Glycemic Index Laboratories

The accelerating complexity of global regulatory frameworks, driven by rapid technological advancements, cross-border transactions, and shifting socio-economic priorities, has placed unprecedented demands on organizations to maintain continuous compliance. Traditional compliance management systems, often rule-based and manually updated, struggle to adapt to the dynamic and fragmented nature of these evolving landscapes. This paper proposes the design of intelligent compliance systems that leverage artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to automate regulatory monitoring, interpretation, and enforcement. By integrating real-time data streams from multiple jurisdictions, the proposed system employs semantic analysis to extract, classify, and map regulatory requirements to organizational policies, operational processes, and risk controls. A modular architecture is developed to ensure scalability, interoperability, and adaptability, enabling sector-specific customization and rapid incorporation of regulatory changes. The system incorporates predictive analytics to forecast regulatory trends, simulate compliance scenarios, and recommend proactive adjustments, thereby transforming compliance from a reactive obligation into a strategic advantage. Furthermore, explainable AI techniques are embedded to enhance transparency and trust, ensuring that automated decisions align with both legal mandates and ethical standards. Case studies across finance, healthcare, and energy sectors illustrate how intelligent compliance systems reduce operational risk, lower compliance costs, and improve audit readiness. The research underscores the importance of harmonizing technological innovation with robust governance frameworks to mitigate algorithmic bias, protect sensitive data, and meet jurisdiction-specific legal obligations such as GDPR, CCPA, and sectoral regulations. This work concludes that intelligent compliance systems represent a paradigm shift, enabling organizations to navigate the evolving global regulatory landscape with agility, accuracy, and strategic foresight, while fostering regulatory harmonization and operational resilience in an increasingly interconnected world. Keywords: Intelligent Compliance Systems, Artificial Intelligence, Machine Learning, Regulatory Technology, RegTech, Global Regulations, Natural Language Processing, Predictive Analytics, Explainable AI, Compliance Automation, Governance Frameworks, Operational Resilience, Risk Management, Legal Technology, Regulatory Harmonization.

The rapid evolution of digital ecosystems—characterized by multi-cloud infrastructures, IoT proliferation, and distributed data flows—has fundamentally altered the governance, risk, and compliance (GRC) landscape. Traditional GRC frameworks, rooted in periodic audits and reactive controls, are increasingly inadequate for addressing the scale, speed, and sophistication of modern cyber threats. This review paper examines the emergence of AI-driven continuous compliance and threat intelligence models as adaptive solutions for managing GRC in complex digital environments. It synthesizes existing literature on regulatory mapping, continuous auditing, and real-time threat intelligence integration, while identifying key limitations of siloed, manual approaches. The study highlights how artificial intelligence and machine learning can enable proactive risk identification, predictive analytics, and automated remediation, transforming GRC into a continuous and intelligent function. Furthermore, the paper explores technical methodologies such as natural language processing for regulatory interpretation, anomaly detection algorithms for compliance monitoring, and predictive modeling for risk forecasting. By analyzing current advancements, challenges, and research gaps, this review proposes a conceptual framework that positions AI as a catalyst for adaptive, resilient, and future-ready GRC architectures. The findings underscore the critical need for intelligent, real-time governance models to ensure organizational sustainability in the face of regulatory volatility and cyber risk escalation. Keywords: Continuous Compliance, Threat Intelligence, Adaptive GRC, Artificial Intelligence in Governance, Predictive Risk Analysis, Automated Remediation.

