Point-of-care antimicrobial susceptibility testing based on simultaneous tracking of multi-phenotypic features of single bacterial cells

基于同时跟踪单个细菌细胞的多表型特征的护理点抗菌药物敏感性测试

• 批准号: 10188407
• 负责人: SHAOPENG WANG
• 金额: $ 107.97万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-25 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10188407
• 关键词: Address; Affect; Agreement; Algorithms; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Antimicrobial Resistance; Antimicrobial susceptibility; Arizona; Bacterial Infections; Biosensing Techniques; Biosensor; Blood Cells; Categories; Cells; Clinic; Clinical; Clinical Microbiology; Communicable Diseases; Computer software; Crystallization; Data; Data Analyses; Detection; Development; Device or Instrument Development; Diagnosis; Enterobacteriaceae; Extended-spectrum β-lactamase; Face; Growth; Hospitals; Hour; Image; Imaging Techniques; Immunotherapy; Individual; Infection; Laboratories; Machine Learning; Measures; Methods; Microbiology; Microscope; Microscopy; Modeling; Morphology; Motion; Optics; Organism; Pathogen detection; Patients; Performance; Phenotype; Pilot Projects; Point of Care Technology; Predisposition; Primary Health Care; Production; Protocols documentation; Public Health; Research Personnel; Resistance; Risk; Sampling; Specificity; Speed; System; Techniques; Technology; Testing; Time; Training; Universities; Urinary tract infection; Urine; Vaccines; Validation; Virotherapy; Visit; Work; antimicrobial; automated algorithm; bacterial resistance; base; carbapenem-resistant Enterobacteriaceae; clinical Diagnosis; clinically relevant; combat; density; design; health care settings; image processing; innovation; instrument; light scattering; machine learning algorithm; multidisciplinary; pathogen; point of care; point-of-care diagnosis; prototype; research clinical testing; technology development; technology validation

ABSTRACT Antibiotic resistance has become a significant public health threat. To combat the problem, a rapid pathogen identification (ID) and antimicrobial susceptibility testing (AST) technology is needed to provide timely diagno- sis of resistant infections and delivery of accurate antibiotic treatment at primary health-care settings, includ- ing hospitals and point-of-care (POC). The present project aims to develop a point-of-care AST (POCASTTM) technology based on a large-image-volume microscopy technique that enables direct detection of individual bacterial cells in clinical samples without culturing or pathogen isolation, and a machine-learning model that allows fast determination of pathogen and susceptibility. To establish the technology, the project will focus on urinary tract infections (UTIs). UTIs affect millions of people annually, and the pathogens that usually cause UTIs are the organisms that pose the highest threat of antimicrobial resistance, including carbapenem- resistant Enterobacteriaceae (CRE) and extended spectrum β-lactamase (ESBL)-producing Enterobacteri- aceae. This project will focus on: 1) developing the large-image-volume microscopy and machine learning model for simultaneous tracking of multi-phenotypic features of single bacterial cells directly in patient urine sample, and performing rapid automatic pathogen ID and AST for UTIs; 2) building prototype instrument, and 3) validating the instrument for UTIs using large scale clinical samples. Successful development and validation of the tech- nology will enable precise antibiotic prescription on the same day of patient visit. The project will be carried out by a multidisciplinary team with expertise in biosensors (Biodesign Center for Bioelectronics and Biosensors, ASU), microbiology and infectious diseases (Biodesign Center for Immuno- therapy, Vaccines and Virotherapy, ASU), biomedical instrument development and production (Biosensing Instrument Inc.), and clinical testing (Clinical Microbiology Laboratory, Mayo Clinic). !

抽象的 抗生素耐药性已成为重大的公共卫生威胁。为了解决这个问题，一种快速病原体 需要鉴定（ID）和抗菌药物敏感性测试（AST）技术来提供及时诊断 抗药性感染的问题以及在初级卫生保健机构提供准确的抗生素治疗，包括 医院和护理点 (POC)。本项目旨在开发即时护理 AST (POCASTTM) 基于大图像体积显微镜技术的技术，能够直接检测个体 无需培养或病原体分离的临床样本中的细菌细胞，以及一种机器学习模型 可以快速确定病原体和敏感性。为了建立该技术，该项目将重点关注 尿路感染（UTI）。尿路感染每年影响数百万人，通常引起尿路感染的病原体 尿路感染是对抗生素耐药性构成最大威胁的微生物，包括碳青霉烯类抗生素。 耐药肠杆菌科 (CRE) 和产超广谱 β-内酰胺酶 (ESBL) 肠杆菌 埃索科。 该项目将重点关注：1）开发大图像体积显微镜和机器学习模型 直接同时跟踪患者尿液样本中单个细菌细胞的多表型特征，以及 对尿路感染进行快速自动病原体 ID 和 AST； 2) 构建原型仪器，以及 3) 验证 使用大规模临床样本检测尿路感染的仪器。该技术的成功开发和验证 nology 可以在患者就诊的同一天提供精确的抗生素处方。 该项目将由具有生物传感器专业知识的多学科团队（生物设计中心）执行 生物电子学和生物传感器，ASU），微生物学和传染病（免疫生物设计中心） 疗法、疫苗和病毒疗法、ASU）、生物医学仪器开发和生产（生物传感 Instrument Inc.）和临床测试（临床微生物学实验室，梅奥诊所）。 ！