• Korean Journal of Audiology
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• v.24 no.2
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• pp.53-60
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• 2020

Several novel animal models that represent the pathophysiological process of endolymphatic hydrops (ELH) of Meniere's disease (MD) have been developed. Animal models are important to identify and characterize the pathophysiology of ELH and to corroborate molecular and genetic findings in humans. This review of the current animal models will be useful in understanding the pathophysiology of and developing proper treatments for MD. Surgical animal models will be replaced by medication-induced animal models. Study models previously developed in guinea pigs will be developed in several smaller animals for ease of conducting molecular analysis. In this review, we provided updated resources including our previous studies regarding the current and desirable animal models for MD.

• Journal of Audiology & Otology
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• v.24 no.2
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• pp.53-60
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• 2020

Several novel animal models that represent the pathophysiological process of endolymphatic hydrops (ELH) of Meniere's disease (MD) have been developed. Animal models are important to identify and characterize the pathophysiology of ELH and to corroborate molecular and genetic findings in humans. This review of the current animal models will be useful in understanding the pathophysiology of and developing proper treatments for MD. Surgical animal models will be replaced by medication-induced animal models. Study models previously developed in guinea pigs will be developed in several smaller animals for ease of conducting molecular analysis. In this review, we provided updated resources including our previous studies regarding the current and desirable animal models for MD.

• Journal of Yeungnam Medical Science
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• v.40 no.1
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• pp.23-29
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• 2023

The pathological hallmark of rheumatoid arthritis (RA) is a synovial pannus that comprises proliferating and invasive fibroblast-like synoviocytes, infiltrating inflammatory cells, and an associated neoangiogenic response. Animal models have been established to study these pathological features of human RA. Spontaneous and induced animal models of RA primarily reflect inflammatory aspects of the disease. Among various induced animal models, collagen-induced arthritis (CIA) and collagen antibody-induced arthritis (CAIA) models are widely used to study the pathogenesis of RA. Improved transplantation techniques for severe combined immunodeficiency (SCID) mouse models of RA can be used to evaluate the effectiveness of potential therapeutics in human tissues and cells. This review provides basic information on various animal models of RA, including CIA and CAIA. In addition, we describe a SCID mouse coimplantation model that can measure the long-distance migration of human RA synoviocytes and cartilage destruction induced by these cells.

• Korean Journal of Biological Psychiatry
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• v.6 no.2
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• pp.161-169
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• 1999

There are a number of approaches in developing experimental models for depression, but there is no such thing as a best model for depressive syndrome. Animal models are subject to the obvious limitations inherent in the assumption that human psychopathology can be represented accurately in lower animals. Recently, the concern increasingly is to develop a variety of experimental paradigms in animals to study selected aspects of human psychopathology, and animal models should be understood as basically experimental preparations that are developed to carry out these objects. Therefore, a battery of a variety of animal models should be applied to permit detailed pathophysiological studies and to develop new antidepressant treatments. Animal models of depression basically consider behavioral isomorphism with the human depression a plus, but not a req-uirement, and the model behavior should be defined operationally in order to be reproduced reliably by other researchers and be responsive to those agents possessing demonstrated clinical efficacy in human depression. In conclusion, animal models of depression have played a significant role in elucidating pathophysiology of depression and developing current treatments for depression, but there is no single comprehensive model for depression until now. Each of the proposed animal model has its advantages and limitations. In other words, certain paradigms are suitable for studying certain phenomena, whereas others are more suitable for studying other aspects. The best model for depression depends upon what the question is.

• Journal of Medicine and Life Science
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• v.15 no.2
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• pp.42-45
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• 2018

Colorectal cancer (CRC) is a third leading cause of cancer-related death in cancer patients. Sporadic and inflammation-related colon carcinogenesis are major mechanism of colorectal cancer. In vivo CRC models have been developed and implicated to understand their mechanisms upon a different type of CRC. Moreover, recently animal models have played important roles in chemopreventive and preclinical trials over the years. In this mini-review, the aim is to introduce various animal models of CRC and help the understanding to establish in vivo experimental plans according to the cancer type of CRC.

