• Title/Summary/Keyword: eRF1

Search Result 433, Processing Time 0.02 seconds

Current Status of Korean Ginseng Research (한국인삼론(韓國人蔘論))

  • Han, Byung-Hoon
    • Korean Journal of Pharmacognosy
    • /
    • v.3 no.3
    • /
    • pp.151-160
    • /
    • 1972
  • Recent achievements of scientific research on the pharmacologic activities and the chemical problems of dammalene glycosides, which are considered to be effective principles of Korean ginseng, are reviewed and analyzed in view of structure-activity relationship. 1) S. Shibata and his co-workers detected 12 glycoside spots of dammalene series on the two dimensional T.L.C. of total glycoside fraction from Japanese ginseng, and designated them Ginsenoside Rx(x=a, b, c, g, h, etc.) in the order of increasing Rf-value. The aglycones of those glycosides were characterized to be protopanaxadiol for the Ginsenoside $Rx(x=a,\;b_{1},\;b_{2},\;c,\;d,\;e,\;f)$ and protopanaxatriol for the Ginsenoside $Rx(x=g_{1},\;g_{2},\;g_{3},\;h_{1}\;'h_{2})$. Using Korean ginseng as the material for our study, the author and his coworkers isolated a new dammalene glycoside(Panax Saponin C), which comes under the category of protopanaxadiol glycosides based on the classification of S. Shibata et al., and characterized this saponin to be the glycoside of protopanaxatriol series. Furthermore, Panax Saponin C dissociated into $two\;components(C_{1}\;and\;C_{2}-acetate)$ by acetylation, both of which returned to original Panax Saponin C by deacetylation. Based on this result, more than 13 glycoside components of dammalene series will be expected in the Korean ginseng. 2) The structures of protopanaxadiol and protopanaxatriol, the genuine aglycones of dammalene glycosides, are fully established to be structural analogues by S. Shibata and his co-workers, therefore antagonistic and/or analogical activities will be expected for the pharmacologic activities of these glycoside series of structural analogues. K. Takaki and his co-workers found central nervous system (CNS) stimmulant activity from the glycosides of protopanaxatriol series and CNS-depressant activity from the glycosides of protopanaxadiol series. On the other hand, the author and his co-workers found stimmulating activity on the protein synthesis from both the series of dammalene glycosides with delayed and long-lasting characteristics. This delayed and long-lasting characteristics were also observed in the anti-inflammatory activity of glycosides of protopanaxatriol series on their time course tendency. For the convenience's sake of argument, pluralistic pharmacologic activities of dammalene glycosides, which were observed by many workers at various pharmacologic site, may be classified into two main categories; one is pan-cellular activity and the other is organ specific activity to the certain tissue which is a mass of cells differentiated to a certain direction for their special functions in the body. Based on the data of K. Takaki and those of the authors, following assumption will be probable; Pharmacologic activities of both series of glycosides of protopanaxadiol and protopanaxatriol aglycones may be antagonistic on their tissue-specific activities and analogic on their pan-cellular activities. Therefore, the mixture of these two series of glycosides in an appropriate ratio, as the case of total extract of Korean ginseng, will be probably beneficial to the host by increasing the synthesis of some functional proteins, due to the additive action of pan-cellular activity, and with the disappearance of any significant behavioral symptoms due to the antagonism of tissue specific activity. This fact will probably be the main reason why classical trials of pharmacologists failed in re-discovering the efficacy of Korean ginseng with their behavioral test. 3) The author and his co-workers achieved the synthesis of $C^{14}-labelled\;Panax\;Saponin\;A\;on\;C_{25}-C_{27}\;position\;of\;aglycone$ in the interest of tracer studies in vivo. The method will be applicable to other dammalene glycosides regardless of their chemical structure. 4) The author and his co-workers converted chemically betulafolienetriol, a triterpene component of Betula platyphylla, to the protopanaxadiol, one of genuine aglycone of dammalene glycosides.

  • PDF

Quantitative Analysis of Magnetization Transfer by Phase Sensitive Method in Knee Disorder (무릎 이상에 대한 자화전이 위상감각에 의한 정량분석법)

  • Yoon, Moon-Hyun;Sung, Mi-Sook;Yin, Chang-Sik;Lee, Heung-Kyu;Choe, Bo-Young
    • Investigative Magnetic Resonance Imaging
    • /
    • v.10 no.2
    • /
    • pp.98-107
    • /
    • 2006
  • Magnetization Transfer (MT) imaging generates contrast dependent on the phenomenon of magnetization exchange between free water proton and restricted proton in macromolecules. In biological materials in knee, MT or cross-relaxation is commonly modeled using two spin pools identified by their different T2 relaxation times. Two models for cross-relaxation emphasize the role of proton chemical exchange between protons of water and exchangeable protons on macromolecules, as well as through dipole-dipole interaction between the water and macromolecule protons. The most essential tool in medical image manipulation is the ability to adjust the contrast and intensity. Thus, it is desirable to adjust the contrast and intensity of an image interactively in the real time. The proton density (PD) and T2-weighted SE MR images allow the depiction of knee structures and can demonstrate defects and gross morphologic changes. The PD- and T2-weighted images also show the cartilage internal pathology due to the more intermediate signal of the knee joint in these sequences. Suppression of fat extends the dynamic range of tissue contrast, removes chemical shift artifacts, and decreases motion-related ghost artifacts. Like fat saturation, phase sensitive methods are also based on the difference in precession frequencies of water and fat. In this study, phase sensitive methods look at the phase difference that is accumulated in time as a result of Larmor frequency differences rather than using this difference directly. Although how MT work was given with clinical evidence that leads to quantitative model for MT in tissues, the mathematical formalism used to describe the MT effect applies to explaining to evaluate knee disorder, such as anterior cruciate ligament (ACL) tear and meniscal tear. Calculation of the effect of the effect of the MT saturation is given in the magnetization transfer ratio (MTR) which is a quantitative measure of the relative decrease in signal intensity due to the MT pulse.

  • PDF

Wildfire Severity Mapping Using Sentinel Satellite Data Based on Machine Learning Approaches (Sentinel 위성영상과 기계학습을 이용한 국내산불 피해강도 탐지)

  • Sim, Seongmun;Kim, Woohyeok;Lee, Jaese;Kang, Yoojin;Im, Jungho;Kwon, Chunguen;Kim, Sungyong
    • Korean Journal of Remote Sensing
    • /
    • v.36 no.5_3
    • /
    • pp.1109-1123
    • /
    • 2020
  • In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.