MiR-301a promotes embryonic stem cell differentiation to cardiomyocytes.

BACKGROUND: Cardiovascular disease is the leading cause of death worldwide. Tissue repair after pathological injury in the heart remains a major challenge due to the limited regenerative ability of cardiomyocytes in adults. Stem cell-derived cardiomyocytes provide a promising source for the cell transplantation-based treatment of injured hearts. AIM: To explore the function and mechanisms of miR-301a in regulating cardiomyocyte differentiation of mouse embryonic stem (mES) cells, and provide experimental evidence for applying miR-301a to the cardiomyocyte differentiation induction from stem cells. METHODS: mES cells with or without overexpression of miR-301a were applied for all functional assays. The hanging drop technique was applied to form embryoid bodies from mES cells. Cardiac markers including GATA-4, TBX5, MEF2C, and α-actinin were used to determine cardiomyocyte differentiation from mES cells. RESULTS: High expression of miR-301a was detected in the heart from late embryonic to neonatal mice. Overexpression of miR-301a in mES cells significantly induced the expression of cardiac transcription factors, thereby promoting cardiomyocyte differentiation and beating cardiomyocyte clone formation. PTEN is a target gene of miR-301a in cardiomyocytes. PTEN-regulated PI3K-AKT-mTOR-Stat3 signaling showed involvement in regulating miR-301a-promoted cardiomyocyte differentiation from mES cells. CONCLUSION: MiR-301a is capable of promoting embryonic stem cell differentiation to cardiomyocytes.

Study on Hesitant Fuzzy Information Measures and Their Clustering Application.

At present, research on hesitant fuzzy operations and measures is based on equal length processing, and an equal length processing method will inevitably destroy the original data structure and change the data information. This is an urgent problem to be solved in the development of hesitant fuzzy sets. Aiming at solving this problem, this paper firstly defines a hesitant fuzzy entropy function as the measure of the degree of uncertainty of hesitant fuzzy information and then proposes the concept of hesitant fuzzy information feature vector. The hesitant fuzzy distance measure and similarity measure are studied based on the information feature vector. Finally, the hesitant fuzzy network clustering method based on similarity measure is given, and the effectiveness of our algorithm through a numerical example is illustrated.