1.Factors Influencing Learning Satisfaction of Migrant Workers in Korea with E-learning-Based Occupational Safety and Health Education.
Safety and Health at Work 2015;6(3):211-217
BACKGROUND: E-learning-based programs have recently been introduced to the occupational safety and health (OSH) education for migrant workers in Korea. The purpose of this study was to investigate how the factors related to migrant workers' backgrounds and the instructional design affect the migrant workers' satisfaction with e-learning-based OSH education. METHODS: The data were collected from the surveys of 300 migrant workers who had participated in an OSH education program. Independent sample t test and one-way analysis of variance were conducted to examine differences in the degree of learning satisfaction using background variables. In addition, correlation analysis and multiple regression analysis were conducted to examine relationships between the instructional design variables and the degree of learning satisfaction. RESULTS: There was no significant difference in the degree of learning satisfaction by gender, age, level of education, number of employees, or type of occupation, except for nationality. Among the instructional design variables, "learning content" (beta = 0.344, p < 0.001) affected the degree of learning satisfaction most significantly, followed by "motivation to learn" (beta = 0.293, p < 0.001), "interactions with learners and instructors" (beta = 0.149, p < 0.01), and "previous experience related to e-learning" (beta = 0.095, p < 0.05). "Learning environment" had no significant influence on the degree of learning satisfaction. CONCLUSION: E-learning-based OSH education for migrant workers may be an effective way to increase their safety knowledge and behavior if the accuracy, credibility, and novelty of learning content; strategies to promote learners' motivation to learn; and interactions with learners and instructors are systematically applied during the development and implementation of e-learning programs.
Education
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Ethnic Groups
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Health Education*
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Humans
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Korea*
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Learning*
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Motivation
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Occupational Health*
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Occupations
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Transients and Migrants*
2.Spectral Analysis of Fetal Heart Rate Variability in Fetal Distress.
Yeonhee CHEON ; Dongjoo KIM ; Daeyoung CHUNG ; Kyungtae PARK ; Youngbo SHIM ; Chulseung LEW ; Sanghoon YI ; Jongchul SHIN ; Hyunggeun LEE ; Soopyung KIM
Korean Journal of Obstetrics and Gynecology 2001;44(5):845-851
OBJECTIVE: To evaluate the usefulness of power spectral analysis on fetal heart rate variability as a new diagnostic method of fetal distress. STUDY DESIGN: Among 76 pregnant women who underwent computerized electronic fetal monitoring and cord blood gas analysis, we divided them into 3 groups, i.e.; normal fetus group (36), presumed distress group (26) and acidemic distress group (14). In order to perform linear analysis on the raw data of the fetal heart rate, after resampling, we performed Fourier transformation and investigated power distributions among very low frequency (VLF), low frequency (LF), high frequency (HF) bands, and autonomic balance (LF/HF). RESULTS: The results of the spectral analysis showed that in normal fetus group, the difference in the distribution of power spectrums of VLF, LF and HF was significantly higher than in presumed distress group and acidemic distress group. In fetal distress, the LF and VLF value (0.0023, 0.0437) were good predictors (sensitivity 97.5%, 75.0% and specificity 86.1%, 94.4%). The LF value (0.0013) was a good predictor in fetal acidemia (sensitivity 97.5% and specificity 86.1%). CONCLUSIONS: A computerized spectral analysis of fetal heart rate variation is a good predictor of fetal distress, which is made automatically and objectively.
Female
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Fetal Blood
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Fetal Distress*
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Fetal Heart*
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Fetal Monitoring
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Fetus
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Fourier Analysis
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Heart Rate, Fetal*
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Humans
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Pregnancy
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Pregnant Women
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Sensitivity and Specificity
3.Single-cell Analysis of CAR-T Cell Activation Reveals A Mixed T1/T2 Response Independent of Differentiation.
Iva XHANGOLLI ; Burak DURA ; GeeHee LEE ; Dongjoo KIM ; Yang XIAO ; Rong FAN
Genomics, Proteomics & Bioinformatics 2019;17(2):129-139
The activation mechanism of chimeric antigen receptor (CAR)-engineered T cells may differ substantially from T cells carrying native T cell receptor, but this difference remains poorly understood. We present the first comprehensive portrait of single-cell level transcriptional and cytokine signatures of anti-CD19/4-1BB/CD28/CD3ζ CAR-T cells upon antigen-specific stimulation. Both CD4 helper T (T) cells and CD8 cytotoxic CAR-T cells are equally effective in directly killing target tumor cells and their cytotoxic activity is associated with the elevation of a range of T1 and T2 signature cytokines, e.g., interferon γ, tumor necrotic factor α, interleukin 5 (IL5), and IL13, as confirmed by the expression of master transcription factor genes TBX21 and GATA3. However, rather than conforming to stringent T1 or T2 subtypes, single-cell analysis reveals that the predominant response is a highly mixed T1/T2 function in the same cell. The regulatory T cell activity, although observed in a small fraction of activated cells, emerges from this hybrid T1/T2 population. Granulocyte-macrophage colony stimulating factor (GM-CSF) is produced from the majority of cells regardless of the polarization states, further contrasting CAR-T to classic T cells. Surprisingly, the cytokine response is minimally associated with differentiation status, although all major differentiation subsets such as naïve, central memory, effector memory, and effector are detected. All these suggest that the activation of CAR-engineered T cells is a canonical process that leads to a highly mixed response combining both type 1 and type 2 cytokines together with GM-CSF, supporting the notion that polyfunctional CAR-T cells correlate with objective response of patients in clinical trials. This work provides new insights into the mechanism of CAR activation and implies the necessity for cellular function assays to characterize the quality of CAR-T infusion products and monitor therapeutic responses in patients.
Antigens
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metabolism
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CTLA-4 Antigen
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metabolism
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Cell Differentiation
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drug effects
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Cell Line
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Cytokines
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metabolism
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Cytotoxicity, Immunologic
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drug effects
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Granulocyte-Macrophage Colony-Stimulating Factor
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pharmacology
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Humans
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Lymphocyte Activation
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drug effects
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immunology
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Lymphocyte Subsets
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drug effects
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metabolism
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Phenotype
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Proteomics
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Receptors, Chimeric Antigen
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metabolism
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Single-Cell Analysis
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methods
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T-Lymphocytes, Regulatory
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drug effects
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metabolism
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Th1 Cells
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cytology
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drug effects
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Th2 Cells
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cytology
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drug effects
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Transcription, Genetic
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drug effects
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Up-Regulation
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drug effects