OBJECTIVE: To investigate the selection and identification of human keratinocyte stem cells(KSC) in vitro. METHODS: According to the characteristics of KSC which can adhere to extracellular matrix very fast, we selected 3 groups of different time(5 minutes, 20 minutes and 60 minutes) and unselected as control group. And the cells were identified by monoclone antibody of beta 1-integrin and cytokeratin 19 (Ck19), then the image analysis was done. Furthermore we analyzed the cultured cells with flow cytometer(FCM) and observed the ultrastructure of the cell by transmission electron microscope(TEM). RESULTS: The cell clones formed in all groups after 10 to 14 days, while the cells of 5 minute group grew more slowly than those of the other groups, however, the clones of this group were bigger. The expression of beta 1-integrin and Ck19 were found in all groups. The positive rate of beta 1-integrin was significant difference between 5 minute group and the other groups (P lt; 0.05). And the expression of Ck19 was no significant difference between 5 minute group and 20 minute group(P gt; 0.05), and between 60 minute group and control group. But significant difference was observed between the former and the later groups(P lt; 0.05). The result of FCM showed that most cells of the 5 minute group lied in G1 period of cell cycle, which was different from those of the other groups. At the same time, the cells of 5 minute group were smaller and contained fewer organelles than those of the other groups. CONCLUSION: The above results demonstrate that the cells of 5 minute group have a slow cell cycle, characteristics of immaturity, and behaving like clonogenic cells in vitro. The cells have the general anticipated properties for KSC. So the KSC can be selected by rapid attachment to extracellular matrix and identified by monoclone antibody of beta 1-integrin and Ck19.
Multi-layer perceptron (MLP) neural network belongs to multi-layer feedforward neural network, and has the ability and characteristics of high intelligence. It can realize the complex nonlinear mapping by its own learning through the network. Bipolar disorder is a serious mental illness with high recurrence rate, high self-harm rate and high suicide rate. Most of the onset of the bipolar disorder starts with depressive episode, which can be easily misdiagnosed as unipolar depression and lead to a delayed treatment so as to influence the prognosis. The early identification of bipolar disorder is of great importance for patients with bipolar disorder. Due to the fact that the process of early identification of bipolar disorder is nonlinear, we in this paper discuss the MLP neural network application in early identification of bipolar disorder. This study covered 250 cases, including 143 cases with recurrent depression and 107 cases with bipolar disorder, and clinical features were statistically analyzed between the two groups. A total of 42 variables with significant differences were screened as the input variables of the neural network. Part of the samples were randomly selected as the learning sample, and the other as the test sample. By choosing different neural network structures, all results of the identification of bipolar disorder were relatively good, which showed that MLP neural network could be used in the early identification of bipolar disorder.
ObjectiveTo compare the biological features of early and late endothelial progenitor cells (EPCs) by isolating and culturing early and late EPCs from the human peripheral blood so as to find some unique properties of EPCs and to propose a suitable strategy for EPCs identification. MethodsMononuclear cells were isolated from the human peripheral blood using density gradient centrifugation. Then, the cells were inoculated in human fibronectin-coated culture flasks and cultured in endothelial cell basal medium 2. After 4-7 days and 2-3 weeks culture, early and late EPCs were obtained respectively. The morphology, proliferation potential, surface markers, cytokine secretion, angiogenic ability, and nitric oxide (NO) release were compared between 2 types of EPCs. Meanwhile, the human aortic endothelial cells (HAECs) were used as positive control. ResultsThe morphology of early and late EPCs was different:early EPCs formed a cell cluster with a spindle shape after 4-7 days of culture, and late EPCs showed a cobblestone appearance. Late EPCs were characterized by high proliferation potential and were able to form capillary tubes on Matrigel, but early EPCs did not have this feature. Both types EPCs could ingest acetylated low density lipoprotein and combine with ulex europaeus Ⅰ. Flow cytometry analysis showed that early EPCs did not express CD34 and CD133, but expressed the CD14 and CD45 of the hematopoietic stem cell markers;however, late EPCs expressed CD31 and CD34 of the endothelial cell markers, but did not express CD14, CD45, and CD133. By RT-PCR analysis, the expressions of vascular endothelial growth receptor 2 and vascular endothelial cadherin in early EPCs were significantly lower than those in the late EPCs and HAECs (P<0.05), but no significant difference was found in the expression of von Willebrand factor and endothelial nitric oxide synthase (eNOS) between 2 type EPCs (P>0.05). The concentrations of vascular endothelial growth factor, granulocyte colony-stimulating factor, and interleukin 8 were significantly higher in the supernatant of early EPCs than late EPCs (P<0.05). Western blot assay indicated eNOS expressed in both types EPCs, while the expression of eNOS in late EPCs was significantly higher than early EPCs at 5 weeks (P<0.05). Both cell types could produce similar amount of NO (P>0.05). ConclusionThe expression of eNOS and the production of NO could be used as common biological features to identify EPCs, and the strategy of a combination of multiple methods for EPCs identification is more feasible.
