• Clinical and Experimental Pediatrics
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• 제62권8호
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• pp.287-291
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• 2019

Fecal calprotectin (FC) is a calcium- and zinc-binding protein of the S100 family, mainly expressed by neutrophils and released during inflammation. FC became an increasingly useful tool both for gastroenterologists and for general practitioners for distinguishing inflammatory bowel disease (IBD) from irritable bowel syndrome. Increasing evidences support the use of this biomarker for diagnosis, follow-up and evaluation of response to therapy of several pediatric gastrointestinal diseases, ranging from IBD to nonspecific colitis and necrotizing enterocolitis. This article summarizes the current literature on the use of FC in clinical practice.

• 생물정신의학
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• 제30권1호
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• pp.24-30
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• 2023

Objectives Alzheimer's disease (AD) is the most common form of dementia in older adults, damaging the brain and resulting in impaired memory, thinking, and behavior. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. The aim of our study was to identify differentially expressed genes associated with AD and combined biomarkers among them to improve AD risk prediction accuracy. Methods Machine learning methods were used to compare the performance of the identified combined biomarkers. In this study, three publicly available gene expression datasets from the hippocampal brain region were used. Results We detected 31 significant common genes from two different microarray datasets using the limma package. Some of them belonged to 11 biological pathways. Combined biomarkers were identified in two microarray datasets and were evaluated in a different dataset. The performance of the predictive models using the combined biomarkers was superior to those of models using a single gene. When two genes were combined, the most predictive gene set in the evaluation dataset was ATR and PRKCB when linear discriminant analysis was applied. Conclusions Combined biomarkers showed good performance in predicting the risk of AD. The constructed predictive nomogram using combined biomarkers could easily be used by clinicians to identify high-risk individuals so that more efficient trials could be designed to reduce the incidence of AD.

• The Korean Journal of Physiology and Pharmacology
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• 제18권6호
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• pp.447-456
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• 2014

Until now, a disease-modifying therapy (DMT) that has an ability to slow or arrest Alzheimer's disease (AD) progression has not been developed, and all clinical trials involving AD patients enrolled by clinical assessment alone also have not been successful. Given the growing consensus that the DMT is likely to require treatment initiation well before full-blown dementia emerges, the early detection of AD will provide opportunities to successfully identify new drugs that slow the course of AD pathology. Recent advances in early detection of AD and prediction of progression of the disease using various biomarkers, including cerebrospinal fluid (CSF) $A{\beta}_{1-42}$, total tau and p-tau181 levels, and imagining biomarkers, are now being actively integrated into the designs of AD clinical trials. In terms of therapeutic mechanisms, monitoring these markers may be helpful for go/no-go decision making as well as surrogate markers for disease severity or progression. Furthermore, CSF biomarkers can be used as a tool to enrich patients for clinical trials with prospect of increasing statistical power and reducing costs in drug development. However, the standardization of technical aspects of analysis of these biomarkers is an essential prerequisite to the clinical uses. To accomplish this, global efforts are underway to standardize CSF biomarker measurements and a quality control program supported by the Alzheimer's Association. The current review summarizes therapeutic targets of developing drugs in AD pathophysiology, and provides the most recent advances in the clinical utility of CSF biomarkers and the integration of CSF biomarkers in current clinical trials.

• 한국멀티미디어학회논문지
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• 제22권10호
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• 한국미생물·생명공학회지
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• 제49권2호
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• pp.264-268
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• 2021

Tuberculosis (TB) is a major infectious disease that threatens the life and health of people globally. Here, we performed a metabolomic analysis of serum samples from patients with intractable TB to identify biomarkers that might shorten the TB treatment period. Serum samples collected at the commencement of patients' treatment and healthy controls were analyzed using the capillary electrophoresis and time-of-flight mass spectrometry metabolome analysis method. The analysis identified the metabolites cystine, kynurenine, glyceric acid, and cystathionine, which might be useful markers for monitoring the TB treatment course. Furthermore, our research may provide experimental data to develop potential biomarkers in the TB treatment course.

• Toxicological Research
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• 제32권1호
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• pp.47-56
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• 2016

The identification of biomarkers for the early detection of acute kidney injury (AKI) is clinically important. Acute kidney injury (AKI) in critically ill patients is closely associated with increased morbidity and mortality. Conventional biomarkers, such as serum creatinine (SCr) and blood urea nitrogen (BUN), are frequently used to diagnose AKI. However, these biomarkers increase only after significant structural damage has occurred. Recent efforts have focused on identification and validation of new noninvasive biomarkers for the early detection of AKI, prior to extensive structural damage. Furthermore, AKI biomarkers can provide valuable insight into the molecular mechanisms of this complex and heterogeneous disease. Our previous study suggested that pyruvate kinase M2 (PKM2), which is excreted in the urine, is a sensitive biomarker for nephrotoxicity. To appropriately and optimally utilize PKM2 as a biomarker for AKI requires its complete characterization. This review highlights the major studies that have addressed the diagnostic and prognostic predictive power of biomarkers for AKI and assesses the potential usage of PKM2 as an early biomarker for AKI. We summarize the current state of knowledge regarding the role of biomarkers and the molecular and cellular mechanisms of AKI. This review will elucidate the biological basis of specific biomarkers that will contribute to improving the early detection and diagnosis of AKI.

• BMB Reports
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• 제46권7호
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• pp.338-345
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• 2013

microRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by targeting the 3'-untranslated region of multiple target genes. Pathogenesis results from defects in several gene sets; therefore, disease progression could be prevented using miRNAs targeting multiple genes. Moreover, recent studies suggest that miRNAs reflect the stage of the specific disease, such as carcinogenesis. Cystic diseases, including polycystic kidney disease, polycystic liver disease, pancreatic cystic disease, and ovarian cystic disease, have common processes of cyst formation in the specific organ. Specifically, epithelial cells initiate abnormal cell proliferation and apoptosis as a result of alterations to key genes. Cysts are caused by fluid accumulation in the lumen. However, the molecular mechanisms underlying cyst formation and progression remain unclear. This review aims to introduce the key miRNAs related to cyst formation, and we suggest that miRNAs could be useful biomarkers and potential therapeutic targets in several cystic diseases.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제3권10호
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• pp.421-428
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• 2014

파킨슨병은 뇌의 흑질 영역에서 도파민계 신경이 파괴되는 질병으로 알츠하이머병과 함께 대표적인 퇴행성 뇌 질환이다. 현재까지 병을 완치시킬 수 있는 치료법은 없지만 병의 진행을 완화시킬 수 있는 치료법이 존재하기 때문에 병의 진단이 굉장히 중요하다. 파킨슨병을 진단하기 위한 과거의 연구는 대부분 단일 바이오마커를 이용한 것으로 이러한 방법은 파킨슨병 환자를 높은 정확도로 진단할 수 있지만 정상인에 대한 진단은 상대적으로 낮은 성능의 한계성이 존재한다. 따라서 본 연구에서는 생화학적 바이오마커인 뇌척수액 내의 ${\alpha}$-synuclein 단백질 수치와 영상학적 바이오마커인 확산 텐서 영상의 여러 모수들을 결합하여 특징으로 사용하는 파킨슨병 진단 모델을 개발하고 성능을 평가하였다. 진단을 위해 개발된 모든 모델은 10-fold cross validation 성능평가에서 정확도가 최고 91.3%의 높은 성능을 보였으며, test 성능평가에서는 확산 텐서 영상의 모수들 중 FA와 ${\alpha}$-synuclein 단백질 수치가 결합된 모델, MO와 ${\alpha}$-synuclein 단백질 수치가 결합된 두 모델에서 최고 72%의 정확도 성능을 보여 파킨슨병의 진단에 유용하게 사용될 수 있는 가능성을 제시하였다. 파킨슨병의 진단을 위해 개발된 모델의 영상학적 특징 벡터를 통하여 파킨슨병 환자와 정상인의 신경섬유 경로의 특징을 분석하였다.

• BMB Reports
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• 제53권6호
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• pp.299-310
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• 2020

Chronic liver disease progresses through several stages, fatty liver, steatohepatitis, cirrhosis, and eventually, it leads to hepatocellular carcinoma (HCC) over a long period of time. Since a large proportion of patients with HCC are accompanied by cirrhosis, it is considered to be an important factor in the diagnosis of liver cancer. This is because cirrhosis leads to an irreversible harmful effect, but the early stages of chronic liver disease could be reversed to a healthy state. Therefore, the discovery of biomarkers that could identify the early stages of chronic liver disease is important to prevent serious liver damage. Biomarker discovery at liver cancer and cirrhosis has enhanced the development of sequencing technology. Next generation sequencing (NGS) is one of the representative technical innovations in the biological field in the recent decades and it is the most important thing to design for research on what type of sequencing methods are suitable and how to handle the analysis steps for data integration. In this review, we comprehensively summarized NGS techniques for identifying genome, transcriptome, DNA methylome and 3D/4D chromatin structure, and introduced framework of processing data set and integrating multi-omics data for uncovering biomarkers.

• Parasites, Hosts and Diseases
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• 제58권6호
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• pp.647-652
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• 2020

Clonorchis sinensis is the most common fish-borne intestinal parasite in Korea. The aim of the present investigation was to survey the status of C. sinensis infection and analyze associated risk factors in residents of Haman-gun, Gyeongsangnam-do. A total of 5,114 residents from 10 administrative towns/villages voluntarily agreed to participate in the study, which comprised fecal examination, a questionnaire survey for risk factors, ultrasonography, and enzyme-linked immunosorbent assay for cancer biomarker detection in the blood. We detected C. sinensis eggs in 5.3% of the subjects. By region, Gunbuk-myeon had the highest number of residents with C. sinensis eggs. The infection rate and intensity were higher in male than in female residents. Based on the risk factor questionnaire, infection was highly associated with drinking, a history of C. sinensis infection, and the practice of eating of raw freshwater fish. Extension of the bile duct, infection intensity, and cancer biomarker detection significantly correlated with the presence of eggs in the study population. In conclusion, the development of feasible, long-term control policies and strategies for the elimination of C. sinensis in Korea is still required.