Evidence map›Paper›PMID 28850804›Full record

ArticleSLAS technology2017

Emerging Microtechnologies and Automated Systems for Rapid Bacterial Identification and Antibiotic Susceptibility Testing.

Yiyan Li, Xing Yang, Weian Zhao

Abstract read
In one paragraph

Article in SLAS technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 1 pooled it
–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

53 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Treatment and Management ofInfection and drug resistance · 2024
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  10. Quick Detection ofSensors (Basel, Switzerland) · 2023
    Article
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  17. Observational
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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

3 authors.

Yiyan Li1 Sue and Bill Gross Stem Cell Research Center, University of California-Irvine, Irvine, CA, USA.
Xing Yang8 bioMérieux, Shanghai, China.
Weian Zhao1 Sue and Bill Gross Stem Cell Research Center, University of California-Irvine, Irvine, CA, USA.

Funding

Integrated Comprehensive Droplet Digital Detection (IC 3D) system for rapid detection of bacteria andantimicrobial resistanceR01AI117061 · NIAID · UNIVERSITY OF CALIFORNIA-IRVINE · PI ZHAO, WEIAN · 2015 to 2019
$5.1M
NIAID NIH HHS R01 AI117061
6 · The paper itself

Abstract

Rapid bacterial identification (ID) and antibiotic susceptibility testing (AST) are in great demand due to the rise of drug-resistant bacteria. Conventional culture-based AST methods suffer from a long turnaround time. By necessity, physicians often have to treat patients empirically with antibiotics, which has led to an inappropriate use of antibiotics, an elevated mortality rate and healthcare costs, and antibiotic resistance. Recent advances in miniaturization and automation provide promising solutions for rapid bacterial ID/AST profiling, which will potentially make a significant impact in the clinical management of infectious diseases and antibiotic stewardship in the coming years. In this review, we summarize and analyze representative emerging micro- and nanotechnologies, as well as automated systems for bacterial ID/AST, including both phenotypic (e.g., microfluidic-based bacterial culture, and digital imaging of single cells) and molecular (e.g., multiplex PCR, hybridization probes, nanoparticles, synthetic biology tools, mass spectrometry, and sequencing technologies) methods. We also discuss representative point-of-care (POC) systems that integrate sample processing, fluid handling, and detection for rapid bacterial ID/AST. Finally, we highlight major remaining challenges and discuss potential future endeavors toward improving clinical outcomes with rapid bacterial ID/AST technologies.

Indexed as

Anti-Bacterial AgentsBacteriaBacteriological TechniquesHumansMicrofluidicsTime FactorsAnti-Bacterial Agentsantibiotic susceptibility testingautomationbloodstream infectionlab on a chipsepsis

Identifiers

PMID28850804
PMCPMC5835395

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.