Background: Tocotrienol, a naturally occurring form of vitamin E, has been extensively studied for its potent antioxidant, anti-inflammatory, and immune-stimulating properties. However, the clinical impact of tocotrienol supplementation on older adults’ overall health and well-being remains relatively unexplored. This research aims to investigate the efficacy of tocotrienol-rich fraction (TRF) on various health parameters associated with general well-being in individuals aged between 50 years and 75 years. Objective: It is hypothesized that TRF supplementation may exhibit positive outcomes on blood biochemistry and several physiological aspects, including lowered levels of oxidative stress and inflammation biomarkers; improvement in vascular age; and enhancement of skin condition, bone mineral density, and cognitive function. Methods: This randomized, double-blind, placebo-controlled trial was designed to investigate the effectiveness of TRF supplementation on overall health in healthy older adults. The study aims to assess the impact of a daily dosage of 200 mg of TRF over a period of 6 months. A total of 220 participants is enrolled in the study, with one-half receiving the placebo and the other one-half receiving TRF supplementation. The study comprises 3 time points: baseline, 3 months, and 6 months. At each time point, various measurements are taken to evaluate different aspects of health. The primary outcome measurements include blood biochemistry assessments, such as liver function tests, renal profile, lipid profile, and full blood count. Oxidative stress markers, including malondialdehyde, advanced glycation end products, protein carbonyl, and isoprostane, are also evaluated. Immune response markers such as interleukin-6 and tumor necrosis factor-α are assessed. Satiety regulation is examined through measurements of leptin and ghrelin. Body composition and skin health parameters, including wrinkling, pigmentation, elasticity, hydration, and sebum secretion, are evaluated. Additionally, arterial stiffness is assessed using arteriography at baseline and 6 months. For secondary outcome measures, bone mineral density is measured using dual x-ray absorptiometry, and cognitive function is assessed using the Montreal Cognitive Assessment, Rey Auditory Verbal Learning Test, and digital span test. Both bone mineral density and cognitive function are also measured at baseline and 6 months. Results: The study is progressing as planned, with 209 participants recruited as of April 2025. The research was funded in 2019, and data collection started in December 2020. Preliminary data analysis has been completed for the first 120 participants, and final results are expected upon completion of data collection and unblinding in 2026. Conclusions: By comprehensively evaluating these health aspects, this study seeks to provide valuable insights into the potential benefits of tocotrienol supplementation for promoting the overall health and well-being of the aging population. Trial Registration: National Medical Research Register (NMRR) NMRR-19-2972-51179; https://tinyurl.com/yy9yueer International Registered Report Identifier (IRRID): DERR1-10.2196/73039

Alzheimer’s disease (AD) poses a significant worldwide health challenge, requiring novel approaches for improved models and treatment development. This comprehensive review emphasises the systematic development and improvement of a biomimetic brain environment to address the shortcomings of existing AD models and enhance the efficiency of screening potential drug treatments. We identify drawbacks in traditional models and emphasise the necessity for more physiologically accurate systems through an in-depth analysis of current literature. This review aims to study the development of an advanced AD model that accurately replicates key AD pathophysiological aspects using cutting-edge biomaterials and microenvironment design. Incorporating biomolecular elements like Tau proteins and beta-amyloid (Aβ) plaques improve the accuracy of illustrating disease mechanisms. The expected results involve creating a solid foundation for high-throughput screening with enhanced scalability, translational significance, and the possibility of speeding up drug discovery. Thus, this review fills the gaps in AD modelling and shows potential for creating precise and efficient drug treatments for AD.

Background: Serum protein electrophoresis (SPE) and immunofixation electrophoresis (IFE) are essential in diagnosing multiple myeloma (MM). Machine learning (ML) methods have emerged as a potential approach to the automation process of electrophoresis interpretation to improve diagnostic efficiency and improve turnaround time. This scoping review mapped current applications of ML in the diagnosis and classification of MM, with emphasis on automated SPE and IFE interpretation. Methods: PubMed, Web of Science, and Scopus databases were searched following PRISMA-ScR guidance. Articles published between January 2015 and April 2025 applying ML to MM diagnosis were included, and findings were synthesised descriptively and thematically. Results: Thirteen studies were included, identifying two dominant application domains: ML-based analysis of IFE images and SPE patterns. Deep learning models, particularly convolutional neural networks, achieved expert-level performance in IFE analysis, with peak accuracies reaching 99.82% and F1-scores frequently exceeding 0.90. For SPE interpretation, both deep learning and classical tree-based models demonstrated robust diagnostic yields, achieving Areas Under the Curve (AUC) between 0.90 and 0.99 and overall accuracies ranging from 82.8% to 99.1%. Notably, top-performing models recorded F1-scores up to 0.98, and in some instances, they surpassed the precision of human expert panels. Conclusions: Explainable AI methods were increasingly incorporated, though external validation, multimodal integration, explainability, and real-world deployment were limited. ML demonstrates potential to augment SPE and IFE interpretation in MM diagnostics. Future work should focus on multicentre validation, explainability, and clinical integration to support safe implementation.

Background: Vitamin E has been studied for its role in reducing the growth of colorectal cancer (CRC). CRC is a worldwide health concern. A meta-analysis reported that CRC patients have a lower concentration of serum vitamin E, suggesting it to be a risk factor. Although rodent models are widely used in disease research, their application in studying vitamin E as a preventive or therapeutic agent in CRC is not well characterized. To address this gap, we conducted a scoping review to examine the available evidence, adhering to the PRISMA-ScR checklist. Methods: We searched PubMed, Google Scholar, Scopus, and Web of Science (WoS) for full-text English original articles published before May 2024, using Medical Subject Headings (MeSH) terms and free text. The following search string strategy was applied: (Vitamin E OR tocopherol$ OR tocotrienol$) AND (Colo$ cancer OR colo$ carcinoma) AND (Rodentia OR mouse OR Rodent$ OR mice OR murine OR rats OR guinea OR rabbit OR hamsters OR Animal model OR Animal testing OR animals) AND (neoplasm$ OR “tumor mass” OR tumor volume OR tumor weight OR tumor burden). Data were charted into five categories using a standardized, pretested form. The charted data were synthesized using descriptive and narrative methods. Conclusions: This study highlights that γ- and δ-tocopherols, as well as δ-tocotrienol and its metabolites, were reported to reduce tumor volume and formation in various rodent models. While these results are promising, this scoping review identifies a need for further research to address translational barriers such as dosing, bioavailability, and long-term safety before clinical application.