Evidence map›Paper›PMID 40614180›Full record

ArticleScience advances2025

Droplet microfluidics-based detection of rare antibiotic-resistant subpopulations in

Sagar N Agnihotri, Nikos Fatsis-Kavalopoulos, Jonas Windhager, Maria Tenje, Dan I Andersson

Abstract read
In one paragraph

Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Review
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

5 authors.

Sagar N AgnihotriDepartment of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.ORCID 0000-0003-0943-6751
Nikos Fatsis-KavalopoulosDepartment of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-5081-0138
Jonas WindhagerDepartment of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-2111-5291
Maria TenjeDepartment of Materials Science and Engineering, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-1264-1337
Dan I AnderssonDepartment of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.ORCID 0000-0001-6640-2174

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Population heterogeneity in bacterial phenotypes, such as antibiotic resistance, is increasingly recognized as a medical concern. Heteroresistance occurs when a predominantly susceptible bacterial population harbors a rare resistant subpopulation. During antibiotic exposure, these resistant bacteria can be selected and lead to treatment failure. Standard antibiotic susceptibility testing methods often fail to reliably detect these subpopulations due to their low frequency, highlighting the need for improved diagnostic approaches. Here, we present a droplet microfluidics method where bacteria are encapsulated in droplets containing growth medium and antibiotics. The growth of rare resistant cells is detected by observing droplet shrinkage under microscopy. We validated this method for three clinically important antibiotics in

Indexed as

Anti-Bacterial AgentsBacteremiaDrug Resistance, BacterialEscherichia coliEscherichia coli InfectionsMicrofluidic Analytical TechniquesMicrofluidicsHumansMicrobial Sensitivity TestsAnti-Bacterial Agents

Identifiers

PMID40614180
PMCPMC12227044

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.