To establish a basis for deep investigation of the role of microRNA (miRNA) in the regulation of hematopoiesis, differential expression profiles of miRNA between human cord blood CD34(+)CD38(-) and CD34(+)CD38(+) cells were analyzed. Mononuclear cells from cord blood (CB) of healthy donors were separated by Ficoll-Hypaque density gradients. CD34(+)CD38(-) and CD34(+)CD38(+) cells were sorted by using FACS Vantage SE. Their mRNA were then extracted and hybridized to miRNA microarray chip. The resulting data were analyzed with GeneSpring and informatics technique. The results showed that eleven miRNAs were found to be downregulated and 73 miRNAs to be upregulated by at least two-fold in the CD34(+)CD38(+) cells of CB, compared with the CD34(+)CD38(-) cells, which maintained CD34(+)CD38(-) cells' self-renewal and multiple lineage potential, that were defined as "stemness" miRNAs. 12 of the 84 genes (14.29%) were common to 33 hematopoietic-expressed miRNAs expressed by CD34(+) cells from both peripheral blood and bone marrow in Georgantas's study, which included 10 upregulated miRNAs (hsa-miR-23b, -26b, -92, -107, -130a, -181a, -197, -213, -222, -223) and 2 downregulated ones (hsa-miR-16a, -155). Some "stemness" miRNAs undergo CD34 antigen-like expression pattern during development and commmitted differeniation of hematopoietic stem cell/progenitors. Hematopoiesis-associated miRNA clusters and putative target genes could be found with informatics technique. It is concluded that the hematopoietic "stemness" miRNAs play important roles in normal hematopoiesis: miRNA expression profiles of hematopoietic stem cell/progenitors --> their gene expression profiles --> their self-renewal and lineage-commmitted differeniation.
ADP-ribosyl Cyclase 1 ; immunology ; Antigens, CD34 ; immunology ; Fetal Blood ; immunology ; metabolism ; Gene Expression Profiling ; Hematopoietic Stem Cells ; cytology ; immunology ; physiology ; Humans ; MicroRNAs ; genetics ; metabolism

Deep learning and neural network models have been new research directions and hot issues in the fields of machine learning and artificial intelligence in recent years. Deep learning has made a breakthrough in the applications of image and speech recognitions, and also has been extensively used in the fields of face recognition and information retrieval because of its special superiority. Bone X-ray images express different variations in black-white-gray gradations, which have image features of black and white contrasts and level differences. Based on these advantages of deep learning in image recognition, we combine it with the research of bone age assessment to provide basic datum for constructing a forensic automatic system of bone age assessment. This paper reviews the basic concept and network architectures of deep learning, and describes its recent research progress on image recognition in different research fields at home and abroad, and explores its advantages and application prospects in bone age assessment.

Objective To realize the automated bone age assessment by applying deep learning to digital radiography(DR)image recognition of left wrist joint in Uyghur teenagers, and explore its practical ap-plication value in forensic medicine bone age assessment. Methods The X-ray films of left wrist joint after pretreatment, which were taken from 245 male and 227 female Uyghur nationality teenagers in Uygur Autonomous Region aged from 13.0 to 19.0 years old, were chosen as subjects. And AlexNet was as a regression model of image recognition. From the total samples above, 60% of male and fe-male DR images of left wrist joint were selected as net train set, and 10% of samples were selected as validation set. As test set, the rest 30%were used to obtain the image recognition accuracy with an error range in ±1.0 and ±0.7 age respectively, compared to the real age. Results The modelling results of deep learning algorithm showed that when the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the net train set was 81.4% and 75.6% in male, and 80.5% and 74.8% in female, respectively. When the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the test set was 79.5% and 71.2% in male, and 79.4% and 66.2% in female, respectively. Conclusion The combination of bone age research on teenagers' left wrist joint and deep learning, which has high accuracy and good feasi-bility, can be the research basis of bone age automatic assessment system for the rest joints of body.

Bone development shows certain regularity with age. The regularity can be used to infer age and serve many fields such as justice, medicine, archaeology, etc. As a non-invasive evaluation method of the epiphyseal development stage, MRI is widely used in living age estimation. In recent years, the rapid development of machine learning has significantly improved the effectiveness and reliability of living age estimation, which is one of the main development directions of current research. This paper summarizes the analysis methods of age estimation by knee joint MRI, introduces the current research trends, and future application trend.
Epiphyses/diagnostic imaging* ; Age Determination by Skeleton/methods* ; Reproducibility of Results ; Magnetic Resonance Imaging/methods* ; Knee Joint/diagnostic imaging*