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( Hye Jung Chang ),( Jae Hoon Lee ),( Young Rok Do ),( Sung Hwa Bae ),( Jung Lim Lee ),( Seung Hyun Nam ),( Sung Soo Yoon ),( Soo Mee Bang1 ) 대한내과학회 2011 The Korean Journal of Internal Medicine Vol.26 No.4
Background/Aims: The clinical efficacy and safety of a three-drug combination of melphalan, prednisone, and thalidomide were assessed in patients with multiple myeloma who were not candidates for high-dose therapy as a firstline treatment. Because the side effects of thalidomide at a dose of ≥ 100 mg daily can be a barrier to effective treatment for these patients, we evaluated the efficacy and safety of a reduced dose of thalidomide, 50 mg, for non-transplant candidates. Methods: Twenty-one patients were treated in 4-week cycles, receiving 4 mg/m2 melphalan and 40 mg/m2 prednisone on days 1-7 and 50 mg thalidomide daily. The primary efficacy outcome was the overall response rate. Aspirin (100 mg daily) was also provided as prophylactic treatment for thromboembolism. Results: The overall response rate was 57.1%; a complete response was seen in 23.8% of patients, a partial response in 33.3%, and stable disease in 9.5%. After a median follow-up time of 16.1 months, the median time to progression was 11.4 months (95% confidence interval, 2.1 to 20.6); the median overall survival was not reached. Grades 3 and 4 adverse events included infection (10%), peripheral neuropathy (5%), diarrhea (5%), thrombosis (10%), and loss of consciousness (10%). Two patients discontinued treatment due to loss of consciousness and neuropathy. Conclusions: Low-dose thalidomide (50 mg) plus melphalan and prednisone is an effective combination drug therapy option for newly diagnosed myeloma patients who are ineligible for high-dose chemotherapy.
An Auto Playlist Generation System with One Seed Song
Bang, Sung-Woo,Jung, Hye-Wuk,Kim, Jae-Kwang,Lee, Jee-Hyong Korean Institute of Intelligent Systems 2010 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.10 No.1
The rise of music resources has led to a parallel rise in the need to manage thousands of songs on user devices. So users have a tendency to build playlist for manage songs. However the manual selection of songs for creating playlist is a troublesome work. This paper proposes an auto playlist generation system considering user context of use and preferences. This system has two separated systems; 1) the mood and emotion classification system and 2) the music recommendation system. Firstly, users need to choose just one seed song for reflecting their context of use. Then system recommends candidate song list before the current song ends in order to fill up user playlist. User also can remove unsatisfied songs from the recommended song list to adapt the user preference model on the system for the next song list. The generated playlists show well defined mood and emotion of music and provide songs that the preference of the current user is reflected.