EFFECT OF ACUPUNCTURE STIMULATION AT SANYINJIAO (SP 6) ON CEREBRAL GLUCOSE METABOLISM IN DYSMENORRHEA PATIENTS

To study the central mechanism of acupuncture stimulation of Sanyinjiao （三阴交 SP 6） in relieving dysmenorrhea. Methods： A total of 6 dysmenorrhea volunteer patients were subjected into this study. On the first positron emission tomography （PET） scan examination, they were assigned to pseudoacupuncture group by using the acupuncture needle just to prick the skin of Sanyinjiao （SP 6） ; while on the second PET scans, they were assigned to acupuncture group by inserting the needle into the same acupoint. 18F fluorodeoxyglucose （;SF-FDG） PET of the whole brain was performed during pseudo-acupuncture and real acupuncture of Sanyinjiao （SP 6）. The acquired PET data were analyzed by using statistical parametric mapping （SPM） software to determine changes of glucose metabolism in different cerebral regions. The patient＇s pain intensity was rated by using 0-10 numerical pain intensity scale. Results： After pseudo-acupuncture stimulation of Sanyinjiao （SP 6）, no significant changes were found in the pain intensity （ P 〉0.05）, while after real-acupuncture stimulation, the pain intensity declined significantly （ P〈0.01 ）. Following acupuncture of the right Sanyinjiao （SP 6）, multiple cerebral regions involving pain were activated （increase of glucose metabolism）, including ipsilateral lenticular nucleus （globus palliclus, putamen）, ipsilateral cerebellum and insular lobe, bilateral dorsal thalamus, ipsilateral paracentral Iobule, bilateral amygdaloid bodies, contralateral substantia nigra of the midbrain, bilateral second somatosensory （S Ⅱ ） areas, ispsilateral hippocampal gyms, frontal part of the ipsilateral cingulated gyrus, and bilateral mammary bodies of the hypothalamus. In addition, fewer regions of the cerebral cortex responded with decrease of the glucose metabolism after real acupuncture.

A Collaborative Filtering Recommendation Algorithm Based on Item and Cloud Model

Recommender system is an important content in the research of E-commerce technology. Collaborative filtering recom-mendation algorithm has already been used successfully at recom-mender system. However,with the development of E-commerce,the difficulties of the extreme sparsity of user rating data have become more and more severe. Based on the traditional similarity measuring methods,we introduce the cloud model and combine it with the item-based collaborative filtering recommendation algorithms. The new collaborative filtering recommendation algorithm based on item and cloud model （IC-Based CF） computes the similarity de-gree between items by comparing the statistical characteristic of items. The experimental results show that this method can improve the performance of the present item-based collaborative filtering algorithm with extreme sparsity of data.