• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.1
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• pp.115-118
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• 2012

Purpose: The Levey-Jennings chart controlled measurement values that deviated from the tolerance value (mean ${\pm}2SD$ or ${\pm}3SD$). On the other hand, the upgraded Westgard Multi-Rules are actively recommended as a more efficient, specialized form of hospital certification in relation to Internal Quality Control. To apply Westgard Multi-Rules in quality control, credible quality control substance and target value are required. However, as physical examinations commonly use quality control substances provided within the test kit, there are many difficulties presented in the calculation of target value in relation to frequent changes in concentration value and insufficient credibility of quality control substance. This study attempts to improve the professionalism and credibility of quality control by applying Westgard Multi-Rules and calculating credible target value by using a commercialized quality control substance. Materials and Methods : This study used Immunoassay Plus Control Level 1, 2, 3 of Company B as the quality control substance of Total T3, which is the thyroid test implemented at the relevant hospital. Target value was established as the mean value of 295 cases collected for 1 month, excluding values that deviated from ${\pm}2SD$. The hospital quality control calculation program was used to enter target value. 12s, 22s, 13s, 2 of 32s, R4s, 41s, $10\bar{x}$, 7T of Westgard Multi-Rules were applied in the Total T3 experiment, which was conducted 194 times for 20 days in August. Based on the applied rules, this study classified data into random error and systemic error for analysis. Results: Quality control substances 1, 2, and 3 were each established as 84.2 ng/$dl$, 156.7 ng/$dl$, 242.4 ng/$dl$ for target values of Total T3, with the standard deviation established as 11.22 ng/$dl$, 14.52 ng/$dl$, 14.52 ng/$dl$ respectively. According to error type analysis achieved after applying Westgard Multi-Rules based on established target values, the following results were obtained for Random error, 12s was analyzed 48 times, 13s was analyzed 13 times, R4s was analyzed 6 times, for Systemic error, 22s was analyzed 10 times, 41s was analyzed 11 times, 2 of 32s was analyzed 17 times, $10\bar{x}$ was analyzed 10 times, and 7T was not applied. For uncontrollable Random error types, the entire experimental process was rechecked and greater emphasis was placed on re-testing. For controllable Systemic error types, this study searched the cause of error, recorded the relevant cause in the action form and reported the information to the Internal Quality Control committee if necessary. Conclusions : This study applied Westgard Multi-Rules by using commercialized substance as quality control substance and establishing target values. In result, precise analysis of Random error and Systemic error was achieved through the analysis of 12s, 22s, 13s, 2 of 32s, R4s, 41s, $10\bar{x}$, 7T rules. Furthermore, ideal quality control was achieved through analysis conducted on all data presented within the range of ${\pm}3SD$. In this regard, it can be said that the quality control method formed based on the systematic application of Westgard Multi-Rules is more effective than the Levey-Jennings chart and can maximize error detection.

• Journal of Chest Surgery
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• v.35 no.12
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• pp.847-853
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• 2002

We previously published the data that proved the safety of retrograde cerebral perfusion for 120 minutes. At this time, we planned to check the neuron-specific enolase and S100-$\beta$ in serum and urine to assess the possibility of early detection of cerebral injury. Material and Method: We used pigs(Landrace species) weighing 35 kg and performed RCP for 120 minutes. After the weaning of cardiopulmonary bypass, we observed the pigs for another 120 minutes. Systemic arterial pressure, central venous pressure, and serum and urine levels of neuron-specific enolose (NSE) and S100$\beta$ protein were checked. Central venous pressure during RCP was maintained in the range of 20 to 25 mmHg. Result: Serum levels of NSE(ng/$m\ell$) were 0.67$\pm$0.18(induction of anesthesia), 0.53$\pm$0.47(soon after CPB), 0.44$\pm$0.27(20min alter CPB), 0.24$\pm$0.09(RCP 20min), 0.37$\pm$0.35(RCP 40min), 0.33$\pm$0.21 (RCP 60min), 0.37$\pm$0.22(RCP 80min), 0.41$\pm$0.23(RCP 100 min), 0.48$\pm$0.26(RCP 120min), 0.42$\pm$0.29(30min after rewarming), 0.35 $\pm$0.32(60min after rewarming, 0.42$\pm$0.37(CPBoff 30min), 0.47$\pm$0.34(CPBOff 60min), 0.47$\pm$0.28(CPBOff 90min), and 0.57$\pm$0.29(CPBOff 120min). There was no statistically significant difference in levels between before and after RCP(ANOVA, p＞0.05). Urine levels of NSE also showed no statistically significant difference in levels between before and after RCP. There was no correlation between urine and serum levels of NSE(Pearson correlation, p＞0.05). Serum levels of S100$\beta$ protein(ng/$m\ell$) during the same time frames were 0.14$\pm$0.08, 0.15$\pm$0.07, 0.22$\pm$0.15, 0.23$\pm$0.07, 0.28$\pm$0.10, 0.40$\pm$0.05, 0.47$\pm$0.03, 0.49$\pm$0.12, 0.43$\pm$0.11, 0.46$\pm$0.15, 0.62$\pm$0.17, 0.77$\pm$0.21, 0.78$\pm$0.23, 0.77$\pm$0.23, and 0.82$\pm$0.33. There was statistically significant difference in levels between before and after RCP(ANOVA, p＜0.05). Urine levels of NSE also showed statistically significant difference in levels between before and after RCP(ANOVA, p＜0.05). There was significant correlation between urine and serum levels of NSE(Pearson correlation, p＜0.05). Conclusion: The author observed the increase in serum and urine levels of S100$\beta$ after 120 minutes of RCP. Significant correlation between serum and urine levels was observed. The results were considered to be the fundamental data that could correlate this study with human-based study.

• Journal of Food Hygiene and Safety
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• v.32 no.6
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• pp.470-476
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• 2017

The purpose of this study was to investigate and evaluate the safety of the grains, nut products, beans and oilseeds being sold in Gyeonggi province by analyzing mycotoxins. A multi-mycotoxins analysis method based on LC-MS/MS was validated and applied for the determination of eight mycotoxins, including aflatoxins ($B_1$, $B_2$, $G_1$ and $G_2$), fumonisins ($B_1$, $B_2$), zearalenone and ochratoxcin A in 134 samples. The limit of detection (LOD) and limit of quantitation (LOQ) for the eight mycotoxins ranged from 0.14 to $8.25{\mu}g/kg$ and from 1.08 to $7.21{\mu}g/kg$, respectively. Recovery rates of mycotoxins were determined in the range of 61.1 to 97.5% with RSD of 1.0~14.5% (n=3). Fumonisin $B_1$, $B_2$, zearalenone, and ochratoxin A were detected in 22 samples, indicating that 27% of grains, 12.5% of beans and 11.8% of oilseeds were contaminated. Fumonisin and zearalenone were detected simultaneously in 2 adlays and 3 sorghums. Fumonisin $B_1$ and $B_2$ were detected simultaneously in most samples whereas fumonisin $B_1$ was detected in 1 adlay, 1 millet and 1 sesame sample. The average detected amount of fumonisin was $49.3{\mu}g/kg$ and $10.1{\mu}g/kg$ for grains and oilseeds, respectively. The average detected amount of zearalenone was $1.9{\mu}g/kg$ and $1.5{\mu}g/kg$ for grains and beans, respectively. In addition, the average amount of ochratoxin A was $0.08{\mu}g/kg$ for grains. The calculated exposure amounts of fumonisin, zeralenone and ochratoxin A for grains, beans and oilseeds were below the PMTDI/PTWI.