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Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea
구유정,이채린,심재윤,이시훈,김경아,김상완,이유미,김효정,임정수,정춘희,전성완,유순집,류옥현,조호찬,홍아람,안창호,김정희,최만호 대한내분비학회 2021 Endocrinology and metabolism Vol.36 No.5
Background: Conventional diagnostic approaches for adrenal tumors require multi-step processes, including imaging studies anddynamic hormone tests. Therefore, this study aimed to discriminate adrenal tumors from a single blood sample based on the combination of liquid chromatography-mass spectrometry (LC-MS) and machine learning algorithms in serum profiling of adrenal steroids. Methods: The LC-MS-based steroid profiling was applied to serum samples obtained from patients with nonfunctioning adenoma(NFA, n=73), Cushing’s syndrome (CS, n=30), and primary aldosteronism (PA, n=40) in a prospective multicenter study of adrenaldisease. The decision tree (DT), random forest (RF), and extreme gradient boost (XGBoost) were performed to categorize the subtypes of adrenal tumors. Results: The CS group showed higher serum levels of 11-deoxycortisol than the NFA group, and increased levels of tetrahydrocortisone (THE), 20α-dihydrocortisol, and 6β-hydroxycortisol were found in the PA group. However, the CS group showed lower levelsof dehydroepiandrosterone (DHEA) and its sulfate derivative (DHEA-S) than both the NFA and PA groups. Patients with PA expressed higher serum 18-hydroxycortisol and DHEA but lower THE than NFA patients. The balanced accuracies of DT, RF, andXGBoost for classifying each type were 78%, 96%, and 97%, respectively. In receiver operating characteristics (ROC) analysis forCS, XGBoost, and RF showed a significantly greater diagnostic power than the DT. However, in ROC analysis for PA, only RF exhibited better diagnostic performance than DT. Conclusion: The combination of LC-MS-based steroid profiling with machine learning algorithms could be a promising one-stepdiagnostic approach for the classification of adrenal tumor subtypes.
자동차 도장부스의 휘발성 유기화합물 최적 관리를 위한 기술적 고찰
전동혁,김수현,임혁,유지호,김상도,최호경,이시훈,정태경,정용 한국에너지기후변화학회 2022 에너지기후변화학회지 Vol.17 No.2
Volatile organic compounds (VOCs) emitted from automotive paint booths around living space aredischarged into the atmosphere due to legal problems, narrow installation area, and poor maintenance, even though airpollution prevention facilities are installed on site. In this study, the technologies suitable for VOCs treatment fromautomotive paint booths were recommended through identification of current status and technical review. Among thecurrently commercialized VOCs treatment technologies, the most suitable technology considering the small installationarea is the fixed bed adsorption tower. Difficulties in adsorbent maintenance can be solved through public management. The public management technology of adsorbent consists of as follow: technology for joint regeneration of adsorbentsat multiple sites; IoT smart platform monitoring VOCs emissions in real time; VOCs to energy during adsorbentregeneration. For the successful application of the mentioned technologies, it is necessary to properly manage theenvironment through voluntary participation of automotive repair companies, energy optimization of public managementfacilities, and revision of laws and regulations