Point-of-care antimicrobial susceptibility testing based on simultaneous tracking of multi-phenotypic features of single bacterial cells
Point-of-care antimicrobial susceptibility testing based on simultaneous tracking of multi-phenotypic features of single bacterial cells
批准号:
10188407
负责人:
SHAOPENG WANG
金额:
$107.97万
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-07-25 至 2023-06-30
关键词:
AddressAffectAgreementAlgorithmsAntibiotic ResistanceAntibiotic TherapyAntibioticsAntimicrobial ResistanceAntimicrobial susceptibilityArizonaBacterial InfectionsBiosensing TechniquesBiosensorBlood CellsCategoriesCellsClinicClinicalClinical MicrobiologyCommunicable DiseasesComputer softwareCrystallizationDataData AnalysesDetectionDevelopmentDevice or Instrument DevelopmentDiagnosisEnterobacteriaceaeExtended-spectrum β-lactamaseFaceGrowthHospitalsHourImageImaging TechniquesImmunotherapyIndividualInfectionLaboratoriesMachine LearningMeasuresMethodsMicrobiologyMicroscopeMicroscopyModelingMorphologyMotionOpticsOrganismPathogen detectionPatientsPerformancePhenotypePilot ProjectsPoint of Care TechnologyPredispositionPrimary Health CareProductionProtocols documentationPublic HealthResearch PersonnelResistanceRiskSamplingSpecificitySpeedSystemTechniquesTechnologyTestingTimeTrainingUniversitiesUrinary tract infectionUrineVaccinesValidationVirotherapyVisitWorkantimicrobialautomated algorithmbacterial resistancebasecarbapenem-resistant Enterobacteriaceaeclinical Diagnosisclinically relevantcombatdensitydesignhealth care settingsimage processinginnovationinstrumentlight scatteringmachine learning algorithmmultidisciplinarypathogenpoint of carepoint-of-care diagnosisprototyperesearch clinical testingtechnology developmenttechnology validation
中文摘要
点击翻译按钮获取中文摘要
英文摘要
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).
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