高雄医学大学

Gastric cancer (GC) remains a global health burden. While international guidelines share consensus, variations exist in disputed issues. The 2025 Taiwan Consensus and Management Guidelines for Gastric Cancer had just been released. We compared the key recommendations with established international guidelines. A multidisciplinary taskforce addressed key questions and recommendations using modified Delphi method and evidence-based approaches. The comparative review aimed to elucidate similarities and differences among guidelines from Taiwan, Japan, South Korea, China, Europe, and the US. Guidelines converge on absolute endoscopic resection criteria for early GC but differ in extended indications and perioperative approaches for locally advanced disease. Heterogeneity exists in biomarker assessment protocols, cutoff thresholds, and companion diagnostics. For metastatic disease, consensus exists on anti-HER2, anti-VEGF, immunotherapy, and biomarker-driven strategies, though oligo-metastatic definitions and intraperitoneal chemotherapy indications remain controversial. Optimal GC management requires integrating global evidence with regional contexts. The comparative review addresses heterogeneity among international guidelines, which helps harmonize management strategies as therapeutic standards evolve.

Osteoporosis is a major and growing health concern in the Asia-Pacific region, y et it remains widely underdiagnosed and undertreated due to limited access to dual-energy X-ray absorptiometry (DXA) in many areas. Artificial intelligence (AI) offers new opportunities to improve osteoporosis screening and management, but unvalidated tools pose risks of inconsistent care. This consensus was developed to provide regionally harmonized guidance on the safe, effective, and equitable use of AI in osteoporosis care. Purpose The aim of this work was to establish expert consensus recommendations on the role of AI in osteoporosis screening and management in the Asia-Pacific region. Key objectives were to define appropriate applications of AI (e.g., imaging-based bone assessment and fracture risk prediction) and specify minimum standards for validation and reporting, addressing region-specific implementation challenges and ensuring that AI use aligns with clinical guidelines and ethical principles. Methods This consensus was developed through multidisciplinary collaboration among experts across the Asia-Pacific region. Each participant reviewed draft statements, contributed feedback during virtual meetings, and provided insights based on clinical experience and current evidence. Consensus was reached iteratively until full agreement was achieved for all statements. The process integrated global best practices and regional adaptations, drawing from peer-reviewed studies, international AI guidelines, and local fracture registry data. The final recommendations emphasize the validation, transparency, and ethical implementation of AI within regional healthcare systems, ensuring compatibility with local regulations. Ultimately, twelve consensus statements were established to guide the responsible use of AI for osteoporosis screening and management in the Asia-Pacific region. Results The panel produced 12 consensus statements covering the role of AI as an adjunct for opportunistic osteoporosis screening rather than a diagnostic tool, requirements for imaging quality and AI model transparency, standards for validation and performance reporting, integration of AI with clinical risk stratification, demonstration of clinical utility in real-world settings, adherence to data protection laws and ethical AI principles, training of clinicians in AI use, strategies for implementation and monitoring (including post-market surveillance and feedback loops), and recognition of technical, clinical, and equity limitations of AI. All 12 statements give extensive recommendations for using AI to improve osteoporosis management while ensuring patient safety, accuracy, and equity. Conclusion This first Asia-Pacific consensus on AI in osteoporosis concludes that AI, when appropriately validated and implemented, can help bridge the osteoporosis care gap by identifying high-risk patients who would otherwise remain undiagnosed, thus facilitating earlier intervention. It emphasizes that AI should complement-not replace-standard diagnostic methods and clinical judgment. The guidance emphasizes validation, transparency, and ethical oversight to facilitate early intervention while minimizing risks associated with unvalidated or premature AI adoption.

Progression of disease within 24 months (POD24) identifies high-risk follicular lymphoma (FL) patients with poor outcomes. The prognostic role of baseline 18 F-FDG PET/CT maximum standardized uptake value (SUVmax) in follicular lymphoma (FL) remains uncertain, especially in Asian populations. We examined the association between baseline SUVmax and POD24, as well as its prognostic relevance for progression-free survival (PFS) in newly diagnosed FL patients. We retrospectively analyzed 119 newly diagnosed FL patients who underwent baseline 18 F-FDG PET/CT at a single institution between 2012 and 2024. Whole-body SUVmax (the highest SUVmax across all lesions) and site-specific SUVmax values were recorded. Clinical variables, including baseline laboratory parameters and histologic grade, were collected. Receiver operating characteristic (ROC) analysis was used to explore an optimal SUVmax cutoff for POD24. Associations with POD24 and PFS were evaluated using Kaplan-Meier analysis and Cox proportional hazards models. The median PFS was 54.3 months, and 24.4

Dobutamine (Dobu) is a widely utilized therapeutic agent for heart failure with emerging potential in oncology. However, its clinical application is constrained by rapid release, short half-life and associated toxicity. This study introduces a dual-polymer nanomicelle system employing Pluronic F127 and lignin to encapsulate Dobu, aiming to enhance its release profile, minimize cytotoxicity and improve therapeutic efficacy. Nanomicelles were synthesized using an oil-in-water emulsion method, achieving encapsulation efficiencies of 99% for F127 and 80% for lignin, along with a controlled drug release profile. This novel dual-polymer nanomicelle system achieves high encapsulation efficiency (99% for F127 versus 80% for lignin) and controlled release, significantly reducing the burst release observed with free Dobutamine. Release studies demonstrated that F127 and lignin nanomicelles significantly reduced burst release compared to free Dobu, extending release durations up to 7 h. Cytotoxicity assays using NIH/3T3 fibroblast cells revealed increased cell viability for encapsulated Dobu, with F127 nanomicelles showing superior biocompatibility. Additionally, in vitro antibacterial evaluations confirmed that the nanomicelles did not inhibit microbial growth, highlighting their suitability for sterile applications. These findings suggest that F127 and lignin-based nanomicelles provide a promising platform for safer, controlled Dobu delivery in cardiovascular and oncological therapies.

INTRODUCTION:Artificial intelligence (AI) and digital pathology have the potential to augment liver biopsy interpretation in MAFLD in clinical practice and trials assessment. However, attitudes and barriers to its implementation have not been systematically explored. METHODS:A survey focusing on conventional liver histology, digital pathology and its AI applications in MAFLD/MASH was conducted among hepatologists and liver pathologists in the Asia Pacific region. RESULTS:AI-assisted digital pathology is perceived to be a valuable addition to existing histological reporting in MAFLD/MASH. Defined standards for application and validation of AI models are important priorities for their implementation. CONCLUSION:There is consensus among clinical experts in the Asia Pacific that AI-assisted histological assessment is useful in MAFLD/MASH interpretation. However, there remain important challenges to the adoption of these technologies into routine clinical workflows.