PMID- 28837780
OWN - NLM
STAT- MEDLINE
DCOM- 20181120
LR  - 20191210
IS  - 2472-6311 (Electronic)
IS  - 2472-6303 (Print)
IS  - 2472-6303 (Linking)
VI  - 22
IP  - 6
DP  - 2017 Dec
TI  - Development of MAST: A Microscopy-Based Antimicrobial Susceptibility Testing 
      Platform.
PG  - 662-674
LID - 10.1177/2472630317727721 [doi]
AB  - Antibiotic resistance is compromising our ability to treat bacterial infections. 
      Clinical microbiology laboratories guide appropriate treatment through 
      antimicrobial susceptibility testing (AST) of patient bacterial isolates. 
      However, increasingly, pathogens are developing resistance to a broad range of 
      antimicrobials, requiring AST of alternative agents for which no commercially 
      available testing methods are available. Therefore, there exists a significant 
      AST testing gap in which current methodologies cannot adequately address the need 
      for rapid results in the face of unpredictable susceptibility profiles. To 
      address this gap, we developed a multicomponent, microscopy-based AST (MAST) 
      platform capable of AST determinations after only a 2 h incubation. MAST consists 
      of a solid-phase microwell growth surface in a 384-well plate format, inkjet 
      printing-based application of both antimicrobials and bacteria at any desired 
      concentrations, automated microscopic imaging of bacterial replication, and a 
      deep learning approach for automated image classification and determination of 
      antimicrobial minimal inhibitory concentrations (MICs). In evaluating a 
      susceptible strain set, 95.8% were within +/-1 and 99.4% were within +/-2, twofold 
      dilutions, respectively, of reference broth microdilution MIC values. Most 
      (98.3%) of the results were in categorical agreement. We conclude that MAST 
      offers promise for rapid, accurate, and flexible AST to help address the 
      antimicrobial testing gap.
FAU - Smith, Kenneth P
AU  - Smith KP
AD  - 1 Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
FAU - Richmond, David L
AU  - Richmond DL
AD  - 2 Image and Data Analysis Core, Harvard Medical School, Boston, MA, USA.
FAU - Brennan-Krohn, Thea
AU  - Brennan-Krohn T
AD  - 1 Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
AD  - 3 Division of Infectious Diseases, Boston Children's Hospital, Boston, MA, USA.
FAU - Elliott, Hunter L
AU  - Elliott HL
AD  - 2 Image and Data Analysis Core, Harvard Medical School, Boston, MA, USA.
FAU - Kirby, James E
AU  - Kirby JE
AD  - 1 Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
LA  - eng
GR  - T32 AI007061/AI/NIAID NIH HHS/United States
GR  - T32 HD055148/HD/NICHD NIH HHS/United States
GR  - UL1 TR001102/TR/NCATS NIH HHS/United States
PT  - Evaluation Study
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20170824
PL  - United States
TA  - SLAS Technol
JT  - SLAS technology
JID - 101697564
RN  - 0 (Anti-Infective Agents)
SB  - IM
MH  - Anti-Infective Agents/*pharmacology
MH  - Bacteria/*drug effects
MH  - Humans
MH  - Microbial Sensitivity Tests/*methods
MH  - Microscopy/*methods
MH  - Time Factors
PMC - PMC5744253
MID - NIHMS926022
OTO - NOTNLM
OT  - antimicrobials
OT  - inkjet printing
OT  - machine learning
OT  - susceptibility testing
EDAT- 2017/08/25 06:00
MHDA- 2018/11/21 06:00
PMCR- 2018/12/01
CRDT- 2017/08/25 06:00
PHST- 2017/08/25 06:00 [pubmed]
PHST- 2018/11/21 06:00 [medline]
PHST- 2017/08/25 06:00 [entrez]
PHST- 2018/12/01 00:00 [pmc-release]
AID - 10.1177/2472630317727721 [doi]
PST - ppublish
SO  - SLAS Technol. 2017 Dec;22(6):662-674. doi: 10.1177/2472630317727721. Epub 2017 
      Aug 24.