• Journal of the Korean Applied Science and Technology
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• v.30 no.4
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• pp.622-634
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• 2013

The multi-origin of obesity and its associated diseases made it's a complex area of biomedical science research and severe health disorder. From the 1970s to onwards this health problem turned to an epidemic without having any report of declining yet and it created a red alert to the health sector. Meanwhile, many animal models have been developed to study the lethal effect of obesity. In consequence, many drugs, therapies and strategies have already been adopted based on the findings of those animal models. However, many complicated things based on molecular and generic mechanism has not been clarified to the date. Thus, it is important to develop a need based animal model for the better understanding and strategic planning to eliminate/avoid the obesity disorder. Therefore, the present review would unveil the pros and cons of presently established animal models for obesity research. In addition, it would indicate the required turning direction for further obesity and obesity based disease research.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.1
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• pp.61-70
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• 2020

To estimate the ventilation volume of mechanically ventilated swine farms, various regression models were applied, and errors were compared to select the regression model that can best simulate actual data. Linear regression, linear spline, polynomial regression (degrees 2 and 3), logistic curve, generalized additive model (GAM), and gompertz curve were compared. Overfitting models were excluded even when the error rate was small. The evaluation criteria were root mean square error (RMSE) and mean absolute percentage error (MAPE). The evaluation results indicated that degree 3 exhibited the lowest error rate; however, an overestimation contradiction was observed in a certain section. The logistic curve was the most stable and superior to all the models. In the estimation of ventilation volume by all of the models, the estimated ventilation volume of the logistic curve was the smallest except for the model with a large error rate and the overestimated model.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.5
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• pp.677-684
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• 2008

Accurate estimation of dry matter intake (DMI) is a prerequisite to meet animal performance targets without penalizing animal health and the environment. The objective of the current study was to evaluate some of the existing models in order to predict DMI when lactating dairy cows were offered a total mixed ration containing a high level of concentrates and locally produced agricultural by-products. Six popular models were chosen for DMI prediction (Brown et al., 1977; Rayburn and Fox, 1993; Agriculture Forestry and Fisheries Research Council Secretariat, 1999; National Research Council (NRC), 2001; Cornell Net Carbohydrate and Protein System (CNCPS), Fox et al., 2003; Fuentes-Pila et al., 2003). Databases for DMI comparison were constructed from two different sources: i) 12 commercial farm investigations and ii) a controlled dairy cow experiment. The model evaluation was performed using two different methods: i) linear regression analysis and ii) mean square error prediction analysis. In the commercial farm investigation, DMI predicted by Fuentes-Pila et al. (2003) was the most accurate when compared with the actual mean DMI, whilst the CNCPS prediction showed larger mean bias (difference between mean predicted and mean observed values). Similar results were observed in the controlled dairy cow experiment where the mean bias by Fuentes-Pila et al. (2003) was the smallest of all six chosen models. The more accurate prediction by Fuentes-Pila et al. (2003) could be attributed to the inclusion of dietary factors, particularly fiber as these factors were not considered in some models (i.e. NRC, 2001; CNCPS (Fox et al., 2003)). Linear regression analysis had little meaningful biological significance when evaluating models for prediction of DMI in this study. Further research is required to improve the accuracy of the models, and may recommend more mechanistic approaches to investigate feedstuffs (common to the Asian region), animal genotype, environmental conditions and their interaction, as the majority of the models employed are based on empirical approaches.

• Journal of Embryo Transfer
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• v.31 no.1
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• pp.97-107
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• 2016

Although many diseases could be treated by the development of modern medicine, there are some incurable diseases including brain cancer, Alzheimer disease, etc. To study human brain cancer, various animal models were reported. Among these animal models, mouse models are valuable tools for understanding brain cancer characteristics. In spite of many mouse brain cancer models, it has been difficult to find a new target molecule for the treatment of brain cancer. One of the reasons is absence of large animal model which makes conducting preclinical trials. In this article, we review a recent study of molecular characteristics of human brain cancer, their genetic mutation and comparative analysis of the mouse brain cancer model. Finally, we suggest the need for development of large animal models using somatic cell nuclear transfer in translational research.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.5
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• pp.607-614
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• 2016

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3-L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of $polynomials{\times}3$ types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea.