• Tuberculosis and Respiratory Diseases
• /
• v.48 no.3
• /
• pp.315-323
• /
• 2000

Background : An immunochromatographic assay (ICT Diagnostics) which facilitates the diagnosis of tuberculosis(TB) by detecting serum antibodies mainly directed against specific 38KDa of Mycobacterium tuberculosis has come into the market. The test consists of a cardboard folding device containing nitrocellulose strip and absorbent pads. The whole procedure is completed within 15 min and does not require any additional equipment. The test has been reported to be sensitive and specific in diagnosing active TB. Thus the test had been evaluated with sera from TB patients and TB-free subjects. Method : Sera from patients with active pulmonary tuberculosis(40 sputum positives for Mycobacterium tuberculosis, 79 sputum negatives, and 3 extrapulmonary tuberculosis) were obtained from the Double-Cross Chest Clinic of the Korean National Tuberculosis Association (KNTA) in Seoul. The control group consisted of TB-free 68 subjects(21 children under 7 years old and 47 healthy staff members of KNTA). Results : Nine out of 68(13.2%) TB-free controls had positive antibody response. Total 106 of 122(86.9%) radiologically active patients had positive antibodies while 16 (13.1%) showed negative reaction. Antibody was detected in 38 of 40(95.0%) sputum positive patients and 68 of 82(82.9%) sputum negative patients who were under the antituberculosis chemotherapy. The sensitivity and specificity were all 87% and the positive predictive value was 92.2% while the negative predictive value was 78.7%, when the prevalence of TB in the sample was 64.2%. Our results clearly show that the detection of antibodies which mainly react with the 38KDa antigen of M. tuberculosis is not suitable as the first-line method of diagnosis but considered only as an adjunctive test to standard techniques of tuberculosis diagnosis. when considering its high false positivity.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_1
• /
• pp.989-998
• /
• 2021

In the utilization of optical satellite imagery, which is greatly affected by clouds, periodic composite technique is a useful method to minimize the influence of clouds. Recently, a technique for selecting the optimal pixel that is least affected by the cloud and shadow during a certain period by directly inputting cloud and cloud shadow information during period compositing has been proposed. Accurate extraction of clouds and cloud shadowsis essential in order to derive optimal composite results. Also, in the case of an surface targets where spectral information is important, such as crops, the loss of spectral information should be minimized during cloud-free compositing. In thisstudy, clouds using two spectral indicators (Haze Optimized Tranformation and MeanVis) were used to derive a detection technique with low loss ofspectral information while maintaining high detection accuracy of clouds and cloud shadowsfor cabbage fieldsin the highlands of Gangwon-do. These detection results were compared and analyzed with cloud and cloud shadow information provided by Sentinel-2A/B. As a result of analyzing data from 2019 to 2021, cloud information from Sentinel-2A/B satellites showed detection accuracy with an F1 value of 0.91, but bright artifacts were falsely detected as clouds. On the other hand, the cloud detection result obtained by applying the threshold (=0.05) to the HOT showed relatively low detection accuracy (F1=0.72), but the loss ofspectral information was minimized due to the small number of false positives. In the case of cloud shadows, only minimal shadows were detected in the Sentinel-2A/B additional layer, but when a threshold (= 0.015) was applied to MeanVis, cloud shadowsthat could be distinguished from the topographically generated shadows could be detected. By inputting spectral indicators-based cloud and shadow information,stable monthly cloud-free composited vegetation index results were obtained, and in the future, high-accuracy cloud information of Sentinel-2A/B will be input to periodic cloud-free composite for comparison.