ABSTRACT
Background and Objectives@#Currently, safety pharmacological tests for the central nervous system depend on animal behavioral analysis. However, due to the subjectivity of behavioral analysis and differences between species, there is a limit to appropriate nervous system toxicity assessment, therefore a new neurotoxicity assessment that can simulate the human central nervous system is required. @*Methods@#and Results: In our study, we developed an in vitro neurotoxicity assessment focusing on neuronal function. To minimize the differences between species and fast screening, hiPSC-derived neurons and a microelectrode array (MEA) that could simultaneously measure the action potentials of the neuronal networks were used. After analyzing the molecular and electrophysiological characters of our neuronal network, we conducted a neurotoxicity assessment on neurotransmitters, neurotoxicants, illicit drugs, and new psychoactive substances (NPS). We found that most substances used in our experiments responded more sensitively to our MEA-based neurotoxicity assessment than to the conventional neurotoxicity assessment. Also, this is the first paper that evaluates various illicit drugs and NPS using MEA-based neurotoxicity assessment using hiPSC-derived neurons. @*Conclusions@#Our study expanded the scope of application of neurotoxicity assessment using hiPSC-derived neurons to NPS, and accumulated evaluation data of various toxic substances for hiPSC-derived neurons.
ABSTRACT
OBJECTIVES: A common problem with analyzing survey data involves incomplete data with either a nonresponse or missing data. The mail questionnaire survey conducted for collecting lifestyle variables on the members of the Korean Elderly Phamacoepidemiologic Cohort(KEPEC) in 1996 contains some nonresponse or missing data. The proper statistical method was applied to evaluate the missing pattern of a specific KEPEC data, which had no missing data in the independent variable and missing data in the response variable, BMI. METHODS: The number of study subjects was 8,689 elderly people. Initially, the BMI and significant variables that influenced the BMI were categorized. After fitting the log-linear model, the probabilities of the people on each category were estimated. The EM algorithm was implemented using a log-linear model to determine the missing mechanism causing the nonresponse. RESULTS: Age, smoking status, and a preference of spicy hot food were chosen as variables that influenced the BMI. As a result of fitting the nonignorable and ignorable nonresponse log-linear model considering these variables, the difference in the deviance in these two models was 0.0034(df=1). CONCLUSION: There is a lot of risk if an inference regarding the variables and large samples is made without considering the pattern of missing data. On the basis of these results, the missing data occurring in the BMI is the ignorable nonresponse. Therefore, when analyzing the BMI in KEPEC data, the inference can be made about the data without considering the missing data.