다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

http://naturalingredient.org/wp/wp-content/uploads/Burt_Essential_Oils_A_Review_2004.pdf

Essential oils: their antibacterial properties and potential applications in foods--a review.

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Bacterial contamination by Listeria monocytogenes not only puts the public at risk, but also is costly for the food-processing industry. Traditional biochemical methods for pathogen identification require complicated sample preparation for reliable results. Optical scattering technology has been used for identification of bacterial cells in suspension, but with only limited success. Therefore, to improve the efficacy of the identification process using our novel imaging approach, we analyze bacterial colonies grown on solid surfaces. The work presented here demonstrates an application of computer-vision and pattern-recognition techniques to classify scatter patterns formed by Listeria colonies. Bacterial colonies are analyzed with a laser scatterometer. Features of circular scatter patterns formed by bacterial colonies illuminated by laser light are characterized using Zernike moment invariants. Principal component analysis and hierarchical clustering are performed on the results of feature extraction. Classification using linear discriminant analysis, partial least squares, and neural networks is capable of separating different strains of Listeria with a low error rate. The demonstrated system is also able to determine automatically the pathogenicity of bacteria on the basis of colony scatter patterns. We conclude that the obtained results are encouraging, and strongly suggest the feasibility of image-based biodetection systems.

Feature extraction from light-scatter patterns of Listeria colonies for identification and classification

Foodborne pathogens pose serious public health risks. Rapid, accurate technologies to detect a low number of target cells (1 cell/25-325 g sample) and microbial toxins are in demand in order to assess product safety in hours to up to 1 day. Varied pathogen loads and the complexity of food present a major challenge. Current culture methods, while accurate, are lengthy. New methods, using brief culturing and detection kits (antibody based, nucleic acid amplification or nano/biosensors) or a culture-independent approach coupled with nucleic acid amplification, traditionally used for viruses/parasites, can be used to obtain results in hours. A strategic approach involving two-step, rapid, high-throughput screening to rule out negatives followed by a confirmatory test could accomplish product testing in 1 h to 1 day.

One day to one hour: how quickly can foodborne pathogens be detected?

Crossing the Intestinal Barrier via Listeria Adhesion Protein and Internalin A.

Complete Inhibition of Low Levels of Listeria monocytogenes on Refrigerated Chicken Meat with Pediocin AcH Bound to Heat-Killed Pediococcus acidilactici Cells ‡.

Arun K. Bhunia

Papers

Feature extraction from light-scatter patterns of Listeria colonies for identification and classification

One day to one hour: how quickly can foodborne pathogens be detected?

Crossing the Intestinal Barrier via Listeria Adhesion Protein and Internalin A.

Complete Inhibition of Low Levels of Listeria monocytogenes on Refrigerated Chicken Meat with Pediocin AcH Bound to Heat-Killed Pediococcus acidilactici Cells ‡.

Cell-based biosensor for rapid screening of pathogens and toxins