• Ultrasonography
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• v.32 no.1
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• pp.59-65
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• 2013

Purpose: The purpose of this study is to establish the methodology regarding synthesis of ultrasound contrast agent imaging, and to evaluate the characteristics of the synthesized ultrasound contrast agents, including size or degradation interval and image quality. Materials and Methods: The ultrasound contrast agent, composed of liposome and SF6, was synthesized from the mixture solution of $21{\mu}mol$ DPPC (1, 2-Dihexadecanoyl-sn-glycero-3-phosphocholine, $C_{40}H_{80}NO_8P$), $9{\mu}mol$ cholesterol, $1.9{\mu}mol$ of DCP (Dihexadecylphosphate, $[CH_3(CH_2)_{15}O]_2P(O)OH$), and chloroform. After evaporation in a warm water bath and drying during a period of 12-24 hours, the contrast agent was synthesized by the sonication process by addition of buffer and SF6 gas. The size of the contrast agent was controlled by use of either extruder or sonication methods. After synthesis of contrast agents, analysis of the size distribution of the bubbles was performed using dynamic light scattering measurement methods. The degradation curve was also evaluated by changes in the number of contrast agents via light microscopy immediate, 12 hours, 24 hours, 36 hours, 48 hours, 60 hours, 72 hours, and 84 hours after synthesis. For evaluation of the role as an US contrast agent, the echogenicity of the synthesized microbubble was compared with commercially available microbubbles (SonoVue, Bracco, Milan, Italy) using a clinical ultrasound machine and phantom. Results: The contrast agents were synthesized successfully using an evaporation-drying-sonication method. The majority of bubbles showed a mean size of 154.2 nanometers, and they showed marked degradation 24 hours after synthesis. ANOVA test revealed a significant difference among SonoVue, synthesized contrast agent, and saline (p < 0.001). Although no significant difference was observed between SonoVue and the synthesized contrast agent, difference in echogenicity was observed between synthesized contrast agent and saline (p < 0.01). Conclusion: We could synthesize ultrasound contrast agents using an evaporation-drying-sonication method. On the basis of these results, many prospective types of research, such as anticancer drug delivery, gene delivery, including siRNA or microRNA, targeted molecular imaging, and targeted therapy can be performed.

• Ultrasonography
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• v.32 no.2
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• pp.132-142
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• 2013

Purpose: The purpose of this study is to investigate the correlations of various kinetic parameters derived from the time intensity curve in a xenograft mouse model injected with a prostate cancer model (PC-3 and LNCaP) using an ultrasound contrast agent with histopathologic parameters. Materials and Methods: Twenty nude mice were injected with human prostate cancer cells (15 PC-3 and five LNCaP) on their hind limbs. A bolus of $500{\mu}L$ ($1{\times}10^8$ microbubbles) of second-generation US contrast agent (SonoVue) was injected into the retroorbital vein. The region of interest was drawn over the entire tumor. The time intensity curve was acquired and then fitted to a gamma variate function. The maximal intensity (A), time to peak (Tp), maximal wash-in rate (washin), washout rate (washout), area under the curve up to 50 sec ($AUC_{50}$), area under the ascending slope ($AUC_{in}$), and area under the descending slope ($AUC_{out}$) were derived from the parameters of the gamma variate fit. Immunohistochemical staining for VEGF and CD31 was performed. Tumor volume, the area percentage of VEGF stained in a field, and the count of CD31 (microvessel density, MVD) positive vessels showed correlation with the parameters from the time intensity curve. Results: No significant differences were observed between the kinetic and histopathological parameters from each group. MVD showed positive correlation with A (r=0.625, p=0.003), washin (r=0.462, p=0.040), $AUC_{50}$ (r=0.604, p=0.005), and $AUC_{out}$ (r=0.587, p=0.007). Positive correlations were also observed between tumor volume and $AUC_{50}$ (r=0.481, p=0.032), washin (r=0.662, p=0.001), and $AUC_{out}$ (r=0.547, p=0.012). Washout showed negative correlations with MVD (r=-0.454, p=0.044) and tumor volume (r=-0.464, p=0.039). The area percentage of VEGF did not show any correlation with calculated data from the curve. Conclusion: MVD showed correlations with several of the kinetic parameters. CEUS has the potential for prediction of tumor vascularity in a prostate cancer animal model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.258-266
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• 2023

This study analyzed methodologies for estimating carbon stocks of perennial woody crops and the research cases in overseas countries. As a result, we found that Australia, Bulgaria, Canada, and Japan are using the stock-difference method, while Austria, Denmark, and Germany are estimating the change in the carbon stock based on the gain-loss method. In some overseas countries, the researches were conducted on estimating the carbon stock change using image data as tier 3 phase beyond the research developing country-specific factors as tier 2 phase. In South Korea, convergence studies as the third stage were conducted in forestry field, but advanced research in the agricultural field is at the beginning stage. Based on these results, we suggest directions for the following four future researches: 1) securing national-specific factors related to emissions and removals in the agricultural field through the development of allometric equation and carbon conversion factors for perennial woody crops to improve the completeness of emission and removals statistics, 2) implementing policy studies on the cultivation area calculation refinement with fruit tree-biomass-based maturity, 3) developing a more advanced estimation technique for perennial woody crops in the agricultural sector using allometric equation and remote sensing techniques based on the agricultural and forestry satellite scheduled to be launched in 2025, and to establish a matrix and monitoring system for perennial woody crop cultivation areas in the agricultural sector, Lastly, 4) estimating soil carbon stocks change, which is currently estimated by treating all agricultural areas as one, by sub-land classification to implement a dynamic carbon cycle model. This study suggests a detailed guideline and advanced methods of carbon stock change calculation for perennial woody crops, which supports 2050 Carbon Neutral Strategy of Ministry of Agriculture, Food, and Rural Affairs and activate related research in agricultural sector.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.398-403
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• 2023

Process-based K-cabbage model is based on physiological processes such as photosynthesis and phenology, making it possible to predict crop growth under different climate conditions that have never been experienced before. Current first-stage process-based models can be used to assess climate impact through yield prediction based on climate change scenarios, but no comparison has been performed between big data obtained from the main production area and model prediction so far. The aim of this study was to find out the direction of model improvement when using the current model for yield prediction. For this purpose, model performance evaluation was conducted based on data collected from farmers growing 'Chungwang' cabbage in Taebaek and Samcheok, the main producing areas of Chinese cabbage in highland region. The farms surveyed in this study had different cultivation methods in terms of planting date and soil water and nutrient management. The results showed that the potential biomass estimated using the K-cabbage model exceeded the observed values in all cases. Although predictions and observations at the time of harvest did not show a complete positive correlation due to limitations caused by the use of fresh weight in the model evaluation process (R²=0.74, RMSE=866.4), when fitting the model based on the values 2 weeks before harvest, the growth suitability index was different for each farm. These results are suggested to be due to differences in soil properties and management practices between farms. Therefore, to predict attainable yields taking into account differences in soil and management practices between farms, it is necessary to integrate dynamic soil nutrient and moisture modules into crop models, rather than using arbitrary growth suitability indices in current K-cabbage model.