• The Korean Journal of Nuclear Medicine
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• v.11 no.1
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• pp.17-32
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• 1977

The present study was undertaken to evaluate the significance of the insulin and the C-peptide rseponse to oral glucose loads in normal and diabetic subjects and to establish the effects of the obesity. In this study, the authors have measured plasma insulin and C-peptide by means of radioimmunoassay in 10 nonobese normal, 5 obese normal, 13 nonobese moderate diabetic patients, 9 obese moderate diabetic patients and 9 severe diabetic patients. The results obtained were as follows; 1. In 10 nonobese normal subjects, the plasma insulin level at fasting state and at 30, 60, 90, and 120 min after oral glucose loads were $15.7{\pm}3.4,\;48.3{\pm}9.8,\;40.4{\pm}6.7,\;37.4{\pm}6.5\;and\;26.0{\pm}4.2uU/ml(Mean{\pm}S.E.)$ and C-peptide were $1.9{\pm}0.3,\;3.9{\pm}0.6,\;6.3{\pm}0.6,\;5.7{\pm}0.5\;and\;4.0{\pm}0.5ng/ml$. The change of C-peptide was found to go almost parallel with that of insulin and the insulin value reaches to the highest level at 30 min whereas C-peptide reaches to its peak at 60min. 2. The plasma insulin level in 5 obese normal subjects were $38.9{\pm}12.3,\;59.5{\pm}12.3,\;59.2{\pm}17.1,\;56.1{\pm}20.0\;and\;48.4{\pm}17.2uU/ml$ and the C-peptide were $5.5{\pm}0.4,\;6.8{\pm}0.5,\;7.9{\pm}0.8,\;7.9{\pm}0.8\;and\;7.8{\pm}2.0ng/ml$. The insulin response appeared to be greater than nonobese normal subjects. 3. In 13 nonobese moderate diabetic patients, the plasma insulin levels were $27.1{\pm}4.9,\;44.1{\pm}6.0,\;37.3{\pm}6.6,\;35.5{\pm}8.1\;and\;34.7{\pm}10.7uU/ml$ and the C-peptide levels were $2.7{\pm}0.4,\;4.9{\pm}0.7,\;6.5{\pm}0.5,\;7.0{\pm}0.3\;and\;6.7{\pm}1.0ng/ml$. There was little significance compared to nonobese normal groups but delayed pattern is noted. 4. In 9 obese moderated diabetic patients, the plasma insulin levels were $22.1{\pm}7.9,\;80.0{\pm}19.3,\;108.0{\pm}27.0,\;62.0{\pm}17.6\;and\;55.5{\pm}10.1uU/ml$ and the C-peptide levels were $5.2{\pm}0.4,\;8.0{\pm}1.0,\;10.4{\pm}1.6,\;10.4{\pm}1.7\;and\;10.1{\pm}1.0ng/ml$ and its response was also greater than that of nonobese moderate diabetic patients. 5. The plasma insulin concentrations in 9 severe diabetic subjects were $8.0{\pm}3.8,\;12.1{\pm}3.5,\;16.8{\pm}4.6,\;19.6{\pm}5.2\;and\;15.0{\pm}5.0uU/ml$ and the C-peptide levels were $1.6{\pm}0.3,\;2.4{\pm}0.4,\;4.1{\pm}0.6,\;4.0{\pm}0.8\;and\;4.5{\pm}0.7ng/ml$ and the insulin and C-peptide responses were markedly reduced in severe diabetic groups. 6. There were-significant differences between each groups of patients on the magnitude of total insulin or C-peptide areas, the insulinogenic index and the C-peptide index.

• The Journal of Korean Academy of Prosthodontics
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• v.47 no.4
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• pp.365-375
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• 2009

Statement of problems: Stress analysis on implant components of the combined screw- and cement-retained implant prosthesis has not investigated yet. Purpose: The purpose of this study was to assess the load distribution characteristics of implant prostheses with the different prosthodontic retention types, such as cement-type, screw-type and combined type by using 3-dimensional finite element analysis. Material and methods: A 3-dimensional finite element model was created in which two SS II implants (Osstem Co. Ltd.) were placed in the areas of the first premolar and the first molar in the mandible, and three-unit fixed partial dentures with four different retention types were fabricated on the two SS II implants. Model 1 was a cement-retained implant restoration made on two cement-retained type abutments (Comocta abutment; Osstem Co. Ltd.), and Model 2 was a screw-retained implant restoration made on the screw-retained type abutments (Octa abutment; Osstem Co. Ltd.). Model 3 was a combined type implant restoration made on the cement-retained type abutment (Comocta abutment) for the first molar and the screw-retained type abutment (Octa abutment) for the first premolar. Lastly, Model 4 was a combined type implant restoration made on the screw-retained type abutment (Octa abutment) for the first molar and the cement-retained type abutment (Comocta abutment) for the first premolar. Average masticatory force was applied on the central fossa in a vertical direction, and on the buccal cusp in a vertical and oblique direction for each model. Von-Mises stress patterns on alveolar bone, implant body, abutment, abutment screw, and prosthetic screw around implant prostheses were evaluated through 3-dimensional finite element analysis. Results: Model 2 showed the lowest von Mises stress. In all models, the von Mises stress distribution of cortical bone, cancellous bone and implant body showed the similar pattern. Regardless of loading conditions and type of abutment system, the stress of bone was concentrated on the cortical bone. The von-Mises stress on abutment, abutment screw, and prosthetic screw showed the lower values for the screw-retained type abutment than for the cement-retained type abutment regardless of the model type. There was little reciprocal effect of the abutment system between the molar and the premolar position. For all models, buccal cusp oblique loading caused the largest stress, followed by buccal cusp vertical loading and center vertical loading. Conclusion: Within the limitation of the FEA study, the combined type implant prosthesis did not demonstrate more stress around implant components than the cement type implant prosthesis. Under the assumption of ideal passive fit, the screw-type implant prosthesis showed the east stress around implant components.

