• BMB Reports
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• v.56 no.1
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• pp.32-42
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• 2023

Heart disease is one of the major life-threatening diseases with high mortality and incidence worldwide. Several model systems, such as primary cells and animals, have been used to understand heart diseases and establish appropriate treatments. However, they have limitations in accuracy and reproducibility in recapitulating disease pathophysiology and evaluating drug responses. In recent years, three-dimensional (3D) cardiac tissue models produced using tissue engineering technology and human cells have outperformed conventional models. In particular, the integration of cell reprogramming techniques with bioengineering platforms (e.g., microfluidics, scaffolds, bioprinting, and biophysical stimuli) has facilitated the development of heart-on-a-chip, cardiac spheroid/organoid, and engineered heart tissue (EHT) to recapitulate the structural and functional features of the native human heart. These cardiac models have improved heart disease modeling and toxicological evaluation. In this review, we summarize the cell types for the fabrication of cardiac tissue models, introduce diverse 3D human cardiac tissue models, and discuss the strategies to enhance their complexity and maturity. Finally, recent studies in the modeling of various heart diseases are reviewed.

• 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 Korean Neurosurgical Society
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• v.65 no.4
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• pp.489-498
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• 2022

Spatial insight into intracranial pathology and structure is important for neurosurgeons to perform safe and successful surgeries. Three-dimensional (3D) printing technology in the medical field has made it possible to produce intuitive models that can help with spatial perception. Recent advances in 3D-printed disease models have removed barriers to entering the clinical field and medical market, such as precision and texture reality, speed of production, and cost. The 3D-printed disease model is now ready to be actively applied to daily clinical practice in neurosurgical planning, simulation, and training. In this review, the development of 3D-printed neurosurgical disease models and their application are summarized and discussed.

• Food Science of Animal Resources
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• v.42 no.6
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• pp.981-995
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• 2022

Cognitive dysfunction is a common symptom of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and Huntington's disease, and is known to be caused by the structural and functional loss of neurons. Many natural agents that can improve cognitive function have been developed and assessed for efficacy using various cognitive deficit animal models. As the gut environment is known to be closely connected to brain function, probiotics are attracting attention as an effective treatment target that can prevent and mitigate cognitive deficits as a result of neurodegenerative diseases. Thus, the objective of this review is to provide useful information about the types and characteristics of cognitive deficit animal models, which can be used to evaluate the anti-cognitive effects of probiotics. In addition, this work reviewed recent studies describing the effects and treatment conditions of probiotics on cognitive deficit animal models. Collectively, this review shows the potential of probiotics as edible natural agents that can mitigate cognitive impairment. It also provides useful information for the design of probiotic treatments for cognitive deficit patients in future clinical studies.

• ETRI Journal
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• v.46 no.1
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• pp.96-105
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• 2024

We propose a method to automatically predict Alzheimer's disease from speech data using the ChatGPT large language model. Alzheimer's disease patients often exhibit distinctive characteristics when describing images, such as difficulties in recalling words, grammar errors, repetitive language, and incoherent narratives. For prediction, we initially employ a speech recognition system to transcribe participants' speech into text. We then gather opinions by inputting the transcribed text into ChatGPT as well as a prompt designed to solicit fluency evaluations. Subsequently, we extract embeddings from the speech, text, and opinions by the pretrained models. Finally, we use a classifier consisting of transformer blocks and linear layers to identify participants with this type of dementia. Experiments are conducted using the extensively used ADReSSo dataset. The results yield a maximum accuracy of 87.3% when speech, text, and opinions are used in conjunction. This finding suggests the potential of leveraging evaluation feedback from language models to address challenges in Alzheimer's disease recognition.

• Archives of Pharmacal Research
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• v.26 no.6
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• pp.433-440
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• 2003

Ulcerative colitis (UC) is an inflammatory bowel disease of unknown etiology limited to the large intestine. The disease is prevalent in industrial societies and is associated with specific ethnic populations. A number of murine models, each focused on distinct aspects of the disease process, were developed over the past 20 years to further our understanding of the pathogenesis of UC. These models have been and remain our best resource for the study of the disorder as a result of their homology to human UC and the ease in which they can be manipulated and examined. This review examines and distills what has been learned from these models and how this information is related back to human UC.

• 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.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.9 no.1
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• pp.3-8
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• 2023

Parkinson's disease is a neurodegenerative disease caused by damage to brain neurons related to dopamine. Non-clinical animal models mainly used in Parkinson's disease research include drug-induced models of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine and 6-hydroxydopamine, and genetically modified transgenic animal models. Parkinson's diagnosis can be made using brain imaging of the substantia nigra-striatal dopamine system and using a radiotracer that specifically binds to the dopamine transporter. In this study, ¹⁸F-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane was used to confirm the image evaluation cutoff between normal and parkinson's disease models, and to confirm model persistence over time. In addition, the efficacy of single or combined administration of clinically used therapeutic drugs in parkinson's animal models was evaluated. Image analysis was performed using the PMOD software. Converted to standardized uptake value, and analyzed by standardized uptake value ratio by dividing the average value of left striatum by the average value of right striatum obtained by applying positron emission tomography images to the atlas magnetic resonance template. The image cutoff of the normal and the parkinson's disease model was calculated as SUVR=0.829, and it was confirmed that it was maintained during the test period. In the three-drug combination administration group, the right and left striatum showed a high symmetry of more than 0.942 on average and recovered significantly. Images using ¹⁸F-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane are thought to be able to diagnose and evaluate treatment efficacy of non-clinical Parkinson's disease.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.149-158
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• 2024

Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.