@article{abdou_mohamed_diagnosing_2021,
	title = {Diagnosing {Antibiotic} {Resistance} {Using} {Nucleic} {Acid} {Enzymes} and {Gold} {Nanoparticles}},
	volume = {15},
	issn = {1936-0851},
	url = {https://doi.org/10.1021/acsnano.0c09902},
	doi = {10.1021/acsnano.0c09902},
	abstract = {The rapid and accurate detection of antimicrobial resistance is critical to limiting the spread of infections and delivering effective treatments. Here, we developed a rapid, sensitive, and simple colorimetric nanodiagnostic platform to identify disease-causing pathogens and their associated antibiotic resistance genes within 2 h. The platform can detect bacteria from different biological samples (i.e., blood, wound swabs) with or without culturing. We validated the multicomponent nucleic acid enzyme–gold nanoparticle (MNAzyme-GNP) platform by screening patients with central line associated bloodstream infections and achieved a clinical sensitivity and specificity of 86\% and 100\%, respectively. We detected antibiotic resistance in methicillin-resistant Staphylococcus aureus (MRSA) in patient swabs with 90\% clinical sensitivity and 95\% clinical specificity. Finally, we identified mecA resistance genes in uncultured nasal, groin, axilla, and wound swabs from patients with 90\% clinical sensitivity and 95\% clinical specificity. The simplicity and versatility for detecting bacteria and antibiotic resistance markers make our platform attractive for the broad screening of microbial pathogens.},
	number = {6},
	urldate = {2021-11-06},
	journal = {ACS Nano},
	author = {Abdou Mohamed, Mohamed A. and Kozlowski, Hannah N. and Kim, Jisung and Zagorovsky, Kyryl and Kantor, Melinda and Feld, Jordan J. and Mubareka, Samira and Mazzulli, Tony and Chan, Warren C. W.},
	month = jun,
	year = {2021},
	note = {Publisher: American Chemical Society},
	pages = {9379--9390},
	file = {Full Text PDF:files/1773/Abdou Mohamed et al. - 2021 - Diagnosing Antibiotic Resistance Using Nucleic Aci.pdf:application/pdf;ACS Full Text Snapshot:files/1775/acsnano.html:text/html},
	url_Paper = {https://inbs.med.utoronto.ca/wp-content/uploads/2022/01/ABDOUM1.pdf}
}

{"_id":"dZjteAAzBhp6FMxD5","bibbaseid":"abdoumohamed-kozlowski-kim-zagorovsky-kantor-feld-mubareka-mazzulli-etal-diagnosingantibioticresistanceusingnucleicacidenzymesandgoldnanoparticles-2021","author_short":["Abdou Mohamed, M. A.","Kozlowski, H. N.","Kim, J.","Zagorovsky, K.","Kantor, M.","Feld, J. J.","Mubareka, S.","Mazzulli, T.","Chan, W. C. W."],"bibdata":{"bibtype":"article","type":"article","title":"Diagnosing Antibiotic Resistance Using Nucleic Acid Enzymes and Gold Nanoparticles","volume":"15","issn":"1936-0851","url":"https://doi.org/10.1021/acsnano.0c09902","doi":"10.1021/acsnano.0c09902","abstract":"The rapid and accurate detection of antimicrobial resistance is critical to limiting the spread of infections and delivering effective treatments. Here, we developed a rapid, sensitive, and simple colorimetric nanodiagnostic platform to identify disease-causing pathogens and their associated antibiotic resistance genes within 2 h. The platform can detect bacteria from different biological samples (i.e., blood, wound swabs) with or without culturing. We validated the multicomponent nucleic acid enzyme–gold nanoparticle (MNAzyme-GNP) platform by screening patients with central line associated bloodstream infections and achieved a clinical sensitivity and specificity of 86% and 100%, respectively. We detected antibiotic resistance in methicillin-resistant Staphylococcus aureus (MRSA) in patient swabs with 90% clinical sensitivity and 95% clinical specificity. Finally, we identified mecA resistance genes in uncultured nasal, groin, axilla, and wound swabs from patients with 90% clinical sensitivity and 95% clinical specificity. The simplicity and versatility for detecting bacteria and antibiotic resistance markers make our platform attractive for the broad screening of microbial pathogens.","number":"6","urldate":"2021-11-06","journal":"ACS Nano","author":[{"propositions":[],"lastnames":["Abdou","Mohamed"],"firstnames":["Mohamed","A."],"suffixes":[]},{"propositions":[],"lastnames":["Kozlowski"],"firstnames":["Hannah","N."],"suffixes":[]},{"propositions":[],"lastnames":["Kim"],"firstnames":["Jisung"],"suffixes":[]},{"propositions":[],"lastnames":["Zagorovsky"],"firstnames":["Kyryl"],"suffixes":[]},{"propositions":[],"lastnames":["Kantor"],"firstnames":["Melinda"],"suffixes":[]},{"propositions":[],"lastnames":["Feld"],"firstnames":["Jordan","J."],"suffixes":[]},{"propositions":[],"lastnames":["Mubareka"],"firstnames":["Samira"],"suffixes":[]},{"propositions":[],"lastnames":["Mazzulli"],"firstnames":["Tony"],"suffixes":[]},{"propositions":[],"lastnames":["Chan"],"firstnames":["Warren","C.","W."],"suffixes":[]}],"month":"June","year":"2021","note":"Publisher: American Chemical Society","pages":"9379–9390","file":"Full Text PDF:files/1773/Abdou Mohamed et al. - 2021 - Diagnosing Antibiotic Resistance Using Nucleic Aci.pdf:application/pdf;ACS Full Text Snapshot:files/1775/acsnano.html:text/html","url_paper":"https://inbs.med.utoronto.ca/wp-content/uploads/2022/01/ABDOUM1.pdf","bibtex":"@article{abdou_mohamed_diagnosing_2021,\n\ttitle = {Diagnosing {Antibiotic} {Resistance} {Using} {Nucleic} {Acid} {Enzymes} and {Gold} {Nanoparticles}},\n\tvolume = {15},\n\tissn = {1936-0851},\n\turl = {https://doi.org/10.1021/acsnano.0c09902},\n\tdoi = {10.1021/acsnano.0c09902},\n\tabstract = {The rapid and accurate detection of antimicrobial resistance is critical to limiting the spread of infections and delivering effective treatments. Here, we developed a rapid, sensitive, and simple colorimetric nanodiagnostic platform to identify disease-causing pathogens and their associated antibiotic resistance genes within 2 h. The platform can detect bacteria from different biological samples (i.e., blood, wound swabs) with or without culturing. We validated the multicomponent nucleic acid enzyme–gold nanoparticle (MNAzyme-GNP) platform by screening patients with central line associated bloodstream infections and achieved a clinical sensitivity and specificity of 86\\% and 100\\%, respectively. We detected antibiotic resistance in methicillin-resistant Staphylococcus aureus (MRSA) in patient swabs with 90\\% clinical sensitivity and 95\\% clinical specificity. Finally, we identified mecA resistance genes in uncultured nasal, groin, axilla, and wound swabs from patients with 90\\% clinical sensitivity and 95\\% clinical specificity. The simplicity and versatility for detecting bacteria and antibiotic resistance markers make our platform attractive for the broad screening of microbial pathogens.},\n\tnumber = {6},\n\turldate = {2021-11-06},\n\tjournal = {ACS Nano},\n\tauthor = {Abdou Mohamed, Mohamed A. and Kozlowski, Hannah N. and Kim, Jisung and Zagorovsky, Kyryl and Kantor, Melinda and Feld, Jordan J. and Mubareka, Samira and Mazzulli, Tony and Chan, Warren C. W.},\n\tmonth = jun,\n\tyear = {2021},\n\tnote = {Publisher: American Chemical Society},\n\tpages = {9379--9390},\n\tfile = {Full Text PDF:files/1773/Abdou Mohamed et al. - 2021 - Diagnosing Antibiotic Resistance Using Nucleic Aci.pdf:application/pdf;ACS Full Text Snapshot:files/1775/acsnano.html:text/html},\n\turl_Paper = {https://inbs.med.utoronto.ca/wp-content/uploads/2022/01/ABDOUM1.pdf}\n}\n\n","author_short":["Abdou Mohamed, M. A.","Kozlowski, H. N.","Kim, J.","Zagorovsky, K.","Kantor, M.","Feld, J. J.","Mubareka, S.","Mazzulli, T.","Chan, W. C. 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