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: 10426291
• 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 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10426291
• 关键词: Address; Affect; Agreement; 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; Morphology; Motion; Optics; Organism; Pathogen detection; Patients; Performance; Persons; 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; bioelectronics; carbapenem-resistant Enterobacteriaceae; clinical diagnosis; clinically relevant; combat; density; design; health care settings; image processing; innovation; instrument; light scattering; machine learning algorithm; machine learning model; 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 抗生素抗性已成为重大的公共卫生威胁。为了解决问题，病原体快速 需要识别（ID）和抗菌敏感性测试（AST）技术以提供及时的诊断 - 抗性感染和在初级医疗保健环境下的准确抗生素治疗的SIS，包括 - 医院和护理点（POC）。本项目旨在开发一个护理点（pocasttm） 基于大图像 - 体积显微镜技术的技术，该技术可以直接检测个体 无文化或病原体隔离的临床样本中的细菌细胞，以及一个机器学习模型 可以快速确定病原体和敏感性。为了建立技术，该项目将重点放在 尿路感染（UTI）。 UTI每年影响数百万的人，以及通常引起的病原体 UTI是构成抗菌耐药性最高威胁的生物，包括碳青霉烯 - 抗性肠杆菌科（CRE）和扩展的频谱β-内酰胺酶（ESBL）产生的肠杆菌 艾科。 该项目将重点介绍：1）为开发大图像 - 体积显微镜和机器学习模型 直接在患者尿液样本中直接跟踪单细菌细胞的多表型特征， 对UTI进行快速自动病原体ID和AST； 2）建筑原型仪器，3）验证 使用大规模临床样品的UTI仪器。成功开发和验证技术 - NOLOGY将在患者访问的同一天启用精确的抗生素处方。 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 生物电子和生物传感器，ASU），微生物学和感染性疾病（免疫设计中心 治疗，疫苗和病毒疗法，ASU），生物医学仪器的发育和生产（生物传感 Instrument Inc.）和临床测试（临床微生物学实验室，梅奥诊所）。 呢

项目成果

Direct Antimicrobial Susceptibility Testing on Clinical Urine Samples by Optical Tracking of Single Cell Division Events.

• DOI: 10.1002/smll.202004148
• 发表时间: 2020-12
• 期刊: Small (Weinheim an der Bergstrasse, Germany)
• 作者: Zhang F;Jiang J;McBride M;Yang Y;Mo M;Iriya R;Peterman J;Jing W;Grys T;Haydel SE;Tao N;Wang S
• 通讯作者: Wang S

Rapid Antimicrobial Susceptibility Testing on Clinical Urine Samples by Video-Based Object Scattering Intensity Detection.

• DOI: 10.1021/acs.analchem.1c00019
• 发表时间: 2021-05-11
• 期刊: Analytical chemistry
• 影响因子: 7.4
• 作者: Zhang F;Jiang J;McBride M;Zhou X;Yang Y;Mo M;Peterman J;Grys T;Haydel SE;Tao N;Wang S
• 通讯作者: Wang S

Rapid Detection of Urinary Tract Infection in 10 min by Tracking Multiple Phenotypic Features in a 30 s Large-Volume Scattering Video of Urine Microscopy.

• DOI: 10.1021/acssensors.2c00788
• 发表时间: 2022-08-26
• 期刊: ACS SENSORS
• 影响因子: 8.9
• 作者: Zhang, Fenni;Mo, Manni;Jiang, Jiapei;Zhou, Xinyu;McBride, Michelle;Yang, Yunze;Reilly, Kenta S.;Grys, Thomas E.;Haydel, Shelley E.;Tao, Nongjian;Wang, Shaopeng
• 通讯作者: Wang, Shaopeng