, corresponding with peak symptomatology. However, FXS individuals also display age-dependent premature cognitive decline. Since APP processing is altered with age, we suggest these treatments for FXS in younger individuals may be translatable to the aging FXS population. Further research is required to understand the mechanism of age-dependent alternation in APP processing in FXS patients and to investigate the efficiency of treatments with increased sample size and different tissues.

Aging with Fragile X Syndrome: Prospective Treatments Targeting the Amyloid Processing Pathway

People from around the world experience mobility impairments, leading to significant impacts on independence and quality of life. While conventional mobility assistive devices such as wheelchairs, walkers, and prosthetics offer essential support, they often lack adaptability and user responsiveness. Artificial intelligence redefines mobility devices by advancing autonomy, personalization, and user experience. This scoping review maps the current landscape of AI-powered mobility aids for adults with physical impairments. Using the Arksey and O’Malley framework and PRISMA-ScR guidelines, we systematically reviewed 39 peer-reviewed studies, which were identified through specific search strategies developed for each database. Screening and data extraction were conducted in pairs using a predefined criterion. The included studies spanned 17 countries and focused primarily on smart wheelchairs (n=22), followed by walkers (n=8), prosthetics (n=3), rollators (n=2), and individual studies on canes, wheelchairs/scooters, lower-limb braces, and body-weight support systems (n=1 each). AI models were then categorized by learning type. AI-powered mobility devices offer individual benefits like enhanced navigation, motivation, and emotional well-being, but users face challenges such as device complexity, usability issues, and fatigue. At the administrative level, these technologies reduce caregiver workload and improve efficiency, yet are hindered by insufficient training and limited user input, while policy-level barriers include inadequate funding and lack of regulatory support. These findings affirm the potential of AI to enhance mobility and autonomy and promote the urgent need for inclusive, real-world research, standardized evaluation, and equitable access across diverse populations.

AI-powered Mobility Devices for Adults With Impairments: A Scoping Review

As populations age globally, older adults are increasingly affected by chronic physical and mental health challenges, including frailty, cognitive impairment, depression, and social isolation. Artificial intelligence (AI) tools such as chatbots, wearable monitors, predictive models, and socially assistive robots have been proposed as scalable solutions to support aging populations. This literature review systematically examined 28 peer-reviewed studies to evaluate the efficacy and usability of current AI interventions designed to enhance health and well-being in adults aged 65 and older. Methods included supervised machine learning for diagnosis and prediction, large language models for health communication, and co-designed systems for home-based care and emotional support. Results suggest that AI applications can improve physical activity, emotional regulation, and early detection of cognitive decline and depression, with user feedback indicating positive perceptions of usability and engagement. However, most studies had small, homogeneous samples, lacked long-term follow-up, and were conducted in simulated or non-clinical settings. Ethical and implementation challenges were common, including data privacy concerns, unclear algorithmic decision-making, limited digital literacy, and underrepresentation of diverse older adult populations. Despite these limitations, the evidence indicates that AI holds promise in augmenting and streamlining older adult care when tools are transparent, inclusive, and tailored to user needs. Broader adoption will require rigorous evaluation in real-world environments, equitable design practices, and sustained attention to ethical risks.

Artificial Intelligence for Care for Older Adults with Chronic Health Conditions

Loneliness is increasingly recognized as a non-motor symptom in Parkinson’s disease (PD), impacting mood, cognitive decline, and even mortality. Individuals with PD are vulnerable due to physical limitations, communication challenges, and stigma. This review synthesizes peer-reviewed literature on the intersection of loneliness, social isolation, and PD. Using Covidence, we screened 232 abstracts and subsequently reviewed 102 full-text articles. Twenty contributed contextual or descriptive insights, while only four studies provided structured, analyzable data using validated measurement tools or reporting statistical associations with clinical outcomes. Most studies were observational, published between 2020 and 2024, in English. Where reported, loneliness prevalence in PD populations ranged from 24% to over 30%, and was linked to poor quality of life, depression, and a greater disease burden. One prospective study identified a significant association between loneliness and incident PD (HR=1.25, fully adjusted), while others connected loneliness to reduced autonomy and poorer functional outcomes. However, intervention studies aimed at alleviating loneliness were notably rare and typically descriptive, with no high-quality trials identified. A concern was the limited use of formal risk bias tools, with only 17% of studies employing one. Furthermore, conflict of interest disclosures were present in a mere 13% of the articles included. While 60% of studies did include reviewer's comments on bias, these lacked standardization. This review underscores a growing but methodologically inconsistent body of literature. Improved standardization is crucial to develop evidence-based targeted interventions to address loneliness in individuals with PD.

