Objective To demonstrate the importance of bioreactor to tissue engineering and to disscuss the direction of bioreactor- researching. Methods Alarge quantity of literature was reviewed, analyzed and summarized. Results Bioreactor has greatly promoted the progress of tissue engineering in heoretics and practice,and has become one of the most important parts of tissue engineering. Conclusion Bioreactor is indispensable for tissue engineering,and it should be developed along integration,automation and intelligentificaton.Its application should run through the whole process of tissue engineering.
OBJECTIVE: To observe the effect of engineered epiphyseal cartilage regenerated in vitro with 3-D scaffold by chondrocytes from epiphyseal plate in repairing the tibial epiphyseal defect, and to explore the methods to promote the confluence between engineered cartilage and epiphyseal plate. METHODS: Chondrocytes were isolated enzymatically from the epiphyseal plates of immature rabbits, and then planted into the tissue culture flasks and cultivated. The first passage chondrocytes were collected and mixed fully with the self-made liquid biological gel at approximately 2.5 x 10(7) cells/ml to form cell-gel fluid. The cell-gel fluid was dropped into the porous calcium polyphosphate fiber/poly-L-lactic acid(CPPf/PLLA)scaffold, and a cell-gel-scaffold complex formed after being solidified. The defect models of 40% upper tibial epiphyseal plate were made in 72 immature rabbits; they were divided into 4 groups: group A(the cell-gel-scaffold complex was transplanted into the defect and the gap filled with chondrocyte-gel fluid), group B (with noncell CPPf/PLLA scaffold), group C(with fat) and group D(with nothing). The changes of roentgenograph, gross and histology were investigated after 2, 4, 6, 8, 12 and 16 weeks of operation. RESULTS: In group A, the typical histological structure of epiphyseal plate derived from the engineered cartilage with a fine integration between host and donor tissues after 2 weeks. The repaired epiphyseal plate had normal histological structure without deformation of tibia after 4 weeks. The early histological change of epiphyseal closure appeared in the repaired area with varus and shortening deformation of the tibia after 8 weeks. The epiphyseal plate was closed in the repaired area with more evident deformation of tibia; the growth function of repaired epiphyseal plate was 43.6% of the normal one. In groups B, C and D, deformation of tibia occurred after 2 weeks; the defect area of epiphyseal plate was completely closed after 4 weeks. The deformation was very severe without growth of the injured epiphyseal plate after 16 weeks, and no significant difference was observed between the three groups. CONCLUSION: Engineered epiphyseal cartilage can repair the epiphyseal defect in the histological structure with partial recovery of the epiphyseal growth capability. Injecting the suspension of fluid chondrocyte-gel into the defects induces a fine integration of host and donor tissues.
Objective To observe the efficiency and biological characteristics in regenerating in vitro tissue-engineered cartilage from epiphyseal chondrocyte-scaffold complex. MethodsThe first passage epiphyseal chondrocytes were collected and mixed with the biological gel-matrix, the chondrocyte-gel fluid wasdropped into the scaffold to form a complex. The complexes were in vitro cultivated. The changes of complexes in morphology and synthesis of collagens type ⅡandtypeⅠ and aggrecan were observed under the gross and the inverted and light microscopes. The sulfate GAG content in complexes was measured by the the modified dimethylmethylene blue method. Results During cultivation, thecomplexes could keep its original shape with the stable homogeneous three-dimensional distribution of chondrocytes，gradually became milk white and translucence with their rigidity increasing. In the 1st week, the chondrocytic lacunae formed in the complexes. After 2 weeks, the complex was gradually reorganized into the mature engineered cartilage with rich collagen typeⅡand aggrecan and typical cartilage histological structure, but with negative immunological staining of collagen typeⅠ. In the 4th week, the engineered cartilage resembled the nature epiphyseal plate in the characteristic of histological structure, and had over 34% of the sulfate GAG content of the natural epiphyseal plate. Conclusion Theepiphyseal chondrocyte-scaffold complex can be reorganized into typical cartilage with the epiphyseallike histological structure, and be fit for repairing the epiphyseal defect. The tissue engineered cartilage cultivated for 1-2 weeks may be a good choice for repairing epiphyseal defect.
