Objective To supply references to tissue-engineered skin cl inical appl ications with autogenic BMSCs composited collagen membrane to repair swine full-thickness cutaneous deficiency. Methods Twenty mL bone marrow were obtained respectively from 4 swine, autogenic BMSCs were cultured and passed to the 3rd passage. The fresh bovine tendontreated by means of chemically cross-l inked was made 5 cm diameter collagen I (Col I) membrane. The 2 × 107/mL P3 swine autogenic BMSCs labeled DAPI were planted to sterile Col I membrane for 24 hours incubation, then the tissue-engineered skin was constructed. The five full-thickness skin defect of 5 cm diameter was excised to the muscle from forward to backward on the back midl ine two sides of swine. The tissue-engineered skin were implanted in the experimental group, while Col I membrane was implanted in control group. After 3 and 8 weeks of implantation, the two swine wound surface heal ing circumstance was observed and further evaluated with histology analysis and TEM. After 3 weeks of implantation, the experimental group were observed with fluorescence microscopy and staining for glycogen. Results After 3 weeks of implantation, the wound surface of control group were observed nigrescence, scab and putrescence, and after 8 weeks of implantation, also evident putrescence and scar. The wound surface of experiment group was al ive after 3 weeks implantation, appearance was leveled off and flexible without evident scar. The wound surface recovered well after 8 weeks of implantation, wound surface heal ing rate was significantly difference between the two groups (P lt; 0.01). After 3 weeks of implantation, control group were observed acestoma hyperplasia and no epidermal coverage by histology analysis. The experimental group was showed integrity epidermis and dermis structure. The basal layer was crimson and continuously positive with glycogen staining. After 8 weeks of implantation, the experimental group and control group were emerged normal skin structure. After 3 weeks of implantation in control group, a lot of neutrophil ic granulocytes and fibroblasts were noticed, but no epidermal structure was observed under TEM. In the experimental group, a lot of epidermal cells were observed, dermatome connection among epidermal cells and hemidermosome connection between basilar membrane cells and basal membrane were observed in epidermis. In the dermis experimental group, blood capillary endothel ial cells were noticed. Furthermore, considerable collagen fiber deposit was found in the surrounding tissue of fibroblasts. After 3 weeks of implantation, BMSCs labeled with DAPI were located reconstructed epidermal basement membrane and dermis by fluorescence microscopy. Conclusion Tissue-engineered skin which is composited with autogenic BMSCs as seed cells and collagen membrane were potential prospects in appl ication of repairing swine full-thickness cutaneous deficiency.
Finite element (FE) model of thorax with high biofidelity is one of the most important methods to investigate thoracic injury mechanism because of the absence of pediatric cadaver experiments. Based on the validated thorax finite element model, the FE models with equivalent muscles and real geometric muscles were developed respectively, and the effect of muscle biofidelity on thoracic injury was analyzed with reconstructing pediatric cadaver thorax impact experiments. The simulation results showed that the thoracic impact force, the maximum displacement and the maximum von-Mises stress of FE models with equivalent muscles were slightly greater than those from FE models with real geometric muscles, and the maximum principal strains of heart and lung were a little lower. And the correlation coefficient between cadaver corridor and FE model with real muscles was also greater than that between cadaver corridor and FE model with equivalent muscles. As a conclusion, the FE models with real geometric muscles can accurately reflect the biomechanical response of thorax during the impact.
The pediatric cadaver impact experiments were reconstructed using the validated finite element(FE) models of the 3-year-old and 6-year-old children. The effect of parameters, such as hammer size, material parameters and thorax anatomical structure characteristics, on the impact mechanical responses of 3-year-old and 6-year-old pediatric thorax was discussed by designing reasonable finite element simulation experiments. The research results showed that the variation of thorax contact peak force for 3-year-old group was far larger than that of 6-year-old group when the child was impacted by hammers with different size, which meant that 3-year-old child was more sensitive to hammer size. The mechanical properties of thoracic organs had little influence on the thorax injury because of the small difference between 3-year-old and 6-year-old child in this research. During the impact, rib deformation led to different impact location and deformation of internal organs because the 3-year-old and 6-year-old children had different geometrical anatomical structures, such as different size of internal organs. Therefore, the injury of internal organs in the two groups was obviously different. It is of great significance to develop children finite element models with high biofidelity according to its real anatomical structures.
The finite element method is a new method to study the mechanism of brain injury caused by blunt instruments. But it is not easy to be applied because of its technology barrier of time-consuming and strong professionalism. In this study, a rapid and quantitative evaluation method was investigated to analyze the craniocerebral injury induced by blunt sticks based on convolutional neural network and finite element method. The velocity curve of stick struck and the maximum principal strain of brain tissue (cerebrum, corpus callosum, cerebellum and brainstem) from the finite element simulation were used as the input and output parameters of the convolutional neural network The convolutional neural network was trained and optimized by using the 10-fold cross-validation method. The Mean Absolute Error (MAE), Mean Square Error (MSE), and Goodness of Fit (R2) of the finally selected convolutional neural network model for the prediction of the maximum principal strain of the cerebrum were 0.084, 0.014, and 0.92, respectively. The predicted results of the maximum principal strain of the corpus callosum were 0.062, 0.007, 0.90, respectively. The predicted results of the maximum principal strain of the cerebellum and brainstem were 0.075, 0.011, and 0.94, respectively. These results show that the research and development of the deep convolutional neural network can quickly and accurately assess the local brain injury caused by the sticks blow, and have important application value for understanding the quantitative evaluation and the brain injury caused by the sticks struck. At the same time, this technology improves the computational efficiency and can provide a basis reference for transforming the current acceleration-based brain injury research into a focus on local brain injury research.