How Does the Stress in the Fixation Device Change during Different Stages of Bone Healing in the Treatment of Fractures? A Finite Element Study of External Fixation for Tibial Fractures.

BACKGROUND: Although the specific relationship between the stress changes in the external fixator during tibial fracture treatment and the bone healing process remains unclear, it is believed that stress variations in the external fixator scaffold can, to a certain extent, reflect the progress of tibial healing. OBJECTIVE: This study aims to propose a non-invasive method for assessing the degree of fracture healing by monitoring the changes in stress transmission, the locations of stress-sensitive points, and displacement in the external fixator-tibia system during the healing process of tibial fractures. METHODS: In this study, finite element models of tibial fractures at various healing stages were developed. Physiological conditions, including axial, torsional, and bending loads on the tibia, were simulated to evaluate stress and strain within the external scaffold-tibia system under normal physiological loading conditions. RESULTS: The results indicate variations in the stress distribution between the external fixator and the tibia during different stages of healing. In the early phase of fracture healing, the external fixator plays a crucial role as the primary load-bearing unit under all three loading conditions. As the fracture healing progresses, the stress on the tibia gradually increases, concentrating on the medial part of the tibia under axial and torsional loading, and at the upper and lower ends, as well as the central part of the anterior and posterior tibia during bending loading. The stress at the callus gradually increases, while micro-movements decrease. The stress within the external bracket gradually decreases, with a tendency for the connecting rod to transfer stress towards the screws. Throughout the fracture healing process, the location of maximum stress in the external fixator remains unchanged. Under axial and torsional loading, the maximum stress is located at the intersection of the lowest screw and the bone cortex, while under bending loading, it is at the intersection of the second screw and the connecting rod. CONCLUSION: During the bone healing process, stress is transferred between the external fixation frame and the bone. As bone healing advances, the stress on the connecting rods and screws of the external fixation frame decreases, and the amplitude of stress changes diminishes. When complete and robust fusion is achieved, stress variations stabilize, and the location of maximum stress on the external fixation frame remains unchanged. The intersections of the lowest screw and the bone cortex, as well as the second screw and the connecting rod, can serve as sensitive points for monitoring the degree of bone healing.

Total glycosides of Rhodiola rosea L. attenuate LPS-induced acute lung injury by inhibiting TLR4/NF-κB pathway.

Acute lung injury (ALI) is a common respiratory disease in clinics, which is characterized by alveolar-capillary membrane loss, plasma protein leakage, pulmonary edema, massive neutrophil infiltration, and the release of proinflammatory cytokines and mediators. Rhodiola rosea L. an adaptogenic plant rich in phenylethanoloids, phenylpropanoids, monoterpenes, has anti-inflammatory and antioxidant effects. We hope to verify the relieving effect of total glycosides of Rhodiola rosea L. (RTG) on ALI in mice and clarify its mechanism through this study. In this study, we identified the effect and mechanism of RTG on ALI through LPS-induced ALI mice. After RTG treatment, the pathological structure of lung tissue in ALI mice induced by LPS was significantly improved, and the infiltration of inflammatory cells was reduced. In addition, RTG reduced the production of IL-6, IL-1ß, and TNF-α in the serum of ALI mice and reduced the content or activity of MPO, T-SOD, GSH, and MDA in lung tissue. RNAseq analysis showed that RTG ameliorated LPS-induced ALI through anti-inflammatory, reduced immune response, and anti-apoptotic activities. The western blotting analysis confirmed that RTG could down-regulate the expression levels of TLR4, MyD88, NF-κB p65, and p-IκBα/IκBα. These results suggest that RTG can attenuate LPS-induced ALI through antioxidants and inhibition of the TLR4/NF-κB pathway.

MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study.

Objectives: To develop and validate an MRI radiomics-based decision support tool for the automated grading of cervical disc degeneration. Methods: The retrospective study included 2,610 cervical disc samples of 435 patients from two hospitals. The cervical magnetic resonance imaging (MRI) analysis of patients confirmed cervical disc degeneration grades using the Pfirrmann grading system. A training set (1,830 samples of 305 patients) and an independent test set (780 samples of 130 patients) were divided for the construction and validation of the machine learning model, respectively. We provided a fine-tuned MedSAM model for automated cervical disc segmentation. Then, we extracted 924 radiomic features from each segmented disc in T1 and T2 MRI modalities. All features were processed and selected using minimum redundancy maximum relevance (mRMR) and multiple machine learning algorithms. Meanwhile, the radiomics models of various machine learning algorithms and MRI images were constructed and compared. Finally, the combined radiomics model was constructed in the training set and validated in the test set. Radiomic feature mapping was provided for auxiliary diagnosis. Results: Of the 2,610 cervical disc samples, 794 (30.4%) were classified as low grade and 1,816 (69.6%) were classified as high grade. The fine-tuned MedSAM model achieved good segmentation performance, with the mean Dice coefficient of 0.93. Higher-order texture features contributed to the dominant force in the diagnostic task (80%). Among various machine learning models, random forest performed better than the other algorithms (p < 0.01), and the T2 MRI radiomics model showed better results than T1 MRI in the diagnostic performance (p < 0.05). The final combined radiomics model had an area under the receiver operating characteristic curve (AUC) of 0.95, an accuracy of 89.51%, a precision of 87.07%, a recall of 98.83%, and an F1 score of 0.93 in the test set, which were all better than those of other models (p < 0.05). Conclusion: The radiomics-based decision support tool using T1 and T2 MRI modalities can be used for cervical disc degeneration grading, facilitating individualized management.