Bridges in Parkinson’s Disease: Connecting Lives, Ending Loneliness

As the global economy expands, its environmental consequences intensify. Atmospheric CO2 now exceeds 420 ppm and continues rising. Greenhouse gases use CO2 enrichment to boost crop yields, but most rely on fossil fuel-derived CO2. This study models a Direct Air Capture (DAC) system using Zeolite 13X to recycle CO2 for greenhouse enrichment. Regeneration via solar, electricity, and natural gas was compared to determine which energy source offers the most sustainable outcome. A custom gas chamber was packed with Zeolite 13X and outfitted with four thermocouples. Following calibration, the device was connected to a carbon dioxide pump to measure and analyze the total adsorption level. Zeolite 13X effectively captured and released CO2 using all regeneration methods. The highest CO2 adsorption was observed using electric heat wrap (0.275 mmol CO2/g) and solar treatment (0.201 mmol CO2/g). Adsorption was greater in heat-assisted trials with Zeolite 13X compared to controls. Energy inputs remained consistent across sources: grid electricity (0.2653 MJ/kg CO2), solar (0.2642 MJ/kg CO2), and natural gas (0.2681 MJ/kg CO2). Grid electricity produced the lowest emissions per kilogram of CO2 captured (0.003 kg CO2 emitted/kg CO2), followed by solar (0.008 kg/kg), and natural gas (0.013 kg/kg). The experimental findings validate the energy-efficient nature of Zeolite 13X carbon capture technology which shows promise for agricultural and clean energy sector applications.

CO₂ Adsorption and Regeneration of Zeolite 13X: Energy Resource Comparison

Heart disease remains the leading global cause of death, highlighting the urgent need for accurate and scalable early detection methods. This study investigates the effectiveness of supervised machine learning techniques in predicting heart disease from routine clinical data. The objective is to determine which classification model provides the most reliable predictions in a clinical setting. We combined four public heart disease datasets from the UCI Machine Learning Repository and selected 14 relevant clinical features. Missing values were filled in using standard statistical methods (mean, median, or mode as appropriate). We then trained and evaluated four supervised machine learning models: logistic regression, support vector machine, extreme gradient boosting, and a multi-layer neural network. Each model was tuned for optimal performance and assessed using accuracy, precision, recall, and the F1-score. All models achieved higher than 80% accuracy. The support vector machine was the most accurate with 83.2% on the test data and also attained the highest F1-score (73%) for identifying heart disease cases. The neural network and gradient boosting models each reached about 82% accuracy, with the neural network identifying slightly more actual positive cases (higher recall). The logistic regression model trailed slightly at 80.7% accuracy but still showed competitive precision and recall. Machine learning models can effectively predict heart disease, providing a valuable tool for early detection. Among the models tested, the support vector machine performed best. These results indicate that support vector machines may offer stronger predictive performance for clinical applications of heart disease detection.

