国家国防医学中心

Albumin, a highly biocompatible protein, has been utilized in the construction of anti-cancer drugs for the treatment of various cancers. However, albumin-based nanomedicines still face several clinical challenges, including low stability in the bloodstream, non-specific targeting ability, and lack of controlled release capability. To address these limitations, we developed a multiprotein-based nanomedicine for targeted chemotherapy of non-small cell lung cancer (NSCLC). This nanomedicine was assembled using a heart-shaped albumin and two Y-shaped anti-CD44 and anti-CD133 antibodies. To enhance stability and improve therapeutic efficacy, the multiproteins were crosslinked using disulfide bond linkers and loaded with paclitaxel and ceramide. This resulted in nanomedicines that exhibited responsiveness to glutathione (GSH) and demonstrated inhibition of tumor cancer cells and cancer stem cells (CSCs). Our experimental results indicated that the inclusion of anti-CD44 and anti-CD133 antibodies enhanced the targeting capability of nanomedicines towards cancer cells and CSCs in an in vitro study and the accumulation in both normoxic regions and hypoxic niches in in vivo tumor xenografts. The multiprotein-based nanomedicines also demonstrated GSH-dependent drug release behavior, induced apoptosis in cancer cells and CSCs, inhibited cell migration, and effectively suppressed NSCLC tumor growth. Overall, our findings presented a novel nanostructure created from differently shaped proteins for application in drug delivery. This multiprotein-based nanomedicines showed promising potential in addressing the limitations of albumin-based nanomedicines and may offer improved therapeutic outcomes for cancer treatment.

Large bone defects and related diseases affect millions worldwide, while conventional treatments such as autologous grafting remain limited by donor supply and high complication rates. Bone tissue engineering (BTE) has therefore emerged as a potential strategy to complement existing therapies. Over the past four decades, BTE has progressed from simple scaffold-based methods toward advanced bone-on-a-chip (BOC) systems. Despite substantial advances, current in vitro models still fail to fully capture the complexity of the bone microenvironment, hindering preclinical future translation. This study traces the evolution of BTE (1980-present) across four phases (foundational, expansion, foundation refinement, and advanced regeneration phase), highlighting innovations in biomaterials, cellular strategies, signaling factors, and culture platforms, which integrate multicellular cocultures, real-time biosensing, and physiologically relevant mechanical cues. BOC not only represents a major step forward for regenerative medicine but also provides versatile applications in disease modeling and drug screening. Nonetheless, three critical challenges remain: standardized design, complete vascularization, and scalable manufacturing. With such advances, BOC may reduce reliance on animal testing, accelerate drug discovery, and enable bone therapies within the near future. The integration of advanced BTE platforms could significantly expand the impact of regenerative medicine worldwide. Interestingly, integration of biosensors with BOC and BTE platforms holds the potential to advance real-time monitoring, increase translational relevance, and assist the development of personalized regenerative strategies in future clinical applications.

Artificial intelligence-enabled electrocardiography (AI-ECG) has rapidly advanced from experimental models to clinically deployed tools. This review outlines the evolution of AI-ECG across key domains including arrhythmia detection, structural heart disease diagnosis, and digital biomarker development. We summarize recent evidence from pragmatic randomized trials and prospective cohort studies that demonstrate the real-world utility of this approach across diverse populations and care settings. AI-ECG has demonstrated consistent accuracy in identifying conditions such as left ventricular systolic dysfunction, hypertrophic cardiomyopathy, and atrial fibrillation, with some studies reporting improved diagnostic rates, earlier intervention, and selected settings, reduced mortality. In addition to diagnostic support, AI-ECG enables longitudinal risk monitoring and screening for systemic diseases. Despite these advances, challenges remain around model generalizability, workflow integration, and regulatory adaptation. This review highlights both the clinical promise and the implementation hurdles of AI-ECG, underscoring the need for rigorous validation and thoughtful deployment to ensure its safe and effective integration into routine care.

Oronasal fistulas in felines present significant clinical challenges and complicate treatment planning and intervention. This report investigates the application of the virtual twin concept and advanced digital technologies, specifically 3-dimensional (3D) scanning, computer-aided design (CAD), and additive manufacturing techniques, toward the development of a custom palatal obturator for managing a chronic oronasal fistula in an 18-month-old male cat. Despite more than 3 prior surgical attempts, the fistula remained unresolved. The approach involved the CAD of the palatal obturator. This involved creation of a 3D model of the palatal lesion, enabling precise simulation, planning, and customization of the obturator. After placing the obturator, complete remission of clinical symptoms indicated a significant improvement in quality of life. The techniques described demonstrate considerable efficacy in closing palatal defects. The integration of virtual twin modeling and 3D technologies provides a minimally invasive treatment option, offering a promising alternative to the surgical approaches by enhancing visualization, design precision, and procedural predictability.

Background As healthcare increasingly evolves into a complex sociotechnical system, clinical decision-making is no longer an individual cognitive task but a dynamic governance process involving patients, families, interprofessional teams, healthcare organizations, and artificial intelligence (AI). Although competency-based medical education (CBME) emphasizes communication, collaboration, professionalism, and reflective practice, existing educational frameworks conceptualize these competencies as parallel attributes rather than explaining how they interact to support high-quality clinical decision-making. This theoretical fragmentation limits the ability of medical education to prepare future clinicians for increasingly complex decision environments. To address this gap, this study introduces the Medical Decision Governance Competency Theory (MDGCT) , a novel competency-based educational framework that conceptualizes governance capability as an integrated developmental competency emerging through the progressive interaction of empathy, communication, collaboration, reflective practice, and adaptive problem-solving. Methods A longitudinal mixed-method educational study was conducted among undergraduate medical and nursing students participating in a three-day Dynamic Patient–Physician Communication and Conflict Management Curriculum. Participants completed validated assessments of empathy, communication attitudes, interprofessional collaboration, metacognitive reflection, and adaptive problem-solving at baseline, immediately after the intervention, and during follow-up. Quantitative findings were triangulated with thematic analysis of reflective learning records to examine the theoretical structure proposed by MDGCT. Results The curriculum produced significant improvements across all governance-related competencies, including empathy, communication attitudes, collaboration, reflective practice, and adaptive problem-solving. Although some competencies showed partial attenuation during follow-up, most remained significantly above baseline, indicating sustained educational development. Nursing students demonstrated stronger long-term retention of empathy than medical students. The convergence of longitudinal quantitative outcomes and qualitative reflections provided initial empirical support for the developmental pathway proposed by MDGCT: Empathy → Communication → Collaboration → Reflective Practice → Adaptive Problem-Solving → Medical Decision Governance . Conclusions This study extends competency-based medical education by proposing governance capability as a distinct educational outcome rather than viewing communication as an isolated professional skill. Medical Decision Governance reframes communication education as the educational foundation for governing complex clinical decisions through the integration of interpersonal, cognitive, collaborative, reflective, and adaptive competencies. Beyond introducing a new competency-based theory, this study establishes a conceptual bridge linking medical education, clinical governance, patient autonomy, shared decision-making, patient safety, and trustworthy AI-enabled healthcare. The findings provide initial empirical evidence supporting governance-oriented education as a new paradigm for preparing healthcare professionals to navigate increasingly complex clinical decision-making environments.