Current Ti‐6Al‐4V bone implants lack trabecular structure and pro‑angiogenic cues, both essential for regeneration. Herein, a dual biomimetic strategy is devised that integrates a 3D‐printed biomimetic trabecular porous Ti‐6Al‐4V scaffold (BTPS) with exosome‐loaded PEGDA/GelMA hydrogel microspheres (PGHExo) designed for sustained release. BTPS is designed using Voronoi algorithms and imaging data, and replicates the geometry and mechanical properties of natural bone. Hypoxia‐induced human umbilical vein endothelial cell (HUVEC) derived exosomes (HExo) are encapsulated in PGHExo microspheres via microfluidic technology, enabling controlled release of HExo, and anchored onto BTPS using polydopamine (pDA) modification (BTPS&pDA@PGHExo). BTPS exhibited an elastic modulus of ≈3.2 GPa and a permeability of 11.52 × 10 ⁻⁸ mm ² , mimicking natural bone. In vitro assays demonstrated that BTPS&pDA@PGHExo significantly enhanced osteogenesis and angiogenesis. mRNA‐Seq analysis suggested that BTPS&pDA@PGHExo regulates osteogenic and angiogenic gene expression through the activation of pathways including MAPK, mTOR, HIF‐1, and VEGF. In vivo, BTPS&pDA@PGHExo improved bone volume, density, and neovascularization in a rabbit model. This dual biomimetic strategy offers a promising clinical solution, addressing the limitations of conventional Ti‐6Al‐4V scaffolds and providing an innovative approach for personalized bone defect repair.

The hemoglobin-to-red blood cell distribution width ratio (HRR) has emerged as a potential predictor of various health outcomes. This study aimed to investigate the association between HRR and all-cause, cancer, and cardiovascular mortality. This cohort study used data from 28,825 participants in the 1999–2018 U.S. National Health and Nutrition Examination Survey. Weighted Cox regression was used to assess the associations between HRR and mortality. Restricted cubic spline (RCS) models evaluated the non-linear associations between HRR and mortality risk. Subgroup and sensitivity analyses were conducted to assess the robustness of the study results. Trend tests assessed the temporal trends of mean HRR. Lower HRR was significantly linked to increased risks of all-cause, cancer, and cardiovascular mortality. According to the fully adjusted model, the highest quintile of HRR (Q5) showed lower mortality risks compared to the lowest quintile (Q1): all-cause mortality (HR 0.47, 95% CI 0.40, 0.55), cancer mortality (HR 0.51, 95% CI 0.37, 0.71), and cardiovascular mortality (HR 0.43, 95% CI 0.32, 0.56). A significant trend effect was observed across HRR quintiles (P for trend < 0.0001). Nonlinear association analyses suggested a linear relationship between HRR and cardiovascular mortality, while “L”-shaped associations were observed for all-cause and cancer mortality. Notably, the mean HRR levels decreased from 1.18 (95% CI 1.16–1.19) in 1999–2000 to 1.07 (95% CI 1.05–1.08) in 2017–2018. An inverse association between HRR and mortality risk was found, with lower HRR levels indicating higher mortality risk. Over the past two decades (1999–2018), there has been a significant decline in HRR levels among U.S. adults. HRR may serve as a valuable and easily obtainable predictor for mortality risk assessment in clinical practice.

The integration of multimodal medical imaging with artificial intelligence (AI) is potentially catalysing a paradigm shift in orthopaedic diagnosis and treatment, moving beyond experience-based practices toward intelligent, data-driven precision medicine. This narrative review synthesizes recent key evidence across imaging modalities and AI frameworks, and highlights the translational gap that persists between algorithmic development and real-world clinical implementation. By combining complementary information from X-ray, CT, MRI, PET, ultrasound, and biochemical data, multimodal AI overcomes the inherent limitations of single-modality approaches, enabling more comprehensive structural, functional, and metabolic assessments. Recent advances demonstrate broad applications, including accurate fracture detection and classification, differentiation of benign and malignant bone tumours, quantitative assessment of osteoarthritis, risk prediction for osteoporosis, and intelligent preoperative planning and intraoperative navigation. Moreover, multimodal AI facilitates efficacy prediction and personalised treatment decision-making, positioning future systems as AI-assisted decision-support tools that support surgeons in surgical strategy, implant design, and long-term follow-up. Nevertheless, significant challenges remain, particularly in data heterogeneity, model generalisation, interpretability, and clinical integration. Progress in constructing standardised multimodal databases, developing self-supervised and multi-task learning strategies, and strengthening ethical–regulatory frameworks will be essential for clinical translation. Ultimately, multimodal AI holds immense potential to transition from laboratory validation to routine practice, delivering safer, more efficient, and precise diagnostic and therapeutic solutions for orthopaedic patients.

