Evidence map›Paper›PMID 30925806›Full record

ReviewMicromachines2019

Electrochemical Biosensors for Detection of Foodborne Pathogens.

Zhenguo Zhang, Jun Zhou, Xin Du

Abstract readReview
In one paragraph

Review in Micromachines, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Review
  6. Strategic Detection ofBiosensors · 2025
    Review
  7. Review
  8. Recent developments in waterborne pathogen detection technologies.Environmental monitoring and assessment · 2025
    Review
  9. Review
  10. Review
  11. Review
  12. Article
  13. Review
  14. Article
  15. Review
  16. Review
  17. Article
  18. Article
  19. Review
  20. 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

3 authors.

Zhenguo ZhangCollege of Life Sciences, Key Laboratory of Food Nutrition and Safety, Key Laboratory of Animal Resistance Biology of Shandong Province, Shandong Normal University, Jinan 250014, China.
Jun ZhouCollege of Life Sciences, Key Laboratory of Food Nutrition and Safety, Key Laboratory of Animal Resistance Biology of Shandong Province, Shandong Normal University, Jinan 250014, China.
Xin DuCollege of Life Sciences, Key Laboratory of Food Nutrition and Safety, Key Laboratory of Animal Resistance Biology of Shandong Province, Shandong Normal University, Jinan 250014, China. xdu@sdnu.edu.cn.ORCID 0000-0003-1494-4910

Funding

National Natural Science Foundation of China 31801200Postdoctoral Science Foundation of China 2017M612334Shandong Provincial Natural Science Foundation ZR2017BC052
6 · The paper itself

Abstract

Foodborne safety has become a global public health problem in both developed and developing countries. The rapid and precise monitoring and detection of foodborne pathogens has generated a strong interest by researchers in order to control and prevent human foodborne infections. Traditional methods for the detection of foodborne pathogens are often time-consuming, laborious, expensive, and unable to satisfy the demands of rapid food testing. Owing to the advantages of simplicity, real-time analysis, high sensitivity, miniaturization, rapid detection time, and low cost, electrochemical biosensing technology is more and more widely used in determination of foodborne pathogens. Here, we summarize recent developments in electrochemical biosensing technologies used to detect common foodborne pathogens. Additionally, we discuss research challenges and future prospects for this field of study.

Indexed as

detectionelectrochemical biosensorfoodpathogen

Identifiers

PMID30925806
PMCPMC6523478

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.