OBJECTIVETo compare the releasing of growth factors between platelet-rich fibrin (PRF) and platelet-rich plasma (PRP) as well as their effects on the proliferation and differentiation of adipose tissue-derived stem cells (ADSCs) in vitro.

METHODSBlood was taken from central artery of rabbits, acquiring PRF was acquired through one time centrifuge and PRP through two times centrifuge. Five milliliters of fresh alpha-MEM was added to PRF and PRP and incubated at 37 degrees C. The time points to collect exudates was in day 1, 7, 14, 21, 28 and the mass concentrations of transforming growth factor-beta1 (TGF-beta1) and platelet derived growth factor-AB(PDGF-AB) were quantified in PRF and PRP. Then the exudates of PRF and PRP were used to culture ADSCs and evaluate the effects of PRF and PRP on proliferation and differentiation of ADSCs.

RESULTS1) Growth factor release: In the PRF exudates at different time points, the mass concentration of TGF-beta1 was the highest at day 14 and the highest mass concentration of PDGF-AB at day 7, the mass concentration of both TGF-beta1 and PDGF-AB was the highest at the first day and then gradually declined. 2)The effect on proliferation and differentiation: PRF exudates of day 14 expressed the maximum proliferation and alkaline phosphatase (ALP) activity. PRF exudates of day 1 demonstrat the maximum proliferation and ALP activity.

CONCLUSIONComparing to PRP, PRF releases growth factors gradually and expressed stronger and more durable effect on proliferation and differentiation of ADSCs in vitro.

Adipose Tissue ; Blood Platelets ; Cell Differentiation ; Fibrin ; In Vitro Techniques ; Intercellular Signaling Peptides and Proteins ; Organic Chemicals ; Platelet-Derived Growth Factor ; Platelet-Rich Plasma ; Stem Cells ; Transforming Growth Factor beta1

With the change of medical diagnosis and treatment mode, the quality of medical image directly affects the diagnosis and treatment of the disease for doctors. Therefore, realization of intelligent image quality control by computer will have a greater auxiliary effect on the radiographer's filming work. In this paper, the research methods and applications of image segmentation model and image classification model in the field of deep learning and traditional image processing algorithm applied to medical image quality evaluation are described. The results demonstrate that deep learning algorithm is more accurate and efficient than the traditional image processing algorithm in the effective training of medical image big data, which explains the broad application prospect of deep learning in the medical field. This paper developed a set of intelligent quality control system for auxiliary filming, and successfully applied it to the Radiology Department of West China Hospital and other city and county hospitals, which effectively verified the feasibility and stability of the quality control system.