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Min Chanhong,Lee Dong Eun,Ryoo Hyun Wook,Jung Haewon,Cho Jae Wan,Kim Yun Jeong,Ahn Jae Yun,Park Jungbae,Mun You Ho,장태창,Jin Sang-Chan 대한응급의학회 2022 Clinical and Experimental Emergency Medicine Vol.9 No.3
Objective High-quality cardiopulmonary resuscitation with chest compression is important for good neurologic outcomes during out-of-hospital cardiac arrest (OHCA). Several types of mechanical chest compression devices have recently been implemented in Korean emergency medical services. This study aimed to identify the effect of prehospital mechanical chest compression device use on the outcomes of OHCA patients. Methods We retrospectively analyzed data drawn from the regional cardiac arrest registry in Daegu, Korea. This registry prospectively collected data from January 2017 to December 2020. Patients aged 18 years or older who experienced cardiac arrest presumed to have a medical etiology were included. The exposure variable was the use of a prehospital mechanical device during transportation by emergency medical technicians. The outcomes measured were neurologic outcomes and survival to discharge. Logistic regression analysis was used. Results Among 3,230 OHCA patients, 1,111 (34.4%) and 2,119 (65.6%) were managed with manual chest compression and with a mechanical chest compression device, respectively. The mechanical chest compression group showed poorer neurologic outcomes than the manual chest compression group (adjusted odds ratio, 0.12; 95% confidence interval, 0.04–0.33) and decreased survival to discharge (adjusted odds ratio, 0.39; 95% confidence interval, 0.19–0.82) after adjustment for confounding variables. Conclusion Prehospital mechanical chest compression device use in OHCA was associated with poorer neurologic outcomes and survival to discharge compared to manual chest compression.
Shang-Guan, Keke,Wang, Min,Htwe, Nang Myint Phyu Sin,Li, Ping,Li, Yaoshen,Qi, Fan,Zhang, Dawei,Cao, Min,Kim, Chanhong,Weng, Haiyong,Cen, Haiyan,Black, Ian M.,Azadi, Parastoo,Carlson, Russell W.,Stacey American Society of Plant Biologists 2018 Plant Physiology Vol.176 No.3
<P>Lipopolysaccharides induce a long-lasting burst of reactive oxygen species that is largely associated with chloroplasts.</P><P>Lipopolysaccharides (LPS) are major components of the outer membrane of gram-negative bacteria and are an important microbe-associated molecular pattern (MAMP) that triggers immune responses in plants and animals. A previous genetic screen in Arabidopsis (<I>Arabidopsis thaliana</I>) identified LIPOOLIGOSACCHARIDE-SPECIFIC REDUCED ELICITATION (LORE), a B-type lectin <I>S</I>-domain receptor kinase, as a sensor of LPS. However, the LPS-activated LORE signaling pathway and associated immune responses remain largely unknown. In this study, we found that LPS trigger biphasic production of reactive oxygen species (ROS) in Arabidopsis. The first transient ROS burst was similar to that induced by another MAMP, flagellin, whereas the second long-lasting burst was induced only by LPS. The LPS-triggered second ROS burst was found to be conserved in a variety of plant species. Microscopic observation of the generation of ROS revealed that the LPS-triggered second ROS burst was largely associated with chloroplasts, and functional chloroplasts were indispensable for this response. The lipid A moiety, the most conserved portion of LPS, appears to be responsible for the second ROS burst. Surprisingly, the LPS- and lipid A-triggered second ROS burst was only partially dependent on LORE. Together, our findings provide insight on the LPS-triggered ROS production and the associated signaling pathway.</P>
암의 이질성 분류를 위한 하이브리드 학습 기반 세포 형태 프로파일링 기법
민찬홍,정현태,양세정,신현정,Min, Chanhong,Jeong, Hyuntae,Yang, Sejung,Shin, Jennifer Hyunjong 대한의용생체공학회 2021 의공학회지 Vol.42 No.5
Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.