[A primary study on simulation of fracture strength based on BP neural networks].

OBJECTIVE: To model the relationship between stimulating stress and fracture strength using BP neural networks, and to provide a theoretical basis for accurate prediction of the rate of fracture healing. METHODS: The bilateral tibiae in New Zealand rabbits were osteotomized and fixed by stress-relaxation plate(SRP) and rigid plate(RP), respectively. The stress shielding rate and bending strength of the healing fractures were measured at 2 to 48 weeks postoperatively. A BP neural network was constructed and trained using the experimental data of the stress-relaxation group. Then the trained network was used for simulation to predict fracture strength of the two groups from the stress at the fracture site. RESULTS: With the input of the data that has been used to train the network, fracture strength similar to those measured in experiment was calculated from the BP neural network. However, poor results were obtained with the input of new data. CONCLUSION: BP neural network can be used to investigate the influence of various factors on fracture healing quantitatively, and to predict the rate of healing. However, the model still needs to be perfected. More experimental or clinical data are needed to train the network

A first order system model of fracture healing.

A first order system model is proposed for simulating the influence of stress stimulation on fracture strength during fracture healing. To validate the model, the diaphyses of bilateral tibiae in 70 New Zealand rabbits were osteotomized and fixed with rigid plates and stress-relaxation plates, respectively. Stress shielding rate and ultimate bending strength of the healing bone were measured at 2 to 48 weeks postoperatively. Ratios of stress stimulation and fracture strength of the healing bone to those of intact bone were taken as the system input and output. The assumed first order system model can approximate the experimental data on fracture strength from the input of stress stimulation over time, both for the rigid plate group and the stress-relaxation plate group, with different system parameters of time constant and gain. The fitting curve indicates that the effect of mechanical stimulus occurs mainly in late stages of healing. First order system can model the stress adaptation process of fracture healing. This approach presents a simple bio-mathematical model of the relationship between stress stimulation and fracture strength, and has the potential to optimize planning of functional exercises and conduct parametric studies.

[Unequal interval jump grey modeling and its application to the spectral analysis of lubricating oil].

Unequal interval jump grey model was built for raw data series with unequal interval and jump trend in this paper. Levenberg-Marquardt arithmetic that belongs to non-linear least-square. estimation was used to recognize the parameters. The model built was used to fit spectrometric analysis values of diesel engine and the fitting precision is good. It is helpful to improve the accuracy and reliability of spectrometric analysis by revising test data after oil change with model parameter.

[Expression of the monoclonal antibody against nucleocapsid antigen of SARS-associated coronavirus in autopsy tissues from SARS patients].

OBJECTIVE: To investigate the presence and distribution of severe acute respiratory syndrome (SARS)-associated coronavirus (SARS-CoV) in autopsy tissues obtained from patients died of SARS. METHODS: Immunohistochemical technique was applied in 4 fatal SARS cases to examine the autopsy tissues including the lungs, spleen, lymph nodes, brain, pituitary, heart, liver, kidney, pancreas, trachea, esophagus, gastrointestinal tract, adrenal glands, parathyroids, skin and bone marrow. RESULTS: Immunohistochemistry identified positive monoclonal antibody against SARS-CoV nuceeocapsid (N) protein in the alveolar epithelium and the infiltrating monocytes or macrophages in the lung, spleen and lymph nodes; the presence of the antibody was also detected in the serous gland epithelium of the trachea/bronchus, squamous epithelium of the esophagus, the gastric parietal cells, the epithelium of the intestinal tract, acidophilic cells in the parathyroids and pituitary, acinus cells in the pancreas, adrenal cortical cells, sweat gland cells, small vessel endothelium, bone marrow promyelocytes, epithelial cells of the distal convoluted tubule of the kidney, brain neurons, and the hepatocytes near the central vein. CONCLUSIONS: A variety of organs and tissues can be infected by SARS-CoV, and the positive expression of SARS-CoV N protein in the epithelial cells of the gastrointestinal tract, the distal convoluted tubule of the kidney and the sweat gland cells is significant for studying the transmission routes of SARS.