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

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.

Translational AI Integration and Gut-Brain Axis Biomarkers in Alzheimer’s Disease: Systematic Review

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

Osteoarthritis (OA) is a prevalent and disabling condition affecting over one third of Canadians aged 65 and older, with the hand and wrist being among the most impacted joints. This chronic, degenerative joint disease severely limits physical independence by causing synovial inflammation and cartilage degradation, leading to joint stiffness. As a result, seniors diagnosed with OA often experience persistent joint pain, progressive joint immobility, and diminished quality of life. Current treatments such as pharmacotherapy and physiotherapy often provide limited or inconsistent relief due to side effects, accessibility, or patient adherence. In response, this project introduces a novel, multimodal therapeutic glove designed to deliver lasting non-pharmacological relief for individuals with hand and wrist OA. The glove integrates thermal stimulation and localized vibration to reduce pain and joint immobility, improving hand function. The design is grounded in an evidence-based understanding of osteoarthritis pain responses, with therapeutic principles derived from case studies and meta-analyses of both thermotherapy and massage therapy. The glove features vibrating mechanisms that simulate massage movements, providing relief and promoting circulation in affected areas. Furthermore, the glove incorporates heat therapy, which helps reduce stiffness and alleviates pain by increasing blood flow. To evaluate the glove’s effectiveness and usability, a pilot test was conducted with nine participants, where two-thirds of the participants reported hand or wrist pain. Guided sessions were used to measure pain and assess mobility using a numerical rating scale (0–5). The average reported pain relief score was 3.89, while the ease-of-movement score averaged 4.56, suggesting moderate to significant improvement.

Multimodal Wearable Devices: A Non-Pharmacological Treatment of Hand and Wrist Osteoarthritis

As global energy demand rises and climate concerns intensify, solar energy systems or photovoltaic systems are becoming increasingly vital sources of renewable energy. However, their potential remains limited by the inability to operate naturally at the Maximum Power Point during hours of fluctuating sunlight. This study addresses this challenge by improving photovoltaic system efficiency through advanced Maximum Power Point Tracking techniques. We hypothesized that real-time duty cycle control would allow actual power to consistently rise towards Pmax, regardless of fluctuations. Using simulation software PowerSIM (PSIM), we analyzed the impact of varying duty cycles on power output across several trial runs. We conducted simulations on four PV panels, each with a distinct maximum power value, by modifying the duty cycle under five controlled power fluctuations designed to mimic variable sunlight. A code was developed and implemented to dynamically adjust the duty cycle based in response to changes in output power. The results showed that the PV panels with the highest (395W) and lowest (225W) Pmax values responded effectively, with actual power rising towards Pmax after each fluctuation. To validate our findings, we implemented the algorithm on a digital signal processing board using PSIM’s Processor-in-the-Loop module. The test completed in 4 minutes and 27 seconds, with the system's duty cycle varying with changes in actual power within a 0.03 range. The broader significance of this project lies in its contributions to developing responsive and cost effective PV systems that align with the goals of economic resilience and sustainable chronic disease management.

Improving Photovoltaic Systems for Renewable Energy Efficiency and Affordably Powering Aging Communities

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

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

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

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

Cardiovascular disease is one of the most common aging diseases and is the leading cause of mortality worldwide. Therefore, new diagnostic and therapeutic strategies are necessary to treat this type of disease. Drug discovery, a research process that identifies and develops a new medication, is time-consuming and costly. Artificial Intelligence (AI) can potentially revolutionize the drug discovery process by improving its efficiency, accuracy, and speed. Binding affinity is an important parameter in drug design. This paper used data from Kaggle's competition “NeurIPS 2024 - Predict New Medicines with BELKA” to train three machine learning models -- a Logistic Regression model, a Sequential Neural Network built with TensorFlow, and an MLPClassifier from Scikit-Learn -- to predict the probability that a molecule will bind to three proteins: BRD4, HSA, and sEH. MLPClassifier achieved the highest public score of 0.04353, and Logistic Regression achieved the highest private score of 0.02994. Future research can enhance the performance of these models and apply transfer learning to extend their use to predicting binding affinity between drugs and cardiovascular disease–related proteins. By leveraging machine learning to predict molecular binding in out-of-distribution protein targets, this research could pave the way for future breakthroughs in developing new treatments for cardiovascular and other diseases.

Predicting Binding Affinity Using Machine Learning: A Step Toward Cardiovascular Drug Discovery

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.

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

Introduction: Acute interventions allow researchers to examine and compare the influence of a single exercise session. While researchers assume the washout period will minimize carryover effects between exercise bouts, early exercise adaptations may still take place, as observed within the repeated-bout effect. Rarely are these early adaptations assessed. This study aimed to assess pre-to-post acute intervention changes in body composition, strength, and cycling peak power output (Max PO) following an acute intervention of four identical exercise bouts. Methods: Eighteen untrained, healthy adults participated in both pre- and post-study testing. Weight and percent body fat were measured using a digital scale and air displacement plethysmography, respectively, pre- and post-study. Estimated 1 repetition maximums (e1RM) for leg press, chest press, seated row testing, and an incremental cycling test was conducted pre- and post-study. Paired t-tests were used to compare pre- and post-study outcomes in the overall group and stratified by sex. Results: Body weight increased overall (p=0.001), but sex-based analyses revealed an increase only in females (p=0.002). There was no change in percent body fat (p>0.05). e1RM increased for leg press, chest press and seated row as did cycling Max PO (p<0.05 for all), with increases in both sexes for leg press and chest press, and males only for seated row. Conclusions: The acute exercise intervention induced strength and performance improvements, indicating possible early adaptations to exercise. These adaptations may have implications on physiological outcomes measures and should be assessed in other acute interventions.

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