• Journal of Microbiology
• /
• 제43권spc1호
• /
• pp.118-131
• /
• 2005

Vibrio vulnificus is an opportunistic pathogen of humans that has the capability of causing rare, yet devastating disease. The bacteria are naturally present in estuarine environments and frequently contaminate seafoods. Within days of consuming uncooked, contaminated seafood, predisposed individuals can succumb to sepsis. Additionally, in otherwise healthy people, V. vulnificus causes wound infection that can require amputation or lead to sepsis. These diseases share the characteristics that the bacteria multiply extremely rapidly in host tissues and cause extensive damage. Despite the analysis of virulence for over 20 years using a combination of animal and cell culture models, surprisingly little is known about the mechanisms by which V. vulnificus causes disease. This is in part because of differences observed using animal models that involve infection with bacteria versus injection of toxins. However, the increasing use of genetic analysis coupled with detailed animal models is revealing new insight into the pathogenesis of V. vulnificus disease.

• 사상체질의학회지
• /
• 제22권3호
• /
• pp.18-28
• /
• 2010

1. Objectives: Purpose of this paper is to study the reinterpretation of Taeyang disease(太陽病) in "Shanghanlun(傷寒論)" based on the pathologic perspective of Sa-sang Constitutional Medicine by comparing factors as pathologic mechanisms, clinical symptoms, and remedies. 2. Methods: The texts referred to pathologic mechanisms, clinical symptoms, and remedies of Taeyang disease(太陽病) described in "Donguisusebowon Gabobon(東醫壽世保元 甲午本)", "Donguisusebowon Sinchookbon(東醫壽世保元 辛丑本)", and Shanghanlun(傷寒論)" in "Donguibogam(東醫寶鑑)" were analysed. 3. Results and Conclusions 1) Early phase of Ulgwang symptomatic pattern(鬱狂證 初證) and of Mangyang symptomatic pattern(亡陽證初證) of Kidney Heat-based Exterior Heat disease(腎受熱表熱病), a category of Soeumin(少陰人) diseases, were described by adopting pathologic models of Taeyang-sangpung symptomatic pattern(太陽傷風證) and Sanghan-hyeol symptomatic pattern(傷寒血證) from "Sanghanlun(傷寒論)". 2) Soyang-sangpung symptomatic pattern(少陽傷風證) of Spleen Cold-based Exterior Cold diseae (脾受寒表寒病) and Hyunggyeok-yeol symptomatic pattern(胸膈熱證) of Stomach Heat-based Interior Heat disease(胃受熱裏熱病), categories of Soyangin(少陽人) diseases, were described by adopting pathologic models of Taeyang-yangsangpunghan symptomatic pattern(太陽兩傷風寒證), Soyang-sangpung symptomatic pattern(少陽傷風證) and Tayangbyong-sahak symptomatic pattern(太陽病似瘧證) from "Sanghanlun(傷寒論)". 3) Baechu-pyo symptomatic pattern(背顀表病輕證) and Hangual symptomatic pattern(寒厥證) of Esophagus Cold-based Exterior Cold disease(胃脘受寒表寒病), a category of Taeeumin(太陰人) diseases, was described by adopting pathologic models of Taeyang-sanghan symptomatic pattern(太陽傷寒證) and Hangual symptomatic pattern(寒厥證) from "Sanghanlun(傷寒論)". 4) Je-Ma Lee reinterpreted various diseases classified as Taeyang disease(太陽病) with the pathologic perspective of Sa-sang Constitutional Medicine. Different from existing medicine, diseases were analysed and treated by the standard, constitution of the patient.

• Journal of Multimedia Information System
• /
• 제6권4호
• /
• pp.209-216
• /
• 2019

Over the past decade, researchers were able to solve complex medical problems as well as acquire deeper understanding of entire issue due to the availability of machine learning techniques, particularly predictive algorithms and automatic recognition of patterns in medical imaging. In this study, a technique called transfer learning has been utilized to classify Magnetic Resonance (MR) images by a pre-trained Convolutional Neural Network (CNN). Rather than training an entire model from scratch, transfer learning approach uses the CNN model by fine-tuning them, to classify MR images into Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC). The performance of this method has been evaluated over Alzheimer's Disease Neuroimaging (ADNI) dataset by changing the learning rate of the model. Moreover, in this study, in order to demonstrate the transfer learning approach we utilize different pre-trained deep learning models such as GoogLeNet, VGG-16, AlexNet and ResNet-18, and compare their efficiency to classify AD. The overall classification accuracy resulted by GoogLeNet for training and testing was 99.84% and 98.25% respectively, which was exceptionally more than other models training and testing accuracies.

• BMB Reports
• /
• 제46권2호
• /
• pp.73-79
• /
• 2013

Polycystic kidney disease (PKD) is a common hereditary disorder which is characterized by fluid-filled cysts in the kidney. Mutation in either PKD1, encoding polycystin-1 (PC1), or PKD2, encoding polycystin-2 (PC2), are causative genes of PKD. Recent studies indicate that renal cilia, known as mechanosensors, detecting flow stimulation through renal tubules, have a critical function in maintaining homeostasis of renal epithelial cells. Because most proteins related to PKD are localized to renal cilia or have a function in ciliogenesis. PC1/PC2 heterodimer is localized to the cilia, playing a role in calcium channels. Also, disruptions of ciliary proteins, except for PC1 and PC2, could be involved in the induction of polycystic kidney disease. Based on these findings, various PKD mice models were produced to understand the roles of primary cilia defects in renal cyst formation. In this review, we will describe the general role of cilia in renal epithelial cells, and the relationship between ciliary defects and PKD. We also discuss mouse models of PKD related to ciliary defects based on recent studies.

