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남민경,김구영,윤시은,장자영,김용희,최은하,강성만,임향숙 생화학분자생물학회 2017 Experimental and molecular medicine Vol.49 No.-
The argon plasma jet (Ar-PJ) is widely used in medical fields such as dermatology and dentistry, and it is considered a promising tool for cancer therapy. However, the in vivo effects of Ar-PJ for medical uses have not yet been investigated, and there are no biological tools to determine the appropriate clinical dosages of Ar-PJ. In this study, we used the caudal fin and embryo of zebrafish as novel in vivo tools to evaluate the biosafety of Ar-PJ. Typically, Ar-PJ is known to induce cell death in twodimensional (2D) cell culture systems. By contrast, no detrimental effects of Ar-PJ were shown in our 3D zebrafish systems composed of 2D cells. The Ar-PJ-treated caudal fins grew by an average length of 0.7 mm, similar to the length of the normally regenerating fins. Remarkably, Ar-PJ did not affect the expression patterns of Wnt8a and β-Catenin, which play important roles in fin regeneration. In the embryo system, 85% of the Ar-PJ-treated embryos hatched, and the lateral length of these embryos was ~ 3.3 mm, which are equivalent to the lengths of normal embryos. In particular, vasculogenesis, which is the main cellular process during tissue regeneration and embryogenesis, occurred normally under the Ar-PJ dose used in this study. Therefore, our biosafety evaluation tools that use living model systems can be used to provide an experimental guideline to determine the clinically safe dosage of Ar-PJ.
위장관 수술 후 발생한 장관 누공 환자에서 영양집중지원팀에 의뢰된 내용 분석
정미진 ( Mi Jin Jeong ),유희철 ( Hee Chul Yu ),황시은 ( Si Eun Hwang ),김찬영 ( Chan Young Kim ),이민로 ( Min Ro Lee ),김선형 ( Sun Haeng Kim ),김행순 ( Hyeong Seon Kim ),김주신 ( Ju Sin Kim ),문미경 ( Mi Kyung Moon ),윤완기 ( Wan 한국정맥경장영양학회 2010 한국정맥경장영양학회지 Vol.3 No.1
Purpose: The role of nutrition support for the management of enterocutaneous fistula is primarily one of supportive care to prevent malnutrition and thereby halt further deterioration of an already debilitated patient. This therapy is best managed by a nutritional support team (NST). For activation of the NST, physicians must become more aware of the need for nutrition support in patients, and so referrals are required from physicians. This study examined the referrals to the nutritional support team for patients with postoperative enterocutaneous fistula. Methods: Between March 2007 and May 2009, we reviewed 34 patients with postoperative enterocutaneous fistula and who was referred to the NST. Results: The mean age of the patients was 61.1±11.5years. Twenty seven cases were males and 7 were females. The routes of nutrition support were EN+PN: 32 (55.2%), PN: 16 (27.6%), EN: 8 (13.0%) and oral intake+PN: 2 (3.4%). The direct referrals were 45 (77.6%) and the indirect referrals though the nutritional screening system were 13 (22.4%). The referrals for EN were 40 (69%) and those for PN were 18 (31.0%). The recommendations by the NST were accepted in 48 (82.8%) of the cases. The EN recommendations were accepted in all 40 (100.0%) of the cases. The PN recommendations by direct referral were accepted in 6 of 7 cases, but only 2 of 11 cases were accepted according to indirect referral. Conclusion: More aggressive and thorough follow-up on whether or not to accept the NST recommendation is required. This study shows that regular scheduled nutrition support service orientations for the different staff and departments of the hospital should be held each year. (KJPEN 2010;3(1):45-49)
Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver
신태영,김현숙,이중협,최종석,민현석,조형주,김경욱,강건,김정규,윤시은,박현규,황영욱,김효진,한미연,배은진,윤종우,나군호,이용성 대한비뇨의학회 2020 Investigative and Clinical Urology Vol.61 No.6
Purpose: Volumetry is used in polycystic kidney and liver diseases (PKLDs), including autosomal dominant polycystic kidney disease (ADPKD), to assess disease progression and drug efficiency. However, since no rapid and accurate method for volumetry has been developed, volumetry has not yet been established in clinical practice, hindering the development of therapies for PKLD. This study presents an artificial intelligence (AI)-based volumetry method for PKLD. Materials and Methods: The performance of AI was first evaluated in comparison with ground-truth (GT). We trained a V-net-based convolutional neural network on 175 ADPKD computed tomography (CT) segmentations, which served as the GT and were agreed upon by 3 experts using images from 214 patients analyzed with volumetry. The dice similarity coefficient (DSC), interobserver correlation coefficient (ICC), and Bland–Altman plots of 39 GT and AI segmentations in the validation set were compared. Next, the performance of AI on the segmentation of 50 random CT images was compared with that of 11 PKLD specialists based on the resulting DSC and ICC. Results: The DSC and ICC of the AI were 0.961 and 0.999729, respectively. The error rate was within 3% for approximately 95% of the CT scans (error<1%, 46.2%; 1%≤error<3%, 48.7%). Compared with the specialists, AI showed moderate performance. Furthermore, an outlier in our results confirmed that even PKLD specialists can make mistakes in volumetry. Conclusions: PKLD volumetry using AI was fast and accurate. AI performed comparably to human specialists, suggesting its use may be practical in clinical settings.