Evaluating Supervised Machine Learning Models for Heart Disease Prediction

Optical Coherence Tomography (OCT) is a widely adopted imaging technique in ophthalmology, yet its clinical interpretation remains prone to inconsistency and subjectivity. This study presents a retrieval-augmented generation (RAG) framework aimed at improving both diagnostic accuracy and interpretability in OCT-based analysis. The pipeline integrates a dual-headed convolutional neural network with a case-based retrieval system and a language model capable of generating structured clinical summaries. A ResNet-50 model was trained to classify diabetic retinopathy (DR), diabetic macular edema (DME), and key retinal biomarkers using a public OCT dataset. Feature embeddings generated by the model were indexed into a similarity-search database to enable retrieval of clinically relevant cases. Retrieved scans, along with classification outputs, were used to prompt a large language model to generate concise diagnostic reports that reflect both the model’s predictions and visually aligned precedents. The system achieved 89% accuracy in distinguishing DR from DME in a hold-out test set. Interpretability was also demonstrated through consistent retrieval of semantically similar cases. Generated reports were found to be readable and aligned with clinical reasoning. By combining classification performance with transparent, case-grounded explanations, the RAG approach enhances clinical confidence and supports decision-making. This work highlights the potential of retrieval-augmented pipelines to reduce variability in OCT interpretation and to serve as a scalable, explainable foundation for future AI-driven diagnostic support in ophthalmology.

Improving Optical Coherence Tomography Interpretation With Retrieval-Guided Diagnosis and Text Generation

Introduction: Breast cancer is predominantly a disease of aging, with most cases occurring in women over the age of 50. A key biological contributor to this age-related risk is “inflammaging”—a chronic, low-grade inflammatory state driven by immunosenescence and dysregulated cytokine signalling. This persistent inflammatory milieu promotes tumour initiation, growth, and progression. Dietary factors, particularly omega-3 polyunsaturated fatty acids (n-3 PUFA), may modulate inflammaging and influence breast cancer development. Methods: This narrative review synthesizes findings from epidemiological, preclinical, and mechanistic studies examining the role of n-3 PUFA, particularly EPA and DHA, in modifying inflammaging and breast cancer risk. Literature was identified through PubMed and reference screening using keywords related to inflammation, aging, dietary fats, and breast cancer. Results: Preclinical and mechanistic studies demonstrate that n-3 PUFA exert anti-inflammatory, anti-proliferative, and pro-apoptotic effects through modulation of NF-κB, cytokine signaling, lipid raft integrity, and immune cell function. Notably, these mechanisms directly target biological processes underlying inflammaging. Early-life exposure to n-3 PUFA appears especially protective against breast cancer. Epidemiologic evidence suggests a modest protective association, particularly in populations with higher marine n-3 PUFA intake and lower n-6/n-3 PUFA ratios. Conclusion: n-3 PUFA may attenuate inflammaging and represent a promising dietary strategy to reduce age-related breast cancer risk. Their integration into the diet, particularly during key windows of susceptibility, could represent a preventive approach targeting the inflammation–aging–breast cancer axis.

The Role of Omega-3 Polyunsaturated Fatty Acids in Inflammaging and Breast Cancer: A Narrative Literature Review

Alzheimer’s disease (AD) is a neurodegenerative disorder driven by diverse mechanisms, including cognitive decline and disrupted gut-brain communication, resulting in a mortality increase of over 145% in North America. Emerging evidence highlights the critical role of gut-derived metabolites, G protein, and short-chain fatty acids, which modulate neuroinflammation, tau phosphorylation, and Aβ aggregation via G protein-coupled receptors (GPCRs), including C3AR, GPR84, and GPR27. These effects are mediated through signalling pathways such as STAT3 and cAMP/PKA, with orphan GPCRs emerging as promising, disease-specific targets. Through examining literature research, we assessed machine learning models, such as support vector machines and random forest models. Insight-generating AI methods and tools such as LIME and SHAP were used to clarify the model decisions, which pinpoint the critical biomarkers and brain regions that are connected to AD progression. We propose a personalized treatment that integrates microbiome sequencing, AI-informed GPCR targeting, and therapeutic strategies such as probiotics, metabolite mimics, and drug repurposing in elderly patients who are living with AD. This system level supports precision neurology by customizing treatments to individual gut and molecular profiles, leading AD patient care towards targeted personalized therapies. Future directions include validation via CRISPR-edited organoids, fecal transplant models, and microbiome-guided clinical trials. This approach holds promise for reducing cognitive decline, long-term care reliance, and healthcare costs, while advancing therapeutic equity and economic resilience in aging populations.

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