An Observation on the Change of Hemodynamic and Respiratory Mechanical Parameters of Patients with Respiratory Failure / 昆明医科大学学报

Objective To observe and study the changes of hemodynamic and respiratory mechanical parameters of patients with respiratory failure.Methods 75 patients with respiratory failure in our hospital during the time of March 2016 to May 2017 were set as the observation group, while 75 healthy persons with the same age were set as the control group.Then the hemodynamic indexes and respiratory mechanical parameters of the two groups were detected, and the detection results of observation group with different severity degree of disease were compared, and the relationship between the hemodynamic indexes, respiratory mechanical parameters and respiratory failure were analyzed with Logistic analysis. Results The hemodynamic indexes and respiratory mechanical parameters of observation group were all worse than those of the control group (P＜0.05), the hemodynamic indexes and respiratory mechanical parameters of observation group with servere disease were all worse than those of patients with mild and moderate disease, the results of patients with moderate disease were worse than those of patients with mild disease (P＜0.05), and the Logistic analysis showed the hemodynamic indexes and respiratory mechanical parameters all had close relationship to the respiratory failure. Conclusions The change of hemodynamic and respiratory mechanical parameters of patients with respiratory failure are greater, and the detection results of patients with different severity degree of disease have significant differences, so those items of patients with respiratory failure should be monitored and improveed.

Diabetes mellitus affects the biomechanical function of the callus and the expression of TGF-beta1 and BMP2 in an early stage of fracture healing

Transforming growth factor beta 1 (TGF-β1) and bone morphogenetic protein-2 (BMP-2) are important regulators of bone repair and regeneration. In this study, we examined whether TGF-β1 and BMP-2 expressions were delayed during bone healing in type 1 diabetes mellitus. Tibial fractures were created in 95 diabetic and 95 control adult male Wistar rats of 10 weeks of age. At 1, 2, 3, 4, and 5 weeks after fracture induction, five rats were sacrificed from each group. The expressions of TGF-β1 and BMP2 in the fractured tibias were measured by immunohistochemistry and quantitative reverse-transcription polymerase chain reaction, weekly for the first 5 weeks post-fracture. Mechanical parameters (bending rigidity, torsional rigidity, destruction torque) of the healing bones were also assessed at 3, 4, and 5 weeks post-fracture, after the rats were sacrificed. The bending rigidity, torsional rigidity and destruction torque of the two groups increased continuously during the healing process. The diabetes group had lower mean values for bending rigidity, torsional rigidity and destruction torque compared with the control group (P<0.05). TGF-β1 and BMP-2 expression were significantly lower (P<0.05) in the control group than in the diabetes group at postoperative weeks 1, 2, and 3. Peak levels of TGF-β1 and BMP-2 expression were delayed by 1 week in the diabetes group compared with the control group. Our results demonstrate that there was a delayed recovery in the biomechanical function of the fractured bones in diabetic rats. This delay may be associated with a delayed expression of the growth factors TGF-β1 and BMP-2.

Articular cartilage simulation using depth-dependent mechanical parameters / 医用生物力学

Objective To evaluate the difference in articular cartilage simulation due to the application of either homogeneous or depth-dependent mechanical parameters. Method The nonlinear， biphasic， porous model of articular cartilage was built using poroelasticity module in COMSOL. Under the static load, the model was computed using homogeneous and depth dependent parameter, respectively. The difference between the results was analyzed. Results For the total stress of cartilage， there was no remarkable difference between two parameter configurations. However, for the analysis such as solid phase stress, fluid pressure and flow velocity， the difference between two parameter configurations must be considered. Conclusions Different parameter configuration has negligible effect on the total stress of cartilage, but it is influential to the flow velocity. Therefore， homogeneous mechanical parameters should be used in order to simplify the total stress computation problem. The other more detailed analysis should be based on the depth dependent parameters. These conclusions could be referred to for future cartilage modeling and numerical computation and thus laying a foundation for the design and computation of artificial joint.

Intelligent direct analysis of physical and mechanical parameters of tunnel surrounding rock based on adaptive immunity algorithm and BP neural network / 西安交通大学学报·英文版

Because of complexity and non-predictability of the tunnel surrounding rock, the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering. During design, it is a frequent practice, therefore, to give recommended values by analog based on experience. It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying, expressing and coping with such complex non-linear relatiouships. The parameters can be verified by searching the optimal network structure, using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results. In the current paper, the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua (FLAC3D. The high non-linearity, network reasoning and coupling ability of the neural network are employed. The output vector required of the training of the neural network is obtained with the numerical analysis software. And the overall space search is conducted by employing the Adaptive Immunity Algorithm. As a result, we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum. At the same time, the computing speed and efficiency are increased as well. Further, in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project. The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively improved the recommended values in the original prospecting data. This is of practical significance to the appraisal of stability and informatiomzation design of the surrounding rock.