Evidence map›Paper›PMID 33708764›Full record

ReviewFrontiers in bioengineering and biotechnology2021

Whole-Cell Microbial Bioreporter for Soil Contaminants Detection.

Ni Zeng, Yichao Wu, Wenli Chen, Qiaoyun Huang, Peng Cai

Abstract readReview
In one paragraph

Review in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Engineering Microbial-Electrochemical Interfaces for Sustainable Water Purification.Water environment research : a research publication of the Water Environment Federation · 2026
    Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Review
  10. Golden Gate Assembly of Aerobic and Anaerobic Microbial Bioreporters.Applied and environmental microbiology · 2022
    Article
  11. Article
  12. Article
  13. Review
  14. Design of Pb(II)-SpecificFrontiers in microbiology · 2022
    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

5 authors.

Ni ZengState Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
Yichao WuState Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
Wenli ChenState Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
Qiaoyun HuangState Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
Peng CaiState Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Anthropogenic activities have released various contaminants into soil that pose a serious threat to the ecosystem and human well-being. Compared to conventional analytical methodologies, microbial cell-based bioreporters are offering a flexible, rapid, and cost-effective strategy to assess the environmental risks. This review aims to summarize the recent progress in the application of bioreporters in soil contamination detection and provide insight into the challenges and current strategies. The biosensing principles and genetic circuit engineering are introduced. Developments of bioreporters to detect and quantify heavy metal and organic contaminants in soil are reviewed. Moreover, future opportunities of whole-cell bioreporters for soil contamination monitoring are discussed.

Indexed as

biosensorenvironmental monitoringheavy metalmicrobial bioreporterorganic contaminantssoil contamination

Identifiers

PMID33708764
PMCPMC7940511

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.