【摘要】：隨著現(xiàn)代交通、建筑、工業(yè)的迅猛發(fā)展,高能量損傷患者數量在外傷患者中所占比例逐年增加,脊髓損傷(Spinal Cord Injury,SCI)的發(fā)生率呈逐年上升趨勢。脊髓損傷是骨科領域常見的疾病之一,且具有高發(fā)性,高致殘率和多損傷嚴重等特點,所帶來的后果可能是災難性的,輕者喪失勞動能力,重者不能活動、小便失禁、喪失生活自理能力。據統(tǒng)計分析發(fā)達國家的SCI發(fā)病率為28.3～45人/百萬人/年,我國發(fā)病率6.7人/百萬人/年。損傷發(fā)生后患者的神經功能恢復率極低,因此對于脊髓損傷的后續(xù)治療,如何提高治愈率是現(xiàn)在醫(yī)學界面臨的難題之一,也是重點研究領域之一。目前針對脊髓損傷已開展大量的研究,但其修復主要面臨的問題是:神經元的凋亡,軸突再生能力差以及損傷所造成的抑制性微環(huán)境。而隨著研究的深入,尤其是細胞移植研究結果表明,細胞移植可以促進軸突的再生和髓鞘化,進而替代凋亡的神經細胞,從而促進脊髓損傷的修復,為感覺及運動功能的恢復創(chuàng)造條件。對于細胞移植治療脊髓損傷而言,如何提高細胞的活力和分化能力是移植成功的關鍵。而現(xiàn)有的研究表明,低強度脈沖超聲波(low intensity pulsed ultrasound stimulation, LIPUS)干預種子細胞能夠明顯的提高細胞的增殖和分化能力。然而怎樣選取合適的干預參數使得治療效果最佳是難題之一。對于多目標尋優(yōu),基于遺傳算法改進的BP神經網絡能夠通過全局搜索,得出最優(yōu)解。本實驗首先搭建一套超聲激勵系統(tǒng),包括信號發(fā)生器、放大器、電源和檢測模塊。其中放大器模塊采用THS4062芯片搭建而成的。然后通過醫(yī)學實驗得出實驗結果。將BP神經網絡與遺傳算法相結合,利用MATLAB軟件對實驗結果進行預測仿真分析,得出最佳實驗條件,即對BMSCs的增殖、活性影響最佳激勵參數的組合。且經過驗證實驗,證明預測結果的正確性,為后期的移植實驗做準備。本文基于低強度脈沖超聲波對骨髓間充質干細胞的生物學效應,利用不同參數組合的LIPUS刺激細胞,并通過采取基于遺傳算法改進的BP網絡模型進行預測。通過對神經網絡計算出的實驗條件進行驗證實驗,得出此時的細胞活性值與比對照組提高了 24.9%,效果顯著,且預測的最佳條件下得到的細胞活性值優(yōu)于其他預設實驗條件下所得到的。
[Abstract]:With the rapid development of modern traffic, construction and industry, the proportion of high-energy injury patients in trauma patients increased year by year, and the incidence of spinal cord injury (Spinal Cord Injury,SCI) increased year by year. Spinal cord injury is one of the most common diseases in orthopedic field, and it has the characteristics of high incidence, high disability rate and severe injury. The result may be disastrous, the light person is incapacitated to work, the heavy person is unable to move, the urine is incontinence, and so on. Loss of ability to take care of oneself. According to statistics and analysis, the incidence of SCI in developed countries is 28.30.45 people / million people / year, and the incidence rate in China is 6.7 people / million people / year. The recovery rate of neurological function in patients with spinal cord injury is very low. Therefore, how to improve the cure rate of spinal cord injury is one of the difficult problems in the medical field and one of the key research fields. At present, a lot of researches have been carried out on spinal cord injury, but the main problems in repairing spinal cord injury are neuron apoptosis, poor axon regeneration ability and the inhibitory microenvironment caused by injury. With the development of research, especially the results of cell transplantation, cell transplantation can promote axon regeneration and myelination, and then replace apoptotic nerve cells, thus promoting the repair of spinal cord injury. To create conditions for the recovery of sensory and motor functions. For the treatment of spinal cord injury by cell transplantation, how to improve cell viability and differentiation ability is the key to successful transplantation. However, current studies have shown that low intensity pulsed ultrasound (low intensity pulsed ultrasound stimulation, LIPUS) can significantly improve the proliferation and differentiation of seed cells. However, how to select the appropriate intervention parameters to make the best treatment is one of the problems. For multi-objective optimization, the improved BP neural network based on genetic algorithm can obtain the optimal solution by global search. In this experiment, a set of ultrasonic excitation system including signal generator, amplifier, power supply and detection module is built. The amplifier module is made of THS4062 chip. Then the experimental results are obtained through medical experiments. By combining BP neural network with genetic algorithm, the experimental results are predicted and simulated by MATLAB software, and the optimal experimental conditions are obtained, that is, the combination of the best excitation parameters affecting the proliferation of BMSCs and the activity of BMSCs. The experimental results proved the correctness of the prediction, and prepared for the later transplantation experiment. Based on the biological effects of low-intensity pulsed ultrasound on bone marrow mesenchymal stem cells (BMSCs), different parameter combinations of LIPUS were used to stimulate the cells, and the improved BP neural network model based on genetic algorithm was adopted to predict the effects. According to the experimental conditions calculated by neural network, the results show that the cell activity at this time is 24. 9% higher than that of the control group, and the effect is remarkable. The predicted values of cell activity under the optimal conditions are better than those obtained under other preset experimental conditions.
【學位授予單位】：天津科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R651.2

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