Background: Patient engagement (PE) or involvement in research is when patient partners are integrated onto teams and initiatives (not participants in research). A number of health research funding organizations have PE frameworks or rubrics but we are unaware of them applying and reporting on their own PE efforts. We describe our work at the Canadian Institutes of Health Research’s Institute of Musculoskeletal Health and Arthritis (CIHR IMHA) to implement, evaluate and understand the impact of its PE strategy.Methods: The Institute hired a PE specialist (who identifies as a patient partner) to design the strategy, its tactics, and timelines. A Patient Engagement Research Ambassador (PERA) group was convened of eight patient partners who lived with conditions represented by the Institute that meet monthly, co-created a mandate and terms of reference, and set priorities. Evaluating the PE strategy and understanding its impact was a collaboration between an external group and PERA. Results: In addition to convening PERA, the Institute produced a number of outputs (modules, video, publications, webinars, blog) to help in doing PE in research. One major output was a How-To Guide to Patient Engagement in Research entirely driven and designed by PERA. The How-To Guide is a series of free, virtual modules for different audiences used by 1,048 individuals to date. The evaluation and impacts of the PE strategy revealed positive impacts with some areas for improvement.Conclusions: Implementing a PE strategy within CIHR IMHA resulted in several PE activities and outputs with impacts within and beyond the Institute. We provide templates and outputs related to this work that may inform the efforts of other health research funding organizations. We encourage health research funders to move beyond encouraging or requiring PE in funded projects to fully ‘walk the talk’ of PE by implementing and evaluating their own PE strategies.

Whether food source or energy mediates the effect of fructose-containing sugars on blood pressure (BP) is unclear. We conducted a systematic review and meta-analysis of the effect of different food sources of fructose-containing sugars at different levels of energy control on BP. We searched MEDLINE, Embase and the Cochrane Library through June 2021 for controlled trials ≥7-days. We prespecified 4 trial designs: substitution (energy matched substitution of sugars); addition (excess energy from sugars added); subtraction (excess energy from sugars subtracted); and ad libitum (energy from sugars freely replaced). Outcomes were systolic and diastolic BP. Independent reviewers extracted data. GRADE assessed the certainty of evidence. We included 93 reports (147 trial comparisons, N = 5,213) assessing 12 different food sources across 4 energy control levels in adults with and without hypertension or at risk for hypertension. Total fructose-containing sugars had no effect in substitution, subtraction, or ad libitum trials but decreased systolic and diastolic BP in addition trials (P<0.05). There was evidence of interaction/influence by food source: fruit and 100% fruit juice decreased and mixed sources (with sugar-sweetened beverages [SSBs]) increased BP in addition trials and the removal of SSBs (linear dose response gradient) and mixed sources (with SSBs) decreased BP in subtraction trials. The certainty of evidence was generally moderate. Food source and energy control appear to mediate the effect of fructose-containing sugars on BP. The evidence provides a good indication that fruit and 100% fruit juice at low doses (up to or less than the public health threshold of ~10% E) lead to small, but important reductions in BP, while the addition of excess energy of mixed sources (with SSBs) at high doses (up to 23%) leads to moderate increases and their removal or the removal of SSBs alone (up to ~20% E) leads to small, but important decreases in BP in adults with and without hypertension or at risk for hypertension. Trial registration: Clinicaltrials.gov: NCT02716870.

There is an increasing recognition of the importance of patient engagement and involvement in health research, specifically within the field of rheumatology. In general, researchers in this specialty appreciate the value of patients as partners in research. In practice, however, the majority of researchers does not involve patients on their research teams. Many researchers find it difficult to match their needs for patient engagement and the potential contributions from individuals living with rheumatic disease. In this Viewpoint, we provide researchers and patients practical tips for matching ‘supply and demand,’ based on our own experiences as patient engagement consultants and trainers in rheumatology research. All authors started as a ‘naïve’ patient or caregiver, an identity that evolved through a process of ‘adversarial growth’: positive changes that are experienced as a result of the struggle with highly challenging life circumstances. Here, we introduce four stages of adversarial growth in the context of research. We submit that all types of patients have their own experiences, qualities and skills, and can add specific input to research. The recommendations for engagement are not strict directives. They are meant as starting points for discussion or interview. Regardless of individual qualities and knowledge, we believe that all patients engaged in research have a single goal in common: to contribute to research that ultimately will change the lives of many other patients.