Background: The correlation between various insulin resistance surrogates and frailty remains under investigation in the scientific community.Methods: Data from NHANES (1999-2018) were used. We utilized weighted binary logistic regression, trend tests, RCS analysis, and subgroup analysis to comprehensively assess the link between METS-IR, HOMA-IR, and TyG, and frailty risk.Results: The results revealed a significant positive association between high levels of METS-IR, HOMA-IR, and TyG with the risk of frailty in all models. Notably, in model 4, the highest quintile of METS-IR showed the strongest link (OR: 2.960, 95% CI: 2.219-3.949), with HOMA-IR (OR: 2.522, 95% CI: 1.927-3.301) following closely behind. Trend tests revealed a positive trend between METS-IR, HOMA-IR, and TyG with the risk of frailty (P for trend < 0.05). RCS analysis showed a linear relationship between METS-IR and the risk of frailty (P for nonlinearity > 0.05). In contrast, HOMA-IR and TyG exhibited a U-shaped nonlinear relationship (P for nonlinearity < 0.05).Conclusion: The research identified a linear association between METS-IR and frailty risk, whereas HOMA-IR and TyG displayed a U-shaped, nonlinear relationship pattern with the risk of frailty. Among the varying levels examined, the linkage between METS-IR and frailty was most pronounced in the top quintile.

Remnant cholesterol (RC), an emerging cardiovascular risk factor, has garnered increasing attention in atherosclerotic cardiovascular disease (ASCVD) research, though its relationship with arterial stiffness remains incompletely understood. This study investigated the association between RC and estimated pulse wave velocity (ePWV), a reliable marker of arterial stiffness, with the goal of enhancing our understanding of RC's role in cardiovascular risk assessment. This study utilized the National Health and Nutrition Examination Survey (NHANES) data from 1999 to 2018, including 12,505 participants aged 20 years and above. Weighted linear regression, restricted cubic splines (RCS), and subgroup analyses were used to explore the association between RC levels and ePWV. Analysis revealed a significant positive association between RC levels and ePWV, with participants in the highest RC quintile (Q5) exhibiting substantially elevated ePWV compared to those in the lowest quintile (Q1). Notably, RCS analysis demonstrated a non-linear relationship characterized by a saturation effect (p-nonlinear <0.05). Subgroup analyses indicated stronger associations among specific demographic groups, including individuals under 40 years, females, non-Hispanic whites, and those above the poverty level (all interaction p < 0.05). Furthermore, mediation analysis found that various inflammatory markers such as neutrophil count (NEU), neutrophil-to-lymphocyte ratio (NLR), and monocyte-to-lymphocyte ratio (MLR) played a mediating role in this association. Increased arterial stiffness is associated with higher RC levels, demonstrating a saturation effect at elevated concentrations. This association is partially mediated by NLR, MLR, and NEU, and was stronger among younger, females, non-Hispanic whites, and non-impoverished individuals.

The triglyceride-glucose (TyG) index and its related indexes (TyG-BMI, TyG-WC, TyG-WHtR) are effective markers for screening metabolic diseases like insulin resistance (IR). However, few studies have explored the relationship between the TyG and its related indexes with bone density (BMD), osteopenia, and osteoporosis. This is a cross-sectional study that involved 1,303 adults aged 50 years and above from the National Health and Nutrition Examination Survey 2007–2010, and 2013–2014. In the multivariable-adjusted model, linear regression analysis and logistic regression analysis demonstrated that TyG and its related indexes have a significant positive correlation with BMD and a negative correlation with osteopenia/osteoporosis in the femoral neck, lumbar spine, and total hip region. Trend analysis further confirms these associations (p < 0.05). Restricted cubic spline analysis showed a nonlinear relationship between these indexes with BMD and osteopenia/osteoporosis. Sensitivity analyses further confirmed the robustness of these associations. This study reveals the significant and complex correlation between the TyG and its related indexes with BMD and osteoporosis, indicating the potential link between IR and bone health. The TyG and related indexes offer a new perspective for the diagnosis, prevention, and treatment of osteoporosis.