1.Single- and double-bundle posterior cruciate ligament reconstruction under arthroscopy: a prospective cohort study
Junhu HOU ; Guiyou WU ; Xishun WANG ; Yadong ZHANG
Chinese Journal of Tissue Engineering Research 2015;19(20):3271-3275
BACKGROUND:Some studies have shown that the double-bundle posterior cruciate ligament reconstruction is not superior to the single-bundle posterior cruciate ligament reconstruction, and stil has some deficiencies difficult to overcome. Which is better, double-bundle reconstruction or single-bundle reconstruction? There is no uniform conclusion. OBJECTIVE:To perform a prospective cohort study on the clinical efficacy and safety of autologous single- and double-bundle posterior cruciate ligament reconstruction. METHODS:Totaly 81 patients with posterior cruciate ligament injury were randomly divided into single-bundle reconstruction group (n=41) and double-bundle reconstruction group (n=40). The knee stability, Lysholm score, Tegner score, hospital stay, operation time, fever days and number of puncture cases were compared between the two groups before and 24 months after reconstruction. RESULTS AND CONCLUSION:Compared with the single-bundle reconstruction group, the knee stability was significantly worse in the double-bundle reconstruction group (F=4.362,P=0.000); the operation time, hospital stay and number of puncture cases were also higher in the double-bundle reconstruction group (P < 0.05). At 24 months after reconstruction, the Lysholm and Tegner scores were both increased significantly in the two groups (P < 0.05), but there was no difference between the two groups (P > 0.05). These findings indicate that both single- and double-bundle reconstruction under arthroscopy is safe and effective treatment for posterior cruciate ligament injury, but the double-bundle reconstruction is not recommended as the preferred surgical procedure because of longer time and larger trauma.
2.Research progress of stochastic resonance in neural models.
Xiaobing LIANG ; Xishun LIU ; Anzhi LIU ; Boliang WANG
Journal of Biomedical Engineering 2009;26(4):912-916
In nonlinear systems, noise can improve the responses of the systems with appropriate noise intensity. This phenomenon is called stochastic resonance. Biological neural systems are noisy and stochastic resonance has been found in them experimentally and theoretically. Now many researches focus on the signal transmission and processing in neural models. So this paper introduces the researches of stochastic resonance in noisy neural models. Then the recent research achievement and progress are reviewed in the following three aspects: noise; the development of stochastic resonance; and neural network. At last, the foreground of the study is discussed.
Humans
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Models, Neurological
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Neurons
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physiology
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Nonlinear Dynamics
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Signal Transduction
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Stochastic Processes
3.The effects of high frequency signal investigated in a neuron model.
Xiaobing LIANG ; Xishun LIU ; Anzhi LIU ; Boliang WANG
Journal of Biomedical Engineering 2009;26(6):1241-1245
We have investigated the effects of high frequency (HF) signal on firing activity in a biologically realistic system--the noisy Hodgkin-Huxley (HH) neuron model via numerical simulations. The results show that when the HF amplitude to frequency ratio (AFR) increases, the firing rate is diminished and stochastic resonance disappears, even the HH neuron model is processing a stimulus of its most sensitive frequency. When the noise intensity is strong, the vibration resonance can be observed. Moreover, the fluctuation around the resting potential will be replaced by an oscillation of the same high frequency with the increasing AFR. The inhibition of the firing activity is consistent with the results of experiment in vivo that HF current can stop the transmission of action potential in peripheral nerve. This study is of functional significance to the biomedical research on the damages caused by electro-pollution in vivo and signal processing.
Action Potentials
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Artifacts
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Computer Simulation
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Humans
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Models, Neurological
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Neurons
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physiology
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Stochastic Processes