1,721,100 research outputs found
Kim Pil Soon, A Great Doctor
No full textKim Pil Soon was born at Sorae Village of Hwang Hye Province, the birth place of the Protestantism in Korean. He was brought up under the strong influence of Christianity and received modern education at Pae Chae School according to the ecommendation of Rev. Underwood. In 1899, Kim Pil Soon, who had been working at Je Joong Won as an assistant and nterpreter of Dr. Sharrocks, was employed by Dr. Avison to help preparing medical textbooks and asked to participate in the medical education. He acquired medical knowledges through his work of translating various medical texts, and which enabled him to teach other medical students. He participated in the administration of the Hospital, taking charge of the provision of meals for in-patients as well as directing the construction of Severance Hospital buildings. And his experience of treating soldiers wounded during the turmoil of the forced dismission of the Korean Army by the Japanese lead him to reflect seriously on Korea’s fate in peril. In addition, he became a member of Sinmin Society, a secret political association, to engage in the independence movement. In 1908, Kim Pil Soon graduated from Severance Hospital Medical School as one of the first seven graduates. On graduation, he was appointed as a professor and took the charge of school affaires in 1910. At first, he worked as a assistant physician of ward and surgery, then he took the responsibility of out-patient clinic in 1911. But suddenly, in December 1911, he exiled to China to escape from the Japanese police who was in pursuit of him on account of his involvement in the so-called 105-Person Affaire, a fabricated affaire served as a pretext for the persecution of independence movement. He continued the independence movement in the form of an ideal village movement and training the Independence Army. In 1919, however, he was poisoned to death in a mysterious way. Kim Pil Soon dedicated himself to the independence movement that demands personal sacrifice: giving up his prospective career as a doctor, professor, and hospital administrator. He no longer remained as a ordinary clinician who treats only diseased persons, but transformed himself to the Great Doctor, a time-old ideal type of doctor in the East Asian countries who treats and cures the diseased nation, by dedicating himself to the independence movement.ope
Fabrication of 3D metal/elastomer hybrid structure using stiffness patterning and folding instability
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How general anesthetics work: from the perspective of reorganized connections within the brain
Full text linkGeneral anesthesia is critical for various procedures and surgeries. Despite the widespread use of anesthetics, their precise mechanisms remain poorly understood. Anesthetics inevitably act on the brain, primarily through the modulation of target receptors. Even if the action is specific to an individual neuron, however, long-range effects can occur due to the tremendous interconnectedness of neuronal activity. The strength of this connectivity can be understood using mathematical models that allow for the study of neuronal connectivity dynamics. These models also allow researchers to develop hypotheses on the candidate mechanisms of action of different types of anesthesia. This review highlights the theoretical background associated with the study of the mechanisms of action of anesthetics. We propose a candidate framework that describes how anesthetics act on the brain and consciousness in general.N
A novel approach to predicting the synergy of anti-cancer drug combinations using document-based feature extraction
Full text linkBackground To reduce drug side effects and enhance their therapeutic effect compared with single drugs, drug combination research, combining two or more drugs, is highly important. Conducting in-vivo and in-vitro experiments on a vast number of drug combinations incurs astronomical time and cost. To reduce the number of combinations, researchers classify whether drug combinations are synergistic through in-silico methods. Since unstructured data, such as biomedical documents, include experimental types, methods, and results, it can be beneficial extracting features from documents to predict anti-cancer drug combination synergy. However, few studies predict anti-cancer drug combination synergy using document-extracted features. Results We present a novel approach for anti-cancer drug combination synergy prediction using document-based feature extraction. Our approach is divided into two steps. First, we extracted documents containing validated anti-cancer drug combinations and cell lines. Drug and cell line synonyms in the extracted documents were converted into representative words, and the documents were preprocessed by tokenization, lemmatization, and stopword removal. Second, the drug and cell line features were extracted from the preprocessed documents, and training data were constructed by feature concatenation. A prediction model based on deep and machine learning was created using the training data. The use of our features yielded higher results compared to the majority of published studies. Conclusions Using our prediction model, researchers can save time and cost on new anti-cancer drug combination discoveries. Additionally, since our feature extraction method does not require structuring of unstructured data, new data can be immediately applied without any data scalability issues.N
Isolation and identification of 18 unrecorded prokaryotic species from the intestinal tracts of aquatic animals in Korea
No full textLee, Jae-Yun, Jeong, Yun-Seok, Kim, Pil Soo, Bae, Dong-Wook Hyun and Jin-Woo (2021): Isolation and identification of 18 unrecorded prokaryotic species from the intestinal tracts of aquatic animals in Korea. Journal of Species Research 10 (1): 1-11, DOI: 10.12651/JSR.2021.10.1.00
Spatially controlled folding instability of moduli-patterned elastic membranes under biaxial compressive stresses
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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