23,781 research outputs found
Ben jing xu shu yao
No full text[V.1-6]. 本經疏證 -- [v.7-9]. 本經續疏 -- [v.9-12]. 本經序疏要.[V.1-6]. Ben jing shu zheng -- [v.7-9]. Ben jing xu shu -- [v.9-12]. Ben jing xu shu yao.鄒澍學 ; 常州常年醫局校栞. 本經續疏 : 六卷 / 鄒澍學 ; 胡杰校栞. 本經序疏要 : 八卷 / 鄒澍學 ; 許恩溥校栞.綫裝 .框16.3x12.7公分, 11行22字, 小字雙行同. 白口, 左右雙邊, 單黑魚尾. 版心上鐫題名, 中鐫卷次, 下鐫葉次.第一冊書名頁刻"鄒閏安先生本經疏證十二卷續疏六卷本經序疏要八卷" ; 又有紅色戳記"常州麟玉山房發兌"《本經序疏要》卷八末刻"常郡韓文煥齋鐫". 並有湯用中道光己酉[1849]跋.《中國中醫古籍總目》(02456)著錄.鈐"莊兆祥印", "莊兆祥".Xian zhuang .Kuang 16.3 x 12.7 gong fen, 11 hang 22 zi, xiao zi shuang hang tong. Bai kou, zuo you shuang bian, dan hei yu wei. Ban xin shang juan ti ming, zhong juan juan ci, xia juan ye ci.Detailed notes in vernacular field only.Detailed notes in vernacular field only.Detailed notes in vernacular field only.Zou Shu xue ; Changzhou Chang nian yi ju jiao kan. Ben jing xu shu : liu juan / Zou Shu xue ; Hu Jie jiao kan. Ben jing xu shu yao : ba juan / Zou Shu xue ; Xu Enpu jiao kan.Qian "Zhuang Zhaoxiang yin", "Zhuang Zhaoxiang"
Hu qing yu tang xue ji
No full text[胡雪巖編].綫裝.框18x14公分,10行20字, 無界行. 白口, 四周雙邊, 單黑魚尾. 版心上鐫"丸丹全集", 中鐫小題, 下鐫葉次及"胡慶餘堂雪記"凡例言"是集既成, 廣✹印送"前有光緖三年[1877]胡雪巖序.題名據總目 ; 出書年據序.《中國中醫古籍總目》04580著錄.鈐"莊兆祥印", "莊兆祥"Xian zhuang.Kuang 18 x 14 gong fen,10 hang 20 zi, wu jie hang. Bai kou, si zhou shuang bian, dan hei yu wei. Ban xin shang juan "Wan dan quan ji", zhong juan xiao ti, xia juan ye ci ji "Hu qing yu tang Xue ji"Detailed notes in vernacular field only.Qian you Guangxu san nian [1877] Hu Xueyan xu.Ti ming ju zong mu ; chu shu nian ju xu.Detailed notes in vernacular field only.[Hu Xueyan bian].Qian "Zhuang Zhaoxiang yin", "Zhuang Zhaoxiang
The political role of the people's liberation army 1949-1973
No full textThis thesis is to study the political role of the People's Liberation Army from the approach of structure and function. The framework of the thesis consists of three major parts, first, the influence of Chinese traditional political culture on, and the formation of, the political role of the PL A; second, the influence of domestic political struggles and external military conflicts on the development of the political role of the PLA; and the third, the analysis of the transition of the PLA's political role from the structure and personnel arrangements of the CCPCC Within the above-mentioned three scopes, this thesis make a thorough discussion on the following: (1) The relationship between the structure of the PRC and the formation of the PLA's political role; (2) How has ideology influenced the army's political role; (3) What is Mao's viewpoint and his influence on the development of the army's political role; (4) What is the link between the army and the party, and how has this developed; (6) What accounts for the expansion of the PLA's political functions; (7) What is the influence of political factional struggles on the PLA's political role; (8) Is it political institution or military institution that controls the recruitment of the military elite; (9) What are the disparities between the military elite in handling international conflicts and what are their political considerations; (10) What is the Party's position in the army; (11) How have the Party’s important meetings and personnel arrangements influenced the rise and fall of the PLA's political role
Bianque xin shu shen fang
No full text扁鵲傳 ; 竇材重集 ; 胡珏參論.綫裝.框17.8x12.2公分, 8行20字, 小字雙行同. 白口, 左右雙邊, 單黑魚尾. 版心上鐫題名, 中鐫卷次, 下鐫葉次.分上, 中, 下卷.《神方》末有乾隆乙酉[1765]王琦跋, 言刻書事.《中國中醫古籍總目》05495著錄有乾隆刻本.鈐"莊兆祥印"朱, 白文各一方.Xian zhuang.Kuang 17.8 x 12.2 gong fen, 8 hang 20 zi, xiao zi shuang hang tong. Bai kou, zuo you shuang bian, dan hei yu wei. Ban xin shang juan ti ming, zhong juan juan ci, xia juan ye ci.Fen shang, zhong, xia juan."Shen fang" mo you Qianlong yi you [1765] Wang Qi ba, yan ke shu shi.Detailed notes in vernacular field only.Bian Que zhuan ; Dou Cai chong ji ; Hu Jue can lun.Qian "Zhuang Zhaoxiang yin" zhu, bai wen ge yi fang
Wu cai Shi zhu zhai shu hua pu /
No full textCalligraphy and painting from the Ten Bamboo Studio.; Also available online at: http://nla.gov.au/nla.gen-vn2539636.880-03 Shi zhu zhai shu hua pu.880-05 Wu se ke luo ban Shi zhu zhai shu hua pu
Image1_Computational Network Pharmacology–Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression.TIF
No full textTraditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.</p
Table4_Computational Network Pharmacology–Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression.DOCX
No full textTraditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.</p
Table3_Computational Network Pharmacology–Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression.XLSX
No full textTraditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.</p
Table1_Computational Network Pharmacology–Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression.XLSX
No full textTraditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.</p
Table5_Computational Network Pharmacology–Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression.XLSX
No full textTraditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.</p
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