Chi Wei Ong

With the growing demand for accurate yet effortless health monitoring at home, most current approaches to stool analysis rely on self-reported diaries that are prone to recall bias and low adherence. Here we present a fully passive alternative: the Precision Health Integrated Diagnostic (PHIND) system, a smart-toilet-based platform that enables automated defecation monitoring without requiring users to alter their daily routines. By integrating optical and pressure sensors with cloud-based convolutional neural networks, the PHIND system classifies stool form according to the Bristol Stool Form Scale and records key defecatory parameters, including total event time, defecation duration and time to first stool drop. The protocol proceeds in three principal stages: (1) assembling and mounting the hardware onto a conventional toilet; (2) training convolutional neural network models for stool classification and event detection; and (3) image acquisition and deploying cloud infrastructure for real-time analysis, data storage and visualization. Compared with traditional methods that depend on user-reported stool diaries, PHIND provides objective, near real-time data free from recall error, enabling more reliable early detection and long-term management of gastrointestinal conditions. Researchers and clinicians can expect high classification accuracy and robust, longitudinal insights into defecation patterns. The complete protocol—from hardware setup to system validation—can typically be completed within 2 d, excluding printed circuit board manufacturing, which generally requires up to 15 d depending on the manufacturing provider.

The global demand for cocoa has increased over the years. However, cocoa production faces challenges such as high production costs and low yield. A potential source that can replace cocoa powder is jackfruit seed flour, which can produce a similar chocolate aroma. Jackfruit seeds (Artocarpus heterophyllus seeds) are commonly discarded, but studies have shown that when jackfruit seeds are roasted, they give off a similar aroma to cocoa powder. Following preferred reporting items for systematic review and meta‐analysis guidelines, articles found across PubMed, Scopus and ScienceDirect were screened. We reviewed 25 relevant articles that explored the aromatic compounds, public receptiveness and nutritional benefits of jackfruit seeds. This review demonstrates that fermented jackfruit seeds and cocoa powder have the greatest similar aromatic compounds. Jackfruit seeds are also shown to have an advantage over cocoa powder in terms of public receptiveness (towards aroma intensity, taste and texture, colour, and appearance), nutritional content, colour, antioxidants, and antimicrobial properties. More studies on the transition costs for producers, types of jackfruit seeds and machinery required are needed to replace cocoa powder with jackfruit seed flour entirely.

Purpose of Review Bananas (Musa spp.), a staple fruit crop in Southeast Asia, are widely recognised for their nutritional value and potential medicinal properties, including the management of diabetes. To address the diabetes pandemic, various studies have explored the efficacy of many natural foods in reducing blood glucose, preventing complications that arise from the chronic illness. However, a comprehensive overview of bioactive compounds and their antidiabetic effects across different banana species in recent years is lacking. This review provides a comprehensive overview of bioactive compounds in various parts of the bananas that have demonstrated antihyperglycemic activities. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a thorough literature search using ScienceDirect, Scopus, and PubMed databases, resulting in the inclusion of 27 relevant articles. Recent Findings Emerging evidence suggests that different parts of the banana plant contain various bioactive compounds with antihyperglycemic activities, offering promising benefits for diabetes management. Summary The findings reveal that the antihyperglycemic effects of bananas can be attributed to specific bioactive compounds, such as phenols, saponins, alkaloids, sterols, and flavonoids, through mechanisms like inhibition of α-glucosidase, β-glucosidase, α-amylase and sucrase enzymes, glucose uptake assay, and inhibition of formation of advanced glycation end-products. Liver glycogen content and fasting blood glucose in rat models, along with HbA1c measurements in human subjects, were also assessed to evaluate invivo antidiabetic activity, which has yielded positive outcomes. The results support the potential medicinal and pharmaceutical benefits of bananas in clinical diabetes management and suggest that incorporating banana-derived compounds could enhance the cost-effectiveness of antidiabetic treatments.

BACKGROUND The increasing prevalence of mental health conditions, exacerbated by the COVID-19 pandemic, underscores the urgent need for improved psychiatric education. This study investigates the potential role of generative artificial intelligence (GenAI) in psychiatric education. OBJECTIVE While GenAI has shown promising outcomes in medical education, its application in psychiatric training remains underexplored. In this study, we hope to highlight the potential roles of GenAI in psychiatric education. METHODS We conducted a scoping review to identify the role of GenAI in psychiatric education based on the educational framework of the Canadian Medical Education Directives for Specialists (CanMEDS). RESULTS Of the 6412 papers identified, 5 studies met the inclusion criteria, revealing key roles for GenAI in case-based learning, simulation, content synthesis, and assessments. Despite these promising applications, limitations such as content accuracy, biases, and concerns regarding security and privacy were highlighted. CONCLUSIONS Despite these promising applications, limitations such as content accuracy, biases, and concerns regarding security and privacy were highlighted. This study contributes to the understanding of how GenAI can enhance psychiatric education and suggests future research directions to refine its use in training medical students and primary care physicians. GenAI holds significant potential to address the growing demand for mental health professionals, provided its limitations are carefully managed.

Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.

Heart failure is a global health concern with significant implications for healthcare systems. Left ventricular assist devices (LVADs) provide mechanical support for patients with severe heart failure. However, the placement of the LVAD outflow graft within the aorta has substantial implications for hemodynamics and can lead to aortic insufficiency during long-term support. This study employs computational fluid dynamics (CFD) simulations to investigate the impact of different LVAD outflow graft locations on aortic hemodynamics. The introduction of valve morphology within the aorta geometry allows for a more detailed analysis of hemodynamics at the aortic root. The results demonstrate that the formation of vortex rings and subsequent vortices during the high-velocity jet flow from the graft interacted with the aortic wall. Time-averaged wall shear stress (TAWSS) and oscillatory shear index (OSI) indicate that modification of the outflow graft location changes mechanical states within the aortic wall and aortic valve. Among the studied geometric factors, both the height and inclination angle of the LVAD outflow graft are important in controlling retrograde flow to the aortic root, while the azimuthal angle primarily determines the rotational direction of blood flow in the aortic arch. Thus, precise positioning of the LVAD outflow graft emerges as a critical factor in optimizing patient outcomes by improving the hemodynamic environment.

Predictive modeling is a complex methodology that involves leveraging advanced mathematical and computational techniques to forecast future occurrences or outcomes. This tool has numerous applications in medicine, yet its full potential remains untapped within this field. Therefore, it is imperative to delve deeper into the benefits and drawbacks associated with utilizing predictive modeling in medicine for a more comprehensive understanding of how this approach may be effectively leveraged for improved patient care. When implemented successfully, predictive modeling has yielded impressive results across various medical specialities. From predicting disease progression to identifying high-risk patients who require early intervention, there are countless examples of successful implementations of this approach within healthcare settings worldwide. However, despite these successes, significant challenges remain for practitioners when applying predictive models to real-world scenarios. These issues include concerns about data quality and availability as well as navigating regulatory requirements surrounding the use of sensitive patient information—all factors that can impede progress toward realizing the true potential impact of predictive modeling on improving health outcomes.