Objective To observe the effect of cigarette smoking on cutaneous wound healing in rats.Methods Healthy adult SD rats,weight 200-250 g,were randomly divided into 4 groups:control group (group C),low tar content group (group L),middle tar content group (group M) and high tar content group (group H),ten rats each group.4 full thickness dermal excision wound models with the diameter of 2.0 cm were established on the back of 40 healthy male or female SD rats.The wound spacing was greater than 2 cm.Group C did not do any intervention treatment,Groups L,M and H rats were put,respectively,into homemade smoke box in 0.1,0.5,and 0.8 mg different-grade tar content of cigarettes and smoked,and the time of wound healing was observed.The wound granulation tissue was taken in 3 and 7 days after surgery.The expression of CD68+ macrophages was detected by immunohistochemical method.The wound healing rate was observed on the days 7 and 16.Results Compared with group C,wound healing time in groups L,M and H was extended (P＜ 0.05).Immunohistochemical results showed that macrophage numbers in groups L,M and H wound were significantly less than that of group C.The wound healing rates 7 days and 16 days after surgery in the three group smoked were significantly lower than that of group C.Conclusions Cigarette smoking leads to wound healing delay,and wound healing rate is reduced at fixed time point,with the decrease in the number of macrophages.

The interference of noise and the weak edge characteristic of symptom information on medical images prevent the traditional methods of segmentation from having good effects. In this paper is proposed a boundary detection method of focus which is based on dyadic wavelet transform and active contour model. In this method, the true edge points are detected by dyadic wavelet transform and linked by improved fast active contour model algorithm. The result of experiment on MRI of brain shows that the method can remove the influence of noise effective and detect the contour of brain tumor actually.
Algorithms ; Brain ; anatomy & histology ; Humans ; Image Enhancement ; Image Interpretation, Computer-Assisted ; methods ; Magnetic Resonance Imaging ; Pattern Recognition, Automated ; methods