BACKGROUND:It is discovered recently that miRNA is a new regulator that is able to have an impact on gene expression and miRNA contributes to proliferation, differentiation and self-renewal of pluripotent stem cels.
OBJECTIVE:To investigate the mechanism by which miR-155 regulates chondrogenic differentiation of bone marrow mesenchymal stem cels.
METHODS: Sixty healthy Sprague-Dawley aged 12 weeks were randomized into study group and control group, with 30 in each group. Under anesthesia, rats were sacrificed to harvest bone marrow of the lower limbs. Then bone marrow mesenchymal stem cels were isolated, cultured, and transfected with miR-155 mimics in the study group and a negative control sequence in the control group. After chondrogenic induction, RT-PCR was used to detect the expressions of Sox9, Colagen II, Aggrecan and Colagen X gene, and western blot assay to detect the expression of Sox9 and Runx2 proteins.
RESULTS AND CONCLUSION:Compared with the control group, the mRNA expressions of Sox9, Colagen II and Aggrecan were higher, but the mRNA expression of Colagen X was lower in the study group (P < 0.05); the protein expression of Sox9 was higher, but the protein expression of Runx2 was lower in the study group (P < 0.05). These findings indicate that miR-155 promotes the chondrogenic differentiation of bone marrow mesenchymal stem cels and moreover, it can suppress the hypertrophy trend of bone marrow mesenchymal stem cels differentiating into chondrocytes.

Objective    To explore the application of Tsetlin Machine (TM) in heart beat classification. Methods    TM was used to classify the normal beats, premature ventricular contraction (PVC) and supraventricular premature beats (SPB) in the 2020 data set of China Physiological Signal Challenge. This data set consisted of the single-lead electro-cardiogram data of 10 patients with arrhythmia. One patient with atrial fibrillation was excluded, and finally data of the other 9 patients were included in this study. The classification results were then analyzed. Results    The classification results showed that the average recognition accuracy of TM was 84.3%, and the basis of classification could be shown by the bit pattern interpretation diagram. Conclusion    TM can explain the classification results when classifying heart beats. The reasonable interpretation of classification results can increase the reliability of the model and facilitate people's review and understanding.

Objective To propose a heart sound segmentation method based on multi-feature fusion network. Methods Data were obtained from the CinC/PhysioNet 2016 Challenge dataset (a total of 3 153 recordings from 764 patients, about 91.93% of whom were male, with an average age of 30.36 years). Firstly the features were extracted in time domain and time-frequency domain respectively, and reduced redundant features by feature dimensionality reduction. Then, we selected optimal features separately from the two feature spaces that performed best through feature selection. Next, the multi-feature fusion was completed through multi-scale dilated convolution, cooperative fusion, and channel attention mechanism. Finally, the fused features were fed into a bidirectional gated recurrent unit (BiGRU) network to heart sound segmentation results. Results The proposed method achieved precision, recall and F1 score of 96.70%, 96.99%, and 96.84% respectively. Conclusion The multi-feature fusion network proposed in this study has better heart sound segmentation performance, which can provide high-accuracy heart sound segmentation technology support for the design of automatic analysis of heart diseases based on heart sounds.