Evidence map›Paper›PMID 33564026›Full record

ArticleScientific reports2021

Rapid profiling of drug-resistant bacteria using DNA-binding dyes and a nanopore-based DNA sequencer.

Ayumu Ohno, Kazuo Umezawa, Satomi Asai, Kirill Kryukov, So Nakagawa, Hayato Miyachi, Tadashi Imanishi

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Detection of Live Shiga Toxin-ProducingInternational journal of molecular sciences · 2025
    Article
  3. 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

7 authors.

Ayumu OhnoDepartment of Molecular Life Science, Tokai University School of Medicine, Isehara, Kanagawa, 259-1193, Japan.
Kazuo UmezawaDepartment of Emergency and Critical Care Medicine, Tokai University School of Medicine, Isehara, Kanagawa, 259-1193, Japan.
Satomi AsaiDepartment of Laboratory Medicine, Tokai University School of Medicine, Isehara, Kanagawa, 259-1193, Japan.
Kirill KryukovDepartment of Molecular Life Science, Tokai University School of Medicine, Isehara, Kanagawa, 259-1193, Japan.
So NakagawaDepartment of Molecular Life Science, Tokai University School of Medicine, Isehara, Kanagawa, 259-1193, Japan.
Hayato MiyachiDepartment of Laboratory Medicine, Tokai University School of Medicine, Isehara, Kanagawa, 259-1193, Japan.
Tadashi ImanishiDepartment of Molecular Life Science, Tokai University School of Medicine, Isehara, Kanagawa, 259-1193, Japan. imanishi@tokai.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spread of drug-resistant bacteria is a serious problem worldwide. We thus designed a new sequence-based protocol that can quickly identify bacterial compositions of clinical samples and their drug-resistance profiles simultaneously. Here we utilized propidium monoazide (PMA) that prohibits DNA amplifications from dead bacteria, and subjected the original and antibiotics-treated samples to 16S rRNA metagenome sequencing. We tested our protocol on bacterial mixtures, and observed that sequencing reads derived from drug-resistant bacteria were significantly increased compared with those from drug-sensitive bacteria when samples were treated by antibiotics. Our protocol is scalable and will be useful for quickly profiling drug-resistant bacteria.

Indexed as

NanoporesSequence Analysis, DNABacteriaColoring AgentsDNA, BacterialDrug Resistance, BacterialMetagenomeRNA, BacterialRNA, Ribosomal, 16SColoring AgentsDNA, BacterialRNA, BacterialRNA, Ribosomal, 16S

Identifiers

PMID33564026
PMCPMC7873225

What OpenQuestion holds

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LicenceCC BY
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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.