Aiming at the problem that the unbalanced distribution of data in sleep electroencephalogram(EEG) signals and poor comfort in the process of polysomnography information collection will reduce the model's classification ability, this paper proposed a sleep state recognition method using single-channel EEG signals (WKCNN-LSTM) based on one-dimensional width kernel convolutional neural networks(WKCNN) and long-short-term memory networks (LSTM). Firstly, the wavelet denoising and synthetic minority over-sampling technique-Tomek link (SMOTE-Tomek) algorithm were used to preprocess the original sleep EEG signals. Secondly, one-dimensional sleep EEG signals were used as the input of the model, and WKCNN was used to extract frequency-domain features and suppress high-frequency noise. Then, the LSTM layer was used to learn the time-domain features. Finally, normalized exponential function was used on the full connection layer to realize sleep state. The experimental results showed that the classification accuracy of the one-dimensional WKCNN-LSTM model was 91.80% in this paper, which was better than that of similar studies in recent years, and the model had good generalization ability. This study improved classification accuracy of single-channel sleep EEG signals that can be easily utilized in portable sleep monitoring devices.
Objective To investigate the therapeutic effectof the modified anterior approach in treatment of the patients with cervicothoracic junction spinal lesions. Methods From September 2000 to January 2005, 23 patients (15 males, 8 females) with spinal lesions in the cervicothoracic junction underwent a standard cervical approach, which was combined with apartial median steotomy and transverse steotomy through the synostosis between the manubrium and body of the sternum to expose the lesion adequately. Among thepatients, 3 had fracture, 7 had dislocation, 6 had tuberculosis, and 7 had tumor. The pathologic change regions was as follows: 2 in the C6-T1 segment, 2in the C6-T2 segment, 3 in the C7-T1 segment, 3 in the T3 segment, 8 in the T1 segment, and 5 in the T2egment. The classification of Frankel were as follows: 2 at grade A, 4 at grade B, 7 at grade C, 4 at grade D, and 6 at grade E. All the patients underwent a radical excision of the affected spinal bone, were given a proper tricortical iliac crest and anterior instrumentation to reconstruct the anterior spinal column, followed by immobilization in a brace for 3-6 months. Results The mean followup period was 30 months (range, 1042 months). Bony fusion was obtained in all the patients.One patient died of pulmonary cancer metastasis 10 months after operation. The nerve function of the spinal cord recovered at different degrees (1 at grade A, None at grade B, 2 at grade C, 10 at grade D, 10 at grade E). Conclusion Ourmodified anterior approach can provide a direct and safe access to the lesions in the region.
The existing automatic sleep staging algorithms have the problems of too many model parameters and long training time, which in turn results in poor sleep staging efficiency. Using a single channel electroencephalogram (EEG) signal, this paper proposed an automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning (TL-SDResNet). Firstly, a total of 30 single-channel (Fpz-Cz) EEG signals from 16 individuals were selected, and after preserving the effective sleep segments, the raw EEG signals were pre-processed using Butterworth filter and continuous wavelet transform to obtain two-dimensional images containing its time-frequency joint features as the input data for the staging model. Then, a ResNet50 pre-trained model trained on a publicly available dataset, the sleep database extension stored in European data format (Sleep-EDFx) was constructed, using a stochastic depth strategy and modifying the output layer to optimize the model structure. Finally, transfer learning was applied to the human sleep process throughout the night. The algorithm in this paper achieved a model staging accuracy of 87.95% after conducting several experiments. Experiments show that TL-SDResNet50 can accomplish fast training of a small amount of EEG data, and the overall effect is better than other staging algorithms and classical algorithms in recent years, which has certain practical value.