• Journal of Bio-Environment Control
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• v.5 no.2
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• pp.215-235
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• 1996

The thermal analysis by mathematical model simulation makes it possible to reasonably predict heating and/or cooling requirements of certain greenhouses located under various geographical and climatic environment. It is another advantages of model simulation technique to be able to make it possible to select appropriate heating system, to set up energy utilization strategy, to schedule seasonal crop pattern, as well as to determine new greenhouse ranges. In this study, the control pattern for greenhouse microclimate is categorized as cooling and heating. Dynamic model was adopted to simulate heating requirements and/or energy conservation effectiveness such as energy saving by night-time thermal curtain, estimation of Heating Degree-Hours(HDH), long time prediction of greenhouse thermal behavior, etc. On the other hand, the cooling effects of ventilation, shading, and pad ||||&|||| fan system were partly analyzed by static model. By the experimental work with small size model greenhouse of 1.2m$\times$2.4m, it was found that cooling the greenhouse by spraying cold water directly on greenhouse cover surface or by recirculating cold water through heat exchangers would be effective in greenhouse summer cooling. The mathematical model developed for greenhouse model simulation is highly applicable because it can reflects various climatic factors like temperature, humidity, beam and diffuse solar radiation, wind velocity, etc. This model was closely verified by various weather data obtained through long period greenhouse experiment. Most of the materials relating with greenhouse heating or cooling components were obtained from model greenhouse simulated mathematically by using typical year(1987) data of Jinju Gyeongnam. But some of the materials relating with greenhouse cooling was obtained by performing model experiments which include analyzing cooling effect of water sprayed directly on greenhouse roof surface. The results are summarized as follows : 1. The heating requirements of model greenhouse were highly related with the minimum temperature set for given greenhouse. The setting temperature at night-time is much more influential on heating energy requirement than that at day-time. Therefore It is highly recommended that night- time setting temperature should be carefully determined and controlled. 2. The HDH data obtained by conventional method were estimated on the basis of considerably long term average weather temperature together with the standard base temperature(usually 18.3$^{\circ}C$). This kind of data can merely be used as a relative comparison criteria about heating load, but is not applicable in the calculation of greenhouse heating requirements because of the limited consideration of climatic factors and inappropriate base temperature. By comparing the HDM data with the results of simulation, it is found that the heating system design by HDH data will probably overshoot the actual heating requirement. 3. The energy saving effect of night-time thermal curtain as well as estimated heating requirement is found to be sensitively related with weather condition: Thermal curtain adopted for simulation showed high effectiveness in energy saving which amounts to more than 50% of annual heating requirement. 4. The ventilation performances doting warm seasons are mainly influenced by air exchange rate even though there are some variations depending on greenhouse structural difference, weather and cropping conditions. For air exchanges above 1 volume per minute, the reduction rate of temperature rise on both types of considered greenhouse becomes modest with the additional increase of ventilation capacity. Therefore the desirable ventilation capacity is assumed to be 1 air change per minute, which is the recommended ventilation rate in common greenhouse. 5. In glass covered greenhouse with full production, under clear weather of 50% RH, and continuous 1 air change per minute, the temperature drop in 50% shaded greenhouse and pad ＆ fan systemed greenhouse is 2.6$^{\circ}C$ and.6.1$^{\circ}C$ respectively. The temperature in control greenhouse under continuous air change at this time was 36.6$^{\circ}C$ which was 5.3$^{\circ}C$ above ambient temperature. As a result the greenhouse temperature can be maintained 3$^{\circ}C$ below ambient temperature. But when RH is 80%, it was impossible to drop greenhouse temperature below ambient temperature because possible temperature reduction by pad ||||&|||| fan system at this time is not more than 2.4$^{\circ}C$. 6. During 3 months of hot summer season if the greenhouse is assumed to be cooled only when greenhouse temperature rise above 27$^{\circ}C$, the relationship between RH of ambient air and greenhouse temperature drop($\Delta$T) was formulated as follows : $\Delta$T= -0.077RH＋7.7 7. Time dependent cooling effects performed by operation of each or combination of ventilation, 50% shading, pad ＆ fan of 80% efficiency, were continuously predicted for one typical summer day long. When the greenhouse was cooled only by 1 air change per minute, greenhouse air temperature was 5$^{\circ}C$ above outdoor temperature. Either method alone can not drop greenhouse air temperature below outdoor temperature even under the fully cropped situations. But when both systems were operated together, greenhouse air temperature can be controlled to about 2.0-2.3$^{\circ}C$ below ambient temperature. 8. When the cool water of 6.5-8.5$^{\circ}C$ was sprayed on greenhouse roof surface with the water flow rate of 1.3 liter/min per unit greenhouse floor area, greenhouse air temperature could be dropped down to 16.5-18.$0^{\circ}C$, whlch is about 1$0^{\circ}C$ below the ambient temperature of 26.5-28.$0^{\circ}C$ at that time. The most important thing in cooling greenhouse air effectively with water spray may be obtaining plenty of cool water source like ground water itself or cold water produced by heat-pump. Future work is focused on not only analyzing the feasibility of heat pump operation but also finding the relationships between greenhouse air temperature(T$_{g}$ ), spraying water temperature(T$_{w}$ ), water flow rate(Q), and ambient temperature(T$_{o}$).