Diabetic foot ulcers (DFUs) are associated with an increased risk of secondary infection and amputation. Platelet-rich fibrin (PRF), a platelet and leukocyte concentrate containing several cytokines and growth factors, is known to promote wound healing. However, the effect of PRF on diabetic wound healing has not been adequately investigated. The aim of the study was to investigate the effect of PRF on skin wound healing in a diabetic mouse model. Platelet-rich fibrin was prepared from whole blood of 8 healthy volunteers. Two symmetrical skin wounds per mouse were created on the back of 16 diabetic nude mice. One of the 2 wounds in each mouse was treated with routine dressings (control), whereas the other wound was treated with PRF in addition to routine dressings (test), each for a period of 14 days. Skin wound healing rate was calculated.Use of PRF was associated with significantly improved skin wound healing in diabetic mice. On hematoxylin and eosin and CD31 staining, a significant increase in the number of capillaries and CD31-positive cells was observed, suggesting that PRF may have promoted blood vessel formation in the skin wound. In this study, PRF seemed to accelerate skin wound healing in diabetic mouse models, probably via increased blood vessel formation.

Reduction mammaplasty using our modified round block technique can maximally preserve the blood supply to the remaining gland as well as the innervation to the nipple-areolar complex, while maintaining the advantages of the traditional technique, such as an invisible scar and good projection.

The point clouds registration is a key step in data processing for the 3D laser scanner to obtain complete information of the object surface, and there are many algorithms. In order to overcome the disadvantages of slow calculation speed and low accuracy of existing point clouds registration algorithms, a fast point clouds registration algorithm based on the improved voxel filter and ISS-USC feature is proposed. Firstly, the improved voxel filter is used for down-sampling to reduce the size of the original point clouds data. Secondly, the intrinsic shape signature (ISS) feature point detection algorithm is used to extra feature points from the down-sampled point clouds data, and then the unique shape context (USC) descriptor is calculated to describe the extracted feature points. Next, the improved random sampling consensus (RANSAC) algorithm is used for coarse registration to obtain the initial position. Finally, the iterative closest point (ICP) algorithm based on KD tree is used for fine registration, which realizes the transform from the point clouds scanned by the 3D laser scanner at different angles to the same coordinate system. Through comparing with other algorithms and the registration experiment of the VGA connector for monitor, the experimental results verify the effectiveness and feasibility of the proposed algorithm, and it has fastest registration speed while maintaining high registration accuracy.