• 한국전문물리치료학회지
• /
• 제28권3호
• /
• pp.177-185
• /
• 2021

Background: Muscle undergoes change continuously with aging. Sarcopenia, in which muscle mass decrease with aging, is associated with various diseases, the risk of falling, and the deterioration of quality of life. Obesity and sarcopenia also have a synergy effect on the disease of the older adults. Objects: This study examined the risk factors for sarcopenia, sarcopenic obesity, and sarcopenia without obesity and developed prediction models. Methods: This machine-learning study used the 2008-2011 Korea National Health and Nutrition Examination Surveys in the analysis. After data curation, 5,563 older participants were selected, of whom 1,169 had sarcopenia, 538 had sarcopenic obesity, and 631 had sarcopenia without obesity; the remaining 4,394 were normal. Decision tree and random forest models were used to identify risk factors. Results: The risk factors for sarcopenia chosen by both methods were body mass index (BMI) and duration of moderate physical activity; those for sarcopenic obesity were sex, BMI, and duration of moderate physical activity; and those for sarcopenia without obesity were BMI and sex. The areas under the receiver operating characteristic curves of all prediction models exceeded 0.75. BMI could predict sarcopenia-related disease. Conclusion: Risk factors for sarcopenia-related diseases should be identified and programs for sarcopenia-related disease prevention should be developed. Data-mining research using population data should be conducted to enhance the effectiveness of early treatment for people with sarcopenia-related diseases through predictive models.

• Asian Pacific Journal of Cancer Prevention
• /
• 제14권6호
• /
• pp.3793-3798
• /
• 2013

The role of protease-activated receptors (PARs) in lung tumors is controversial. Although PAR4 is preferentially expressed in human lung tissues, its possible significance in lung cancer has not been defined. The studies reported herein used a combination of clinical observations and molecular methods. Surgically resected lung adenocarcinomas and associated adjacent normal lung tissues were collected and BEAS-2B and NCI-H157 cell lines were grown in tissue culture. PAR4 expression was evaluated by RT-PCR, RT-qPCR, Western blotting and immunohistochemistry analysis. The results showed that PAR4 mRNA expression was generally decreased in lung adenocarcinoma tissues as compared with matched noncancerous tissues (67.7%) and was associated with poor differentiation (p=0.017) and metastasis (p=0.04). Western blotting and immunohistochemical analysis also showed that PAR4 protein levels were mostly decreased in lung adenocarcinoma tissues (61.3%), and were also associated with poor differentiation (p=0.035) and clinical stage (p=0.027). Moreover, PAR4 expression was decreased in NCI-H157 cells as compared with BEAS-2B cells. In conclusion, PAR4 expression is significantly decreased in lung adenocarcinoma, and down-regulation of PAR4 is associated with a more clinically aggressive phenotype. PAR4 may acts as a tumor suppressor in lung adenocarcinoma.

• Journal of Information Processing Systems
• /
• 제17권6호
• /
• pp.1115-1126
• /
• 2021

Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

• IMMUNE NETWORK
• /
• 제21권2호
• /
• pp.12.1-12.17
• /
• 2021

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the emergence of SARS-CoV-2 in the human population in late 2019, it has spread on an unprecedented scale worldwide leading to the first coronavirus pandemic. SARS-CoV-2 infection results in a wide range of clinical manifestations from asymptomatic to fatal cases. Although intensive research has been undertaken to increase understanding of the complex biology of SARS-CoV-2 infection, the detailed mechanisms underpinning the severe pathogenesis and interactions between the virus and the host immune response are not well understood. Thus, the development of appropriate animal models that recapitulate human clinical manifestations and immune responses against SARS-CoV-2 is crucial. Although many animal models are currently available for the study of SARS-CoV-2 infection, each has distinct advantages and disadvantages, and some models show variable results between and within species. Thus, we aim to discuss the different animal models, including mice, hamsters, ferrets, and non-human primates, employed for SARS-CoV-2 infection studies and outline their individual strengths and limitations for use in studies aimed at increasing understanding of coronavirus pathogenesis. Moreover, a significant advantage of these animal models is that they can be tailored, providing unique options specific to the scientific goals of each researcher.

• 제3의학
• /
• 제1권1호
• /
• pp.57-89
• /
• 1996

A Typification of diagnosis and treatment(DT) model must be done in order to generalize the objective stage to the result of treatment to internal disease in connection with the type of viscera and bowel symptom. We could find 108 DT models in internal disease from the combination of 18 types of viscera and bowel and 6 types of DT treatment processes. Thus, the typification of 108 models of DT can be viewed as a modeling processes of utilizing DT knowledge at each stage. We argue that objectivity in diagnosis and treatment of internal disease can be obtained practically from typification of DT model.

• BMB Reports
• /
• 제47권7호
• /
• pp.369-375
• /
• 2014

Pituitary adenylate cyclase-activating polypeptide (PACAP) is a pleiotropic bioactive peptide that was first isolated from an ovine hypothalamus in 1989. PACAP belongs to the secretin/glucagon/vasoactive intestinal polypeptide (VIP) superfamily. PACAP is widely distributed in the central and peripheral nervous systems and acts as a neurotransmitter, neuromodulator, and neurotrophic factor via three major receptors (PAC1, VPAC1, and VPAC2). Recent studies have shown a neuroprotective role of PACAP using in vitro and in vivo models. In this review, we briefly summarize the current findings on the neurotrophic and neuroprotective effects of PACAP in different brain injury models, such as cerebral ischemia, Parkinson's disease (PD), and Alzheimer's disease (AD). This review will provide information for the future development of therapeutic strategies in treatment of these neurodegenerative diseases.