Intravascular ultrasound (IVUS) is widely used in coronary artery examination. Ultrasonic elastography combined with IVUS is very conspicuous in identifying plaque component and in detecting plaque vulnerability degree. In this study, a simulation model of the blood vessel based on finite element analysis (FEA) was established. The vessel walls generally have radial changes caused by different intravascular pressure. The signals at lower pressures were used as the pre-deformation data and the signals at higher pressure were used as the post-deformation data. Displacement distribution was constructed using the time-domain cross-correlation method, and then strain images. By comparison of elastograms under different pressures, we obtained the optimal pressure step. Furthermore, on the basis of the obtained optimize pressure step, the simulation results showed that this method could effectively distinguish characteristics between different component plaques, and could guide the later experiments and clinical applications.
Delirium is a common complication in elderly inpatients which could result in cognitive impairment, and increase the risk of disability, fall and mortality. Moreover, it could cause heavy social burden. Even with multiple bedside screening scales to detect delirium, the rate of missed diagnosis is still high. Maybe it is associated with the acute fluctuation and nocturnal onset of delirium. With the development of the intelligence and automation of the electronic medical record (EMR), previous studies have explored the use of EMR to identify delirium patients, and this method provides help for delirium diagnosis and prevention. In this paper, we reviewed and summarized the current situation of research on delirium recognition by EMR, and put forward the development prospect in this method in order to provide basis and lay a foundation for intelligent diagnosis of delirium.
ObjectiveWe constructed a real-world evidence evaluation system to provide reference for obtaining high-quality evidence in evidence-based medicine.MethodsThrough the investigation and analysis of the key factors influencing the real-world research evidence, combined with domestic and foreign literature and evaluation tools, we preliminarily constructed the indicators of the real-world evidence evaluation system, then consulted experts in related fields by the Delphi method, modified and determined the final evaluation indicators. ResultsThe indicators of the final real-world evidence evaluation system included 40 items. The recovery efficiencies of the two rounds of expert consultation were 88.2% and 100%; The expert coordination coefficients were 0.174 (P<0.001) and 0.189 (P<0.001). After the second round of consultation, the mean of Likert scale in the range of 3.73~4.93, and the coefficient of variation varied in the range of 0.05~0.21. ConclusionThe real-world evidence evaluation system constructed in this study has certain reliability and scientificity, which can provide a basis and help for the transformation of real-world research into high-quality evidence.
ObjectiveTo summarize the latest progress of parathyroid gland identification in thyroid surgery, and to provide some reference for improving the clinical efficacy.MethodThe literatures about the identification of parathyroid gland in thyroid surgery in recent years were collected to make an review.ResultsThere were many methods for identifying parathyroid gland in thyroid surgery, such as naked eye identification method, intraoperative frozen section, intraoperative staining identification method, intraoperative optical identification method, intraoperative parathyroid hormone assay, γ-detector, and histological identification, each method had its own advantages and disadvantages.ConclusionThe identification of parathyroid gland does not only depend on a certain method, but also require surgeons to enhance their ability to distinguish parathyroid gland.
Forced oscillation technique (FOT) is an active method to test pulmonary function, which can derive the mechanical characteristics of the respiratory system with liner system identification theory by pushing in an oscillation air signal and measuring the changes of output pressure and flow. A pulmonary function determination system was developed based on the FOT in this paper. Several critical technologies of this determination system were analyzed, including the selection criteria of oscillation air generator, pressure and flow sensor, the signal design of oscillation air generator, and the synchronous sampling of pressure and flow data. A software program on LabVIEW platform was set up to control the determination system and get the measuring data. The performance of sensors and oscillation air generator was verified. According to the frequency response curve of the pressure, the amplitude of driving signal to the oscillation air generator was corrected at the frequency range between 4~40 Hz. A simulation experiment was carried out to measure the respiratory impedance of the active model lung ASL5000 and the results were close to the setting values of the model lung. The experiment testified that the pulmonary function determination system based on FOT had performance good enough to provide a tool for the in-depth research of the mechanical properties of the respiratory system.
Based on bioelectrical impedance theory and pattern recognition algorithm, we in this study measured varieties of people's bioelectrical impedance in hands and identified different people according to their bioelectrical impedance. We designed a bioelectrical impedance collection circuit with AD5933 chip to measure the impedance in different people's hands, and we obtained the bioelectrical impedance spectrum for each person under 1-100 kHz electrical stimulation. We calculated the segmentation slopes of bioelectrical impedance spectrum, and took the slopes as characteristic parameters. In order to promote the recognition rate and prevent the overfitting of the model, we divided the people into the training set and the test set, and designed a 3 layer back propagation neural network model to train and test the samples. The results showed that back propagation neural network model could identify the test set effectively. The recognition rate of the training sets was as high as 97.62%, recognition rate of validation sets was 88.79%, recognition rate of test sets was 86.34%, and the synthetical recognition rate was 94.22%. It gives a clue that the network can perfectly recognize people in the training network as well as strangers that comes from the outside of the tests. Our work can verify the feasibility and reliability of using bioelectrical impedance and pattern recognition algorithm for identification, and can provide a simple and supplementary way to identify people.
In order to develop safe training intensity and training methods for the passive balance rehabilitation training system, we propose in this paper a mathematical model for human standing balance adjustment based on T-S fuzzy identification method. This model takes the acceleration of a multidimensional motion platform as its inputs, and human joint angles as its outputs. We used the artificial bee colony optimization algorithm to improve fuzzy C-means clustering algorithm, which enhanced the efficiency of the identification for antecedent parameters. Through some experiments, the data of 9 testees were collected, which were used for model training and model results validation. With the mean square error and cross-correlation between the simulation data and measured data, we concluded that the model was accurate and reasonable.