Evidence map›Paper›PMID 41629481›Full record

ArticleNature nanotechnology2026

Ultra-rapid nanoplasmonic colorimetry in microfluidics for antimicrobial susceptibility testing directly from specimens.

Mahsa Jalali, Tamer AbdElFatah, Carolina Del Real Mata, Imman I Hosseini, Sripadh Guptha Yedire, Geoffrey A McKay, Rachel Corsini, Roozbeh Siavash Moakhar, Hamed Shieh, Grace Reszetnik and 4 more

Abstract read
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In one paragraph

Article in Nature nanotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Mahsa Jalali *Department of Bioengineering, McGill University, Montreal, Quebec, Canada.ORCID http://orcid.org/0000-0003-2353-3910
Tamer AbdElFatah *Department of Bioengineering, McGill University, Montreal, Quebec, Canada.ORCID http://orcid.org/0000-0003-4141-2442
Carolina Del Real MataDepartment of Bioengineering, McGill University, Montreal, Quebec, Canada.ORCID http://orcid.org/0000-0003-2907-5703
Imman I HosseiniDepartment of Bioengineering, McGill University, Montreal, Quebec, Canada.ORCID http://orcid.org/0000-0003-0852-678X
Sripadh Guptha YedireDepartment of Bioengineering, McGill University, Montreal, Quebec, Canada.ORCID http://orcid.org/0000-0002-7751-9295
Geoffrey A McKayResearch Institute of the McGill University Health Centre, Montreal, Quebec, Canada.
Rachel CorsiniResearch Institute of the McGill University Health Centre, Montreal, Quebec, Canada.
Roozbeh Siavash MoakharDepartment of Bioengineering, McGill University, Montreal, Quebec, Canada.ORCID http://orcid.org/0000-0001-8334-085X
Hamed ShiehDepartment of Bioengineering, McGill University, Montreal, Quebec, Canada.
Grace ReszetnikResearch Institute of the McGill University Health Centre, Montreal, Quebec, Canada.
Seyed Vahid HamidiDepartment of Bioengineering, McGill University, Montreal, Quebec, Canada.ORCID http://orcid.org/0000-0003-0119-6175
Cedric P YansouniResearch Institute of the McGill University Health Centre, Montreal, Quebec, Canada.ORCID http://orcid.org/0000-0002-4765-3469
Dao NguyenResearch Institute of the McGill University Health Centre, Montreal, Quebec, Canada.ORCID http://orcid.org/0000-0003-4012-5441
Sara MahshidDepartment of Bioengineering, McGill University, Montreal, Quebec, Canada. sara.mahshid@mcgill.ca.ORCID http://orcid.org/0000-0003-4203-819X

Funding

Canada Foundation for Innovation (Fondation canadienne pour l'innovation) G248924Gouvernement du Canada | Instituts de Recherche en Santé du Canada | CIHR Skin Research Training Centre (Skin Research Training Centre) 261193Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada) 258928Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada) G247765
6 · The paper itself

Abstract

Antimicrobial susceptibility testing (AST) technologies that rapidly identify pathogenic bacteria and their resistance phenotypes are critical in addressing the antimicrobial resistance crisis, enabling timely and precise antibiotic treatment decisions. We present a modular automated platform based on nanoplasmonic colorimetry in microfluidics for parallel bacterial identification and phenotypic profiling of AST (QolorPhAST), achieving an eightfold enhancement in detection rapidity. QolorPhAST reduces drug susceptibility profiling times in direct specimens from days to minutes, bypassing overnight cultures and pathogen isolation typically required in standard clinical AST workflows. The approach was validated with a broad range of microbial pathogens, spanning 10 bacterial species and 34 strains across various antibiotic concentrations to identify pathogens and antibiotic minimal inhibitory concentrations in a multiplexed fashion. In a proof-of-concept clinical study, QolorPhAST was tested with a cohort of blinded patient samples suspected of urinary tract infections, achieving 100% accuracy in species identification, an average categorical agreement of 91.81% and an average essential agreement of 86.4%, with a turnaround time of 36 min from specimen introduction to result. The study suggests that QolorPhAST, with its ease of use and cost-effectiveness, can be a transformative solution to address the antimicrobial resistance burden.

Indexed as

Anti-Bacterial AgentsBacteriaColorimetryMicrofluidic Analytical TechniquesMicrofluidicsNanotechnologyHumansMicrobial Sensitivity TestsUrinary Tract InfectionsAnti-Bacterial Agents

Identifiers

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.