BACKGROUND:Currently, bone marrow mesenchymal stem cel s can differentiate into nerve cel s via many approaches. Different methods for inducing bone marrow mesenchymal stem cel s differentiating into nerve cel s have different ratios. OBJECTIVE:To investigate the difference between chemical method and co-culture method to induce the differentiation of rat bone marrow mesenchymal stem cel s into nerve cel s. METHODS:Rat bone marrow mesenchymal stem cel s were isolated and purified using whole bone marrow culture method, and then randomly divided into two groups:chemical group,β-mercaptoethanol was added;co-culture group, co-cultured in a Transwel chamber. RESULTS AND CONCLUSION:Visible protrusions from induced cel s showed radiation growth at 1 week of induced culture, and neuron-specific enolase staining was positive at 2 weeks of culture. Star-like structure of nerve cel s was visible in the co-culture group within 4-5 days of culture, and then more protrusions formed. Meanwhile, the positive rate of neuron-specific enolase was (70.82±2.46)%. After 6-7 days of culture, neuron-like cel s formed and were interconnected in the chemical group;while, the positive rate of neuron-specific enolase was (52.37±1.83)%. These findings suggest that cel microenvironment plays a leading role in the differentiation of bone marrow mesenchymal stem cel s into nerve cel s, and chemical induction method is inferior to the co-culture method.

Objective To explore the correlation of the operation effects of the miorovascular decompression(MVD) and the findings on magnetic resonance tomographic angiography(MRTA) in patients of neurovascular compression of the cranial nerves.Methods Two hundred and twenty three patients treated with the microvascular decompression were analyzed retrospectively.They were grouped and graded according to the vessel compression on the cranial nerves.The compression were grouped as none, moderate and severe, and the operation effects were graded as Ⅰ ( complete relief), Ⅱ ( partial relief) and Ⅲ ( no relief).The operation effects grades were correlated according to the compression groups by Kruskal-Wallis test and the operation effects between each two of the groups were compared using Nemenyi test.P < 0.05 was defined as statistic significant.Results Of the 53 cases of non-compression group, 31 cases were graded as Ⅰ , 13 cases were graded as Ⅱ and 9 cases were graded as Ⅲ, according to the operation-effects of the decompression.Of the 110 cases of moderate group,95 cases were grade as Ⅰ , 11 cases were graded as Ⅱ and 4 cases were graded as Ⅲ.Of the 60 cases of severe group, 48 cases were graded as Ⅰ, 7 cases were graded as Ⅱ and 5 cases were graded as Ⅲ.There were statistic significance among the three groups,where χ2= 16.84 and P <0.05.The mean rank of the non-compression, the moderate and the severe group was 134.21,102.37 and 110.4 ,respectively.The difference of the mean ranks between the non-compression group and the moderate group was 31.84, and between the non-compression and the severe group was 24.17, respectively, where P < 0.05 both.Conclusions There was close relationship between the findings on magnetic resonance tomographic angiography and the operation effects of the MVD.The operation effects of patients with moderate and severe vessel compression were much better than the non-compression group.MRTA is helpful for MVD surgical indication and its prognosis.

Carotid is in a high risk of atherosclerosis due to its special geometric features and complex flow characteristics. Various biomechanical parameters are practical tools for carotid risk assessment. It has beenwidely accepted that oscillatory low shear environment promotes plaque formation. Based on this, more and more biomechanical indexes have been proposed, such as time-average wall shear stress, oscillatory shear index, relative residence time and so on. In this paper, multiple biomechanical parameters were introduced from the perspectives of shear stress and its temporal and spatial variation, turbulence, platelet transport and activation, stress concentration in vascular wall, etc. The development trend of biomechanical parameters related to carotid artery risk assessment was also analyzed, so as to provide the theoretical basis for more comprehensive and rapid carotid risk assessment

Cardiovascular disease is the leading cause of death worldwide, accounting for 48.0% of all deaths in Europe and 34.3% in the United States. Studies have shown that arterial stiffness takes precedence over vascular structural changes and is therefore considered to be an independent predictor of many cardiovascular diseases. At the same time, the characteristics of Korotkoff signal is related to vascular compliance. The purpose of this study is to explore the feasibility of detecting vascular stiffness based on the characteristics of Korotkoff signal. First, the Korotkoff signals of normal and stiff vessels were collected and preprocessed. Then the scattering features of Korotkoff signal were extracted by wavelet scattering network. Next, the long short-term memory (LSTM) network was established as a classification model to classify the normal and stiff vessels according to the scattering features. Finally, the performance of the classification model was evaluated by some parameters, such as accuracy, sensitivity, and specificity. In this study, 97 cases of Korotkoff signal were collected, including 47 cases from normal vessels and 50 cases from stiff vessels, which were divided into training set and test set according to the ratio of 8 : 2. The accuracy, sensitivity and specificity of the final classification model was 86.4%, 92.3% and 77.8%, respectively. At present, non-invasive screening method for vascular stiffness is very limited. The results of this study show that the characteristics of Korotkoff signal are affected by vascular compliance, and it is feasible to use the characteristics of Korotkoff signal to detect vascular stiffness. This study might be providing a new idea for non-invasive detection of vascular stiffness.
Humans ; Vascular Stiffness ; Neural Networks, Computer ; Cardiovascular Diseases/diagnosis* ; Sensitivity and Specificity