ObjectiveTo investigate the biomechanical characteristics of the proximal tibia (including cortical bone, cancellous bone, and bone cement) after lateral unicompartmental knee arthroplasty (L-UKA) under conditions of normal bone mass, osteopenia, and osteoporosis through finite element analysis of the tibial plateau, and to evaluate the impact of osteoporosis on the risk of postoperative tibial fracture from a biomechanical perspective, focusing on stress, strain, and deformation distribution patterns. MethodsBased on CT data of the tibia from a healthy adult male volunteer, a three-dimensional finite element model of L-UKA was established, including the femoral component, tibial component, ultra-high molecular weight polyethylene insert, bone cement, medial tibial cartilage, and tibia (comprising cortical and cancellous bone). Three groups of bone density parameters were defined: normal bone mass (T-score ≥?1.0SD), osteopenia (T-score –2.5SD-–1.0SD), and osteoporosis (T-score ≤–2.5SD). Different bone conditions were simulated by adjusting the elastic modulus of cortical and cancellous bone. Boundary conditions included complete constraint of the distal tibia, application of a 600 N vertical load on the femoral component, and a 400 N vertical load on the medial tibial cartilage to simulate single-leg stance during slow walking. The maximum stress, maximum strain, and maximum deformation of key structures were measured. Results As bone mass decreased, the biomechanical responses of bone and bone cement changed significantly. Specifically, the maximum stress, maximum strain, and maximum deformation of cortical bone and the bone cement layer increased markedly. For cancellous bone, the maximum stress decreased, while the maximum strain and maximum deformation increased. The maximum stress of the insert were similar across the three groups, with minimal variation (<1%); the peak stress was located at the contact area between the insert and the femoral component. ConclusionThe biomechanical risk of tibial fracture after L-UKA significantly increases in patients with osteoporosis, particularly for periprosthetic stress or fragility fractures.
Donor lungs from donation after circulatory death (DCD) are an important supplementary source for expanding the donor pool in lung transplantation. Compared with donor lungs from donation after brain death (DBD), DCD donor lungs undergo hypoventilation, hypoxia, hypoperfusion, circulatory arrest, and functional warm ischemia after withdrawal of life-sustaining treatment. Consequently, varying degrees of pre-preservation injury may already be present before cold flushing and static hypothermic preservation and may further accumulate during preservation, rewarming, and reperfusion. Disruption of mitochondrial homeostasis may serve as a key link between these continuous injury phases and reperfusion vulnerability. The major mechanisms include impaired recovery of oxidative phosphorylation, metabolic reprogramming, increased reactive oxygen species generation, calcium dyshomeostasis, mitochondrial permeability transition pore opening, and dysregulation of mitochondrial dynamics and quality control. These alterations intersect with apoptosis, necroptosis, pyroptosis, ferroptosis, and other lytic cellular injury phenotypes. In recent years, controlled hypothermic storage at 10°C, ex vivo lung perfusion-based assessment and repair, optimization of procurement workflows, and metabolic interventions have provided new directions for DCD donor lung preservation and utilization. However, a substantial proportion of the available mechanistic evidence is derived from general donor lungs, marginal donor lungs, or models of lung ischemia-reperfusion injury and cannot be directly extrapolated to DCD lungs with different injury burdens. This review summarizes the continuous process of DCD donor lung preservation injury, mitochondrial homeostasis disruption, related cellular injury phenotypes, and preservation and dynamic assessment strategies. Evidence directly derived from DCD lungs is distinguished from lung transplantation-related evidence and cross-organ mechanistic references. These findings may inform donor-lung protection and optimization of lung transplantation workflows involving DCD donors.
In recent years, the TRIPOD 2015 statement has shown significant limitations with the gradual application of machine learning methods in the development and evaluation of clinical prediction models. Therefore, TRIPOD 2015 statement has been updated in 2024 as the TRIPOD+AI statement entitled "TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods", aiming to promote the complete, accurate, and transparent reporting of studies that develop a prediction model or evaluate its performance. This article interprets the key contents and items of the TRIPOD+AI in order to provide aids for clinical researchers.
