• 한국환경보건학회지
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• 제27권3호
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• pp.71-80
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• 2001

Blood and urine mercury level of three workers were monitored during 60~80 days after high exposure to mercury at the silver refining plant. Mercury was used to form silver-mercury amalgam from plating sludge. Workers were exposed to mercury about 70 days at the several processes, such as hand held weaving, vibration table, and heating from the furnace. mercury was analysed by atomic absorption spectroscopy-vapor generation technique. Recovery from the biological sample was 95.51% and pooled standard deviation was 0.033. At the time of study, there was no work at the workplace. So, airborne mercury concentration was measured with area sampling 5 days after the work, ranged from 0.1459 to 1.2351 mg/㎥(Arithmatic mean 0.4711 mg/㎥, Geometric mean 0.3566 mg/㎥) at the inside of the plant, that is far above the ACGIH's TLV(0.025 mg/㎥) and ranged from 0.0073 to 0.0330 mg/㎥ at the outdoor. Blood mercury levels at the beginning of the monitoring were 4~14 times greater than the American Conference of Governmental Industrial Hygienists Biological Exposure Index(ACGIH BEI, 15 ug/L). Blood mercury levels were decreased logarithmically, that is, rapidly at the high level and slowly at the low level but sustained above the level of the ACGIH BEI 60~80 days after the work. Urine mercury levels at the beginning of the monitoring were 8~16 times greater than the ACGIH BEI(35 ug/g creatinine). Urine mercury levels were decreased logarithmically, but correlation between urine level and off-days were lower than those of blood. Decreasing pattern of blood mercury levels were little affected than that of urine levels when the chelating agent, D-penicillamine, was administered. There was correlation between blood mercury level and urine mercury level(0.81~0.83) but it didn\`t mean that the highest blood mercury level corresponded the highest urine mercury level. In our study, Case 1 always shows the highest level in urine but case 3 always shows the highest level in blood. Creatinine correction represented better correlations between urine mercury levels and blood levels, and between urine levels and off-days rather than by urine volume. Spot urine sampling had a wide variation than that of whole day urine sampling. So, We recommend spot urine sampling for screening and whole day urine sampling for exact diagnosis.

• Korean Journal of Radiology
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• 제25권1호
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• pp.43-54
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• 2024

Objective: To evaluate the added value of diffusion-weighted imaging (DWI)-based quantitative parameters to distinguish uterine sarcomas from atypical leiomyomas on preoperative magnetic resonance imaging (MRI). Materials and Methods: A total of 138 patients (age, 43.7 ± 10.3 years) with uterine sarcoma (n = 44) and atypical leiomyoma (n = 94) were retrospectively collected from four institutions. The cohort was randomly divided into training (84/138, 60.0%) and validation (54/138, 40.0%) sets. Two independent readers evaluated six qualitative MRI features and two DWI-based quantitative parameters for each index tumor. Multivariable logistic regression was used to identify the relevant qualitative MRI features. Diagnostic classifiers based on qualitative MRI features alone and in combination with DWI-based quantitative parameters were developed using a logistic regression algorithm. The diagnostic performance of the classifiers was evaluated using a cross-table analysis and calculation of the area under the receiver operating characteristic curve (AUC). Results: Mean apparent diffusion coefficient value of uterine sarcoma was lower than that of atypical leiomyoma (mean ± standard deviation, 0.94 ± 0.30 10^-3 mm²/s vs. 1.23 ± 0.25 10^-3 mm²/s; P < 0.001), and the relative contrast ratio was higher in the uterine sarcoma (8.16 ± 2.94 vs. 4.19 ± 2.66; P < 0.001). Selected qualitative MRI features included ill-defined margin (adjusted odds ratio [aOR], 17.9; 95% confidence interval [CI], 1.41-503, P = 0.040), intratumoral hemorrhage (aOR, 27.3; 95% CI, 3.74-596, P = 0.006), and absence of T2 dark area (aOR, 83.5; 95% CI, 12.4-1916, P < 0.001). The classifier that combined qualitative MRI features and DWI-based quantitative parameters showed significantly better performance than without DWI-based parameters in the validation set (AUC, 0.92 vs. 0.78; P < 0.001). Conclusion: The addition of DWI-based quantitative parameters to qualitative MRI features improved the diagnostic performance of the logistic regression classifier in differentiating uterine sarcomas from atypical leiomyomas on preoperative MRI.