폐암 조기 진단을 위한 단백질 바이오마커 측정용 전압-전류법 기반의 나노바이오 분석법 개발

리징징 ( Jingjing Li ),스윈페이 ( Yunpei Si ),누드듀돈타뉴 ( Dieudonne Tanue Nde ),이혜진 ( Hye Jin Lee ) 한국공업화학회 2021 공업화학 Vol.32 No.4

본 논문에서는 이동성이 좋고 경제적이며, 간편하게 일회용 진단칩으로 제작 가능한 스크린 프린팅 한 탄소칩 전극 [screen printed carbon electrode (SPCE)] 기반의 전압전류법 나노물질 융합형 바이오센서를 제작하여 폐암 조기진단에 활용 가능한 단백질 표지 인자 중에 하나인 heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) 단백질의 농도를 정량 분석하고자 하였다. 먼저 SPCE 표면에 금 나노입자를 전기적으로 증착한 후 크로스링커를 이용하여 hnRNP A1에 특이적으로 결합할 수 있는 바이오리셉터인 DNA 압타머를 고정하였다. Ethanolamine을 블로킹 시약으로 사용하여 압타머와 함께 센서 표면에 고정하여 그 표면을 처리함으로써 비특이적인 생물질의 흡착에 의한 방해 신호를 최소화하고자 하였다. DNA칩과 hnRNP A1 용액을 접촉하여 DNA와 hnRNP A1을 결합시킨 후 alkaline phosphatase (ALP) 효소로 접합한 hnRNP A1 항체(anti-hnRNP A1)을 센서칩 표면으로 주입하여 샌드위치 복합체를 형성하고, 이를 기질인 4-aminophenyl phosphate (APP)와 효소-기질 특이적 산화 반응에 의한 전류 변화를 순환 전압전류법과 시차 펄스 전압전류법으로 측정하여 단백질의 농도를 정량적으로 분석하였다. 상기 산화 반응에 의한 피크 전류 변화는 순환 전압전류법과 시차 펄스 전압전류법을 사용할 때 -0.05와 -0.17 V (vs. Ag/AgCl) 전위 값에서 각각 일어났다. 개발한 나노바이오센서를 실제 정상인 혈청 시료 분석에 적용 가능함을 보여줌으로써 혈청 한 방울로 폐암의 조기진단 가능성을 제시하고자 하였다. In this article, a portable and cost-effective voltammetric biosensor with nanoparticles was developed for the measurements of heterogeneous nuclear ribonucleoprotein A1 protein (hnRNP A1) biomarker which can potentially be used for lung cancer diagnosis. Gold nanoparticles were first electrodeposited onto screen printed carbon electrode (SPCE) followed by immobilizing a single stranded DNA aptamer specific to hnRNP A1 onto the electrode surface. Ethanolamine was also used when immobilizing DNA aptamer on the surface to prevent signals from non-specific adsorption events. Sequential injection of hnRNP A1 biomarker and anti-hnRNP A1 conjugated with alkaline phosphatase (ALP) onto the aptamer chip surface allows to form the sandwich complex of DNA aptamer/hnRNP A1/ALP-anti-hnRNP A1 on the electrode surface which further reacted with 4-aminophenyl phosphate (APP). The electrocatalytic reaction of the enzyme, ALP, and the substrate, APP, resulting in the oxidative current response changes at -0.05 and -0.17 V (vs. Ag/AgCl) against the hnRNP A1 concentration was measured using cyclic and differential pulse voltammetry, respectively. The Au nanoparticles-integrated voltammetric biosensor was applied to analyze human normal serum solutions possibly suggesting potential applicability for lung cancer diagnosis.

대장암 진단용 단백질 바이오마커 측정을 위한 바이오센서 개발의 최신 연구 동향

리징징 ( Jingjing Li ),스윈페이 ( Yunpei Si ),이혜진 ( Hye Jin Lee ) 한국공업화학회 2021 공업화학 Vol.32 No.3

Colorectal cancer (CRC) is one of the most prevalent diseases in modern society, constituting a serious threat to global health. Currently, routine clinical screening and early removal of precancerous polyps are the most successful methods for reducing CRC incidence and mortality. However, the high cost and invasive detection of sigmoidoscopy and colonoscopy limited the CRC-screening participation and prevention. The emergence of biosensors provides an inexpensive, sensitive, less invasive tool for detecting CRC disease biomarkers. This review highlights some of recent efforts made on developing biosensors with electrochemical and optical techniques targeting CRC specific protein biomarkers for early diagnosis and prognosis, potential applications, and future perspectives.

온라인 주식 포럼의 핫토픽 탐지를 위한 감성분석 모형의 개발

홍태호(Taeho Hong),이태원(Taewon Lee),리징징(Jingjing Li) 한국지능정보시스템학회 2016 지능정보연구 Vol.22 No.1

소셜 미디어를 이용하는 사용자들이 직접 작성한 의견 혹은 리뷰를 이용하여 상호간의 교류 및 정보를 공유하게 되었다. 이를 통해 고객리뷰를 이용하는 오피니언마이닝, 웹마이닝 및 감성분석 등 다양한 연구분야에서의 연구가 진행되기 시작하였다. 특히, 감성분석은 어떠한 토픽(주제)를 기준으로 직접적으로 글을 작성한 사람들의 태도, 입장 및 감성을 알아내는데 목적을 두고 있다. 고객의 의견을 내포하고 있는 정보 혹은 데이터는 감성분석을 위한 핵심 데이터가 되기 때문에 토픽을 통한 고객들의 의견을 분석하는데 효율적이며, 기업에서는 소비자들의 니즈에 맞는 마케팅 혹은 투자자들의 시장동향에 따른 많은 투자가 이루어지고 있다. 본 연구에서는 중국의 온라인 시나 주식 포럼에서 사용자들이 직접 작성한 포스팅(글)을 이용하여 기존에 제시된 토픽들로부터 핫토픽을 선정하고 탐지하고자 한다. 기존에 사용된 감성 사전을 활용하여 토픽들에 대한 감성값과 극성을 분류하고, 군집분석을 통해 핫토픽을 선정하였다. 핫토픽을 선정하기 위해 k-means 알고리즘을 이용하였으며, 추가로 인공지능기법인 SOM을 적용하여 핫토픽 선정하는 절차를 제시하였다. 또한, 로짓, 의사결정나무, SVM 등의 데이터마이닝 기법을 이용하여 핫토픽 사전 탐지를 하는 감성분석을 위한 모형을 개발하여 관심지수를 통해 선정된 핫토픽과 탐지된 핫토픽을 비교하였다. 본 연구를 통해 핫토픽에 대한 정보 제공함으로써 최신 동향에 대한 흐름을 알 수 있게 되고, 주식 포럼에 대한 핫토픽은 주식 시장에서의 투자자들에게 유용한 정보를 제공하게 될 뿐만 아니라 소비자들의 니즈를 충족시킬 수 있을 것이라 기대된다. Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China"s online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China’s online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum’s topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers’ signals or attitudes towards government policy and firms’ products and services.