Evidence map›Paper›PMID 38753148›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2024

A Thermoplastic Microsystem to Perform Antibiotic Susceptibility Testing by Monitoring Oxygen Consumption.

Petra Jusková, André Kling, Steven Schmitt, Petra S Dittrich

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Metabolism-Based Biomarkers for Rapid Phenotypic Antibiotic Susceptibility Testing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  4. High-throughput chip-calorimeter using a BiMicrosystems & nanoengineering · 2025
    Article
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

4 authors.

Petra JuskováDepartment of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
André KlingDepartment of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
Steven SchmittMyria Biosciences AG, Basel, Switzerland.
Petra S DittrichDepartment of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland. petra.dittrich@bsse.ethz.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antibiotic susceptibility testing (AST) is a routine procedure in diagnostic laboratories to determine pathogen resistance profiles toward antibiotics. The need for fast and accurate resistance results is rapidly increasing with a global rise in pathogen antibiotic resistance over the past years. Microfluidic technologies can enable AST with lower volumes, lower cell numbers, and a reduction in the sample-to-result time compared to state-of-the-art systems. We present a protocol to perform AST on a miniaturized nanoliter chamber array platform. The chambers are filled with antibiotic compounds and oxygen-sensing nanoprobes that serve as a viability indicator. The growth of bacterial cells in the presence of different concentrations of antibiotics is monitored; living cells consume oxygen, which can be observed as an increase of a luminesce signal within the growth chambers. Here, we demonstrate the technique using a quality control Escherichia coli strain, ATCC 35218. The AST requires 20 μL of a diluted bacterial suspension (OD

Indexed as

Anti-Bacterial AgentsEscherichia coliMicrobial Sensitivity TestsLab-On-A-Chip DevicesMicrofluidic Analytical TechniquesOxygenOxygen ConsumptionAnti-Bacterial AgentsOxygenAntibiotic susceptibility testingMicrofluidicsThermoplastic microfluidic devices

Identifiers

PMID38753148

What OpenQuestion holds

Textmetadata
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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.