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질식분만을 위한 경막외 시술 중 발생한 기뇌증 -증례 보고-
손주형,지영석,신영철,윤희조,이인호,전주연 대한마취통증의학회 2012 Anesthesia and pain medicine Vol.7 No.3
Pneumocephalus can be developed after a dural puncture, which occurs in association with epidural procedures. A 37-year-old,gestational age 40 weeks, pregnant woman was admitted for vaginal delivery. She asked for epidural analgesia when she suffers with labor pain. Epidural anesthesia was done at the L3-L4 interspace with the loss of resistance technique, using air. During the identification of the epidural space, an accidental dural puncture was diagnosed by observing a free flow of CSF, through the needle. The patient developed headache 2 hours later. She was treated with hydration,oxygen, analgesics and the autologus blood patch procedure was done, at the L4-L5 interspace. Despite these measures, the patient’s symptoms worsened with nausea and vomiting. A brain CT scan showed the presence of pneumocephalus. After 100%oxygen therapy and metoclopramide injection, she was discharged on postpartum 2 days, without any complications.
치매 진단을 위한 Faster R-CNN 활용 MRI 바이오마커 자동 검출 연동 분류 기술 개발
손주형,김경태,최재영 한국멀티미디어학회 2019 멀티미디어학회논문지 Vol.22 No.10
In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.
NO<sub>X</sub> 가스 검출 특성을 이용한 MWCNT/ZnO 복합체 필름 가스 센서의 메커니즘 분석
손주형,김현수,박용서,장경욱,Son, Ju-Hyung,Kim, Hyun-Soo,Park, Yong-Seo,Jang, Kyung-Uk 한국전기전자재료학회 2018 전기전자재료학회논문지 Vol.31 No.3
In this study, we fabricated an $NO_X$ gas sensor using a composite film of multi-walled carbon nanotubes (MWCNT)/zinc oxide (ZnO). Carbon nanotubes (CNTs) show good electronic conductivity and chemical-stability, and zinc oxide (ZnO) is a wide band gap semiconductor with a large exciton binding energy. Gas sensors require characteristics such as high speed, sensitivity, and selectivity. The fabricated gas sensor was used to detect $NO_X$ gas at different $NO_X$ concentrations. The sensitivity of the gas sensor increased with increasing gas concentrations. Additionally, while changing the temperature inside the chamber containing the MWCNT/ZnO gas sensor, we obtained the sensitivity and normalized responses for detecting $NO_X$ gas in comparison to ZnO and MWCNT film gas sensors. From the experimental results, we confirmed that the gas sensor sensing mechanism was enhanced in the composite-film gas-sensor and that the electronic interaction between MWCNT and ZnO contributed to the improved sensor performance.