Evidence map›Paper›PMID 29234023›Full record

ArticleScientific reports2017

Discriminating single-bacterial shape using low-aspect-ratio pores.

Makusu Tsutsui, Takeshi Yoshida, Kazumichi Yokota, Hirotoshi Yasaki, Takao Yasui, Akihide Arima, Wataru Tonomura, Kazuki Nagashima, Takeshi Yanagida, Noritada Kaji and 4 more

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Recent advances in single-particle analysis with nanopore technology.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2025
    Review
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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

14 authors.

Makusu TsutsuiThe Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, 567-0047, Japan. tsutsui@sanken.osaka-u.ac.jp.
Takeshi YoshidaThe Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, 567-0047, Japan.
Kazumichi YokotaThe Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, 567-0047, Japan.
Hirotoshi YasakiDepartment of Applied Chemistry, Graduate School of Engineering and ImPACT Research Center for Advanced Nanobiodevices, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan.
Takao YasuiDepartment of Applied Chemistry, Graduate School of Engineering and ImPACT Research Center for Advanced Nanobiodevices, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan.
Akihide ArimaThe Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, 567-0047, Japan.
Wataru TonomuraThe Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, 567-0047, Japan.
Kazuki NagashimaInstitute for Materials Chemistry and Engineering, Kyushu University, 6-1 Kasuga-Koen, Kasuga, Fukuoka, 816-8580, Japan.
Takeshi YanagidaInstitute for Materials Chemistry and Engineering, Kyushu University, 6-1 Kasuga-Koen, Kasuga, Fukuoka, 816-8580, Japan.
Noritada KajiDepartment of Applied Chemistry, Graduate School of Engineering and ImPACT Research Center for Advanced Nanobiodevices, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan.
Masateru TaniguchiThe Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, 567-0047, Japan.
Takashi WashioThe Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, 567-0047, Japan. washio@ar.sanken.osaka-u.ac.jp.
Yoshinobu BabaDepartment of Applied Chemistry, Graduate School of Engineering and ImPACT Research Center for Advanced Nanobiodevices, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan.
Tomoji KawaiThe Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, 567-0047, Japan. kawai@sanken.osaka-u.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conventional concepts of resistive pulse analysis is to discriminate particles in liquid by the difference in their size through comparing the amount of ionic current blockage. In sharp contrast, we herein report a proof-of-concept demonstration of the shape sensing capability of solid-state pore sensors by leveraging the synergy between nanopore technology and machine learning. We found ionic current spikes of similar patterns for two bacteria reflecting the closely resembled morphology and size in an ultra-low thickness-to-diameter aspect-ratio pore. We examined the feasibility of a machine learning strategy to pattern-analyse the sub-nanoampere corrugations in each ionic current waveform and identify characteristic electrical signatures signifying nanoscopic differences in the microbial shape, thereby demonstrating discrimination of single-bacterial cells with accuracy up to 90%. This data-analytics-driven microporescopy capability opens new applications of resistive pulse analyses for screening viruses and bacteria by their unique morphologies at a single-particle level.

Indexed as

NanoporesProof of Concept StudyBacteriaElectrochemical TechniquesFeasibility StudiesIonsMachine LearningNanotechnologyIons

Identifiers

PMID29234023
PMCPMC5727063

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.