Evidence map›Paper›PMID 32328403›Full record

Review3 Biotech2020

Microbial and plant-assisted heavy metal remediation in aquatic ecosystems: a comprehensive review.

Shyamalina Haldar, Abhrajyoti Ghosh

Abstract readReview
In one paragraph

Review in 3 Biotech, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

2 authors.

Shyamalina Haldar1Department of Biochemistry, Asutosh College, University of Calcutta, Kolkata, 700026 India.
Abhrajyoti Ghosh2Department of Biochemistry, Centenary Campus, Bose Institute, P-1/12 C.I.T. Scheme VII-M, Kolkata, 700054 India.ORCID 0000-0002-2469-3740

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heavy metal (HM) pollution in aquatic ecosystems has an adverse effect on both aquatic life forms as well as terrestrial living beings, including humans. Since HMs are recalcitrant, they accumulate in the environment and are subsequently biomagnified through the food chain. Conventional physical and chemical methods used to remove the HMs from aquatic habitats are usually expensive, slow, non-environment friendly, and mostly inefficient. On the contrary, phytoremediation and microbe-assisted remediation technologies have attracted immense attention in recent years and offer a better solution to the problem. These newly emerged remediation technologies are eco-friendly, efficient and cost-effective. Both phytoremediation and microbe-assisted remediation technologies adopt different mechanisms for HM bioremediation in aquatic ecosystems. Recent advancement of molecular tools has contributed significantly to better understand the mechanisms of metal adsorption, translocation, sequestration, and tolerance in plants and microbes. Albeit immense possibilities to use such bioremediation as a successful environmental clean-up technology, it is yet to be successfully implemented in the field conditions. This review article comprehensively discusses HM accumulation in Indian aquatic environments. Furthermore, it describes the effect of HMs accumulation in the aquatic environment and the role of phytoremediation as well as microbe-assisted remediation in mitigation of the HM toxicity. Finally, the review concludes with a note on the challenges, opportunities and future directions for bioremediation in the aquatic ecosystems.

Indexed as

Aquatic ecosystemHeavy metalsHuman healthPhytoremediationWater-treatment plants

Identifiers

PMID32328403
PMCPMC7171024

What OpenQuestion holds

